an appraisal into the potential application of big …

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AN APPRAISAL INTO THE POTENTIAL APPLICATION OF BIG DATA IN

THE CONSTRUCTION INDUSTRY

SITI AISYAH ISMAIL

Universiti Teknologi Malaysia

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NOTES: * If the thesis is CONFIDENTAL or RESTRICTED, please attach with the letter from

the organization with period and reasons for confidentiality or restriction.

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AN APPRAISAL INTO THE POTENTIAL APPLICATION OF BIG DATA IN

THE CONSTRUCTION INDUSTRY

SITI AISYAH ISMAIL

A dissertation submitted in partial fulfillment of the

requirements for the awards of the degree of

Bachelor of Quantity Surveying

Faculty of Built Environment

Universiti Teknologi Malaysia

JUNE 2018

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DEDICATION

To my late dad, Ismail Abas

Thank you for always believing in me. This is for you, Ayah.

Dearest mum, Kamariah Mohd Basri, and sisters (Sasa, Yana, Rara),

Thank you for all the endless love and support you gave me, morally and financially.

My beloved Amyrul Hafiz and dearest friends,

Thank you for always being there for me through thick and thin.

And last but not least, to myself,

Thank you for not giving up. All the hard work, sacrifices, sleepless nights, struggles,

downfalls have finally paid off!

“May Allah SWT shower His countless blessings on all of us in both worlds.

Ameen”

Love, A

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ACKNOWLEDGMENT

My deepest appreciation goes out to all those people that had provided me with

assistance, guidance, support, and motivation for me to complete the writing of this

thesis.

First and foremost, I would like to express my gratitude to my supervisor, Dr.

Shamsulhadi Bin Bandi. I am beyond grateful with him sharing his expertise, ideas,

guidance as well as encouragement for me upon completing this thesis. Thank you for

giving me the trust to conduct this study.

I am also thankful for my respondents who are willing to spare their time

despite their busy schedule for me. They deserve a special thanks for their assistance

in providing necessary information that is required for my thesis write-up.

Last but not least, to my family and Amyrul Hafiz, thank you for your endless

support and to my dearest friends (Zulaikha, Nabila, Raihan, Najiha, Husna, Zureen

and Zati) whom struggle with me in achieving the best for our degree, may we never

forget the memories we had upon completing our studies. Finally, we’ve made it girls!

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ABSTRACT

The volume of data generated by the construction industry has increased exponentially following an intense use of modern technologies. The data explosion thus lead towards the big data phenomenon which is envisioned to revolutionize the construction like never before. Like any other technologies, big data is a disruptive paradigm and inevitably will give impact to the construction industry. As the industry is refocusing towards an improved productivity, the appeal to embrace big data is certain given the value it offers. This certainly will benefit construction akin to the manufacturing and the retail industry alike. Nevertheless, a review of the literature suggested a limited coverage on the potential application of big data in construction as compared to other industries. This limits understanding of its potential, where the industry is seemingly unaware thus could not relate and extract its real value. Hence, this study aims to draw insights into the potential application of big data in the construction industry. The research objectives include: (1) to analyse the current extent of big data research in construction; (2) to map out the orientation of the current research on big data in construction; and (3) to validate the findings as the basis to identify the potential application of big data in construction. The qualitative method through a desk study approach has been carried out to attain the first two objectives. It involved a structured review process which covered articles from the online databases assisted by the NVivo software. This resulted in the theoretical orientation which was conceptualized as: (1) project management; (2) safety; (3) energy management; (4) decision making design framework; and (5) resource management. The theoretical orientation discovered from the review process form the basis for the semi-structured interviews that were held with industry personnel who have experienced big data. The outcome of the interview agreed that project management is the focused area of big data and added that the same attention should be given to resource management. Correspondingly, the findings then suggested that the potential application of big data in the construction industry can be categorized into; (1) Big data application through IoT devices; (2) Predictive model for decision-making; (3) Pricing system; and (4) Contractors’ information system. As big data is set to influence the industry, the research findings would be a catalyst for creating an awareness to support the development of big data for the construction industry.

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ABSTRAK

Bilangan data yang dihasilkan oleh industri pembinaan telah meningkat dengan pesat berikutan penggunaan teknologi moden yang berlebihan. Ledakan data seterusnya membawa kepada fenomena data raya di mana ia dijangka untuk merevolusikan industri pembinaan. Sepertimana teknologi-teknologi yang lain, data raya merupakan sebuah paradigma yang tidak dapat dielakkan dan bakal memberikan impak kepada industri pembinaan. Memandangkan industri sedang menuju ke arah produktiviti yang lebih baik, penglibatan untuk menerima data raya pasti memberi nilai yang sewajarnya. Ini semestinya akan memberi manfaat kepada industri pembinaan sebagaimana dengan industri pembuatan dan industri runcit. Walau bagaimanapun, kajian literatur menunjukkan liputan yang terhad mengenai potensi penggunaan data raya dalam pembinaan berbanding dengan industri lain. Hal ini menghalang pemahaman mengenai potensi data raya, di mana industri kelihatan tidak sedar sehingga tidak dapat mengaitkan dan mengekstrak nilai sebenar data raya. Oleh itu, kajian ini bertujuan untuk memberi gambaran tentang potensi penggunaan data raya di dalam industri pembinaan. Objektif penyelidikan merangkumi: (1) untuk menganalisis sejauh mana penyelidikan data raya dalam pembinaan; (2) untuk memetakan arah tuju penyelidikan semasa mengenai data raya dalam pembinaan; dan (3) untuk mengesahkan penemuan sebagai asas untuk mengenal pasti potensi penggunaan data raya dalam pembinaan. Kaedah kualitatif melalui pendekatan desk

study telah dijalankan untuk mencapai objektif pertama dan kedua. Ia melibatkan proses analisis berstruktur yang meliputi artikel dari pangkalan data dalam talian yang dibantu oleh perisian NVivo. Ini membawa penemuan kepada teori arah tuju data raya yang dikonseptualisasikan sebagai: (1) pengurusan projek pembinaan; (2) keselamatan; (3) pengurusan tenaga; (4) rangka keputusan kerja reka bentuk; dan (5) pengurusan sumber. Teori arah tuju yang ditemui dari proses kajian membentuk asas untuk wawancara separa berstruktur yang diadakan bersama pihak industri yang mempunyai pengetahuan dan pengalaman berkenaan data raya. Hasil temubual bersetuju bahawa pengurusan projek merupakan fokus utama kepada applikasi data raya dan perhatian yang sama harus diberikan kepada pengurusan sumber. Sejajar dengan itu, penemuan kemudian mencadangkan bahawa potensi penggunaan data besar dalam industri pembinaan dapat dikategorikan sebagai; (1) Penggunaan data raya melalui peranti IoT; (2) Model ramalan untuk membuat keputusan; (3) Sistem harga; dan (4) Sistem maklumat kontraktor. Oleh kerana data raya dilihat dapat mempengaruhi industri, penemuan penyelidikan ini akan menjadi pemangkin untuk mewujudkan kesedaran dalam menyokong pembangunan data raya dalam industri pembinaan.

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TABLE OF CONTENT

CHAPTER TITLE PAGE

ACKNOWLEDGMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENT vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF ABBREVIATIONS xii

LIST OF APPENDICES xiii

INTRODUCTION 1

1.1 Introduction 1

1.2 Background of Study 1

1.3 Problem Statement 4

1.4 Research Questions 6

1.5 Research Aim and Objectives 7

1.6 Scope of Study 7

1.7 Significance of Study 8

1.8 Research Methodology 8

1.9 Chapter Outline 10

1.10 Chapter Summary 11

LITERATURE REVIEW 12

2.1 Introduction 12

2.2 Big Data Evolution 12

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2.2.1 Big Data 1.0 (1994 – 2004) 14

2.2.2 Big Data 2.0 (2005 – 2014) 15

2.2.3 Big Data 3.0 (2015 – present) 15

2.3 An Overview of Big Data 16

2.3.1 Definition of Big Data 17

2.3.2 The V’s in Big Data 19

2.3.3 Categorisation of Big Data 22

2.4 Big Data Application in Other Industries 24

2.4.1 Banking 24

2.4.2 Healthcare 25

2.4.3 Retail 25

2.4.4 Manufacturing 26

2.4.5 Oil and Gas 26

2.5 Big Data and the Construction Industry 27

2.6 Triggering Constituents of Big Data in the Construction

Industry 28

2.6.1 Building Information Modelling (BIM) 29

2.6.2 Cloud Computing 29

2.6.3 Internet of Things (IoT) 30

2.6.4 Smart Buildings 30

2.6.5 Augmented Reality (AR) 31

2.6.6 Social Networking Services 31

2.7 Current Big Data Research in the Construction Industry 32


RESEARCH METHODOLOGY 37

3.1 Introduction 37

3.2 Research Design 37

3.2.1 Stage 1: Preliminary Study 40

3.2.2 Stage 2: Research Methodology 40

3.2.2.1 Desk Study 41

3.2.2.2 Semi-structured interview 41

3.2.2.3 Sampling 43

3.2.3 Stage 3: Data Analysis and Findings 44

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3.2.3.1 Detail Analytical Study 45

3.2.3.2 Qualitative Analysis using NVivo

Software 45

3.2.3.3 Interview Content Analysis 46

3.2.4 Stage 4: Conclusion and Recommendation 46


DATA ANALYSIS AND DISCUSSION 48

4.1 Introduction 48

4.2 Detail Analytical Study 48

4.2.1 Searching 49

4.2.2 Mapping Ideas and Analysis 50

4.2.3 Synthesis, Mapping, and Discussion of the

outcomes 50

4.4 Validation of the Findings 54

4.5 Respondents’ Background 54

4.6 Interview Findings 56

4.6.1 Concept of big data in the context of the

construction industry 56

4.6.2 Orientation of big data in the construction

industry 59

4.6.3 Potential application of big data in the



CONCLUSION AND RECOMMENDATION 73

5.1 Introduction 73

5.2 Achievements of Research Objectives 73

5.3 Research Limitations 77

5.4 Recommendations for future research 78

REFERENCES 79

Appendices A - C 93 - 142

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LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Definitions of Big Data from several literatures 18

2.2 Categorisation of Big Data 22

2.3 Big Data Context in the Construction Industry 27

2.4 Big Data Research from various literatures 33

3.1 Details of interview instrument 42

4.1 Detailed context of big data research area 52

4.2 Respondents’ Background 55

4.3 Respondents’ responses towards the big data concept 56

4.4 Respondents’ responses towards the big data orientation 59

4.5 Summarisation of respondents’ responses towards the big

data orientation 62

4.6 Suggested potential application of big data in the construction

industry 65

4.7 Correlation between the suggested potential applications with

the theoretical big data orientation 67

5.1 Detailed context of the suggested potential application 75

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LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Data growth rate in Zettabytes 2

1.2 Personalities of Big Data 3

2.1 Summarisation of Big Data Evolution 13

2.2 Information created surpass the available storage 17

2.3 Integration between Big Data Characteristics 21

2.4 Triggering Constituents of Big Data 28

3.1 Research Methodology Framework 39

3.2 Exponential Non-Discriminative Snowball Sampling 44

4.1 Generated model representing the frequency of big data

research area 51


concept by respondents 58

4.3 NVivo project map on big data concept in the context of


4.4 NVivo project map on big data orientation in the



orientation by respondents 62

4.6 NVivo project map on potential application of big data in

the construction industry 64

4.7 Correlation between suggested potential applications with

the theoretical big data orientation 66

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LIST OF ABBREVIATIONS

AI - Artificial Intelligence

AR - Augmented Reality

BD - Big Data

BIM - Building Information Modelling

CAD - Computer-Aided Drawing

CIDB - Construction Industry Development Board

CITP - Construction Industry Transformation Programme

GPS - Global Positioning System

ICT - Information and Communication Technology

IEEE - Institute of Electrical and Electronics Engineers

IoT - Internet of Things

IT - Information Technology

LEED - Leadership in Energy and Environmental Design

LOD - Linked Open Data

MGI - Mckinsey Global Institute

PC - Personal Computer

RFID - Radio Frequency Identification

SALSA - Search, Appraisal, Synthesis, and Analysis

SQL - Structured Query Language

UTM - Universiti Teknologi Malaysia

VR - Virtual Reality

WWW - World Wide Web

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LIST OF APPENDICES

APPENDIX TITLE PAGE

APPENDIX A Interview questions form 93 - 97

APPENDIX B Interview Transcript 98 - 128

APPENDIX C Published article in IJBES 129 - 142

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CHAPTER 1 INTRODUCTION

CHAPTER 1

INTRODUCTION

1.1 Introduction

This chapter delves into the background of the study, the issues identified, the

research questions, aim and the objectives of the study. A brief methodology is then

presented and ends by the organization of the study.

1.2 Background of Study

Today, we can no longer deny that our lives are surrounded by data almost at

all times. The advent of big data era is initiated by the data explosion resulted from the

presence of advanced technology in today’s world. Data in accordance to Simon

(2013) is stagnant but it will expand and become more influential by times as so big

data is becoming too big to neglect. According to Waal-Montgomery (2015)

prediction, the world’s data volume will rise at approximately 40% per year and will

continue to intensify fifty times from the current volume by the year 2020. Figure 1.1

shows the rapid growth rate of data in Zettabytes by Reckoning (2013) which reaffirm

the increase of the volume of data generated.

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Figure 1.1: Data growth rate in Zettabytes

Source: Reckoning (2013)

Big data has been buzzing among many industries around the world on its

potential in dissolving most of the industries’ common issues and transform them into

a smarter way of operating. The pace at which data is being generated has lead towards

data explosion hence big data gain its traction. Basically, big data is often termed based

on the 3Vs namely (i) Volume - amount of the data itself, (ii) Velocity – the speed

where the data is generated and (iii) Variety – the diversity and complexity of data

sources.

A model made by Elragal (2014) shows that the characteristics of big data

involve two additional attributes that are Veracity and Value. Elragal (2014) defines

Veracity as the accuracy of the data as the resources where the data is obtained from

should be precise and the security should also be assured. On the other hand, Value is

explained as the process of the product of the data generally described as information

is enriched as well as the information should be generated after all of the procedures

take place. As it is the major aim in the big data technology (Emani, Cullot, & Nicolle,

2015), it is included to the V’s of big data.

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Figure 1.2: 5 Personalities of Big Data

Source: (Elragal, 2014)

The significance of the additional attributes of Veracity is because of the arising

issues regarding the trustworthiness of the data (Yaqoob et al., 2016). On the other

hand, Value is added as in order to harness the benefits of big data, one must have the

insight on the worth of big data (Yaqoob et al., 2016). This is supported by a study

from Portela, Lima, and Santos (2016) that include Veracity and Value into the big

data personalities that stressed out on the quality of information presented in terms of

its worthy and reliability.

The construction industry is considered as the sector which responsible towards

all development projects in the world. There are a vast amount of data being generated

from the great number of resources for the construction projects. This includes both

written and graphic information produced at every construction stages, from planning

towards the project completion Shrestha (2013). Additionally, the rises of big data in

construction industry is also triggered by the digitalized revolution such as the

introductory of Building Information Modeling (BIM), Internet of Things (IoT), Smart

Buildings and others which adds to the tremendous amount of data growth (Bilal,

Oyedele, Qadir, et al., 2016). Other than that, the technological change in the

construction industry has also given impact towards the invaded of big data into the

industry. The data that are generated from the technology used in the construction

BIG DATA

Volume

Veracity

Value

Velocity

Variety

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industry reflects the 3Vs of big data basic concept and the utilization of these data

could be the next frontier for construction industry development.

1.3 Problem Statement

Peiffer (2016) asserted big data as one of the significant driving factors in

configuring the direction which should lead towards improving the industry’s

efficiency. Though the construction industry is acknowledged as one of the indicators

for economic well-being, productivity and efficiency are at an all-time low which

Harenberg (2017) sorely contended in comparison to when it was in the year 1993.

This inefficiency, according to Santiago Castagnino, Christoph Rothballer, and

Gerbert (2016) was the result of the slow movement made by the industry in adopting

new technologies. This is supported by the MGI’s digitization index that put

construction sector as the least digitized industry in the world. Santiago Castagnino et

al. (2016) added the deliberate changes made by the industry is caused by the

insufficient data-driven decision making.

Data is said to be the poster child in enhancing the industry’s productivity. This

follows as a real-time data exchange could lead to a broadened insight into the

industry’s operational performance thus making way for a smarter working (Peiffer,

2016). However, albeit of the massive amount of data that is generated in the

construction industry, the big data is usually siloed and not being fully utilized for a

bigger picture. According to Burger (2017), the inefficiencies of data usage is due to

the limited ability in dealing with unstructured data such as free text, images or sensors

reading. This is where big data could be the saviour in improving the utilization of

data.

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According to the Construction Industry Development Board Malaysia (CIDB),

reliable and quality big data is currently in demand to align with the board’s initiatives

under the aspiration of the Construction Industry Transformation Programme (CITP).

In conjunction with this, it is essential to identify the level of big data needs for the

industry. The current move by CIDB is justified as the most typical error made by

organizations was to utilize big data without assessing whether their needs could be

satisfied by the use of the technology (Portela et al., 2016). Likewise, Addo-Tenkorang

and Helo (2016), added that there appear to be a limited understanding of the value

and the potential of big data for construction. This had resulted in a consequential

discouragement in the progress of the adoption of big data in the construction industry

as compared to other industries.

Data and the construction industry are indivisible as the industry are dealing

with a huge amount of heterogeneous data. This follows as data related to construction

industry has been predicted by Bilal, Oyedele, Qadir, et al. (2016) to rise exponentially

with the advancement of technologies and the Internet of Things (IoT). According to

Addo-Tenkorang and Helo (2016), new opportunities in the form of valuable insights

can be developed by excerpting the huge amount of data obtained. Despite, a study

that focuses on the potential application of big data, particularly in the construction

industry, has not been comprehensively undertaken (Bilal, Oyedele, Qadir, et al.,

2016). This limits understanding of its potential, where the industry is seemingly

unaware thus could not relate and extract its real value.

Therefore, this study draws insights on the specific areas of construction big

data research as well as the orientation of big data research in the construction industry

and its potential application in the context of which area can fully benefit from big data

in order to exploit its value and performance in the construction industry. In

conjunction with that, the industry could increase their capabilities to harness the

potential of big data as well as encouraging talent and infrastructure development to

tackle the big data challenges.

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1.4 Research Questions

Based on the problems and issues highlighted, this study attempts to answer

the following questions:

i. What is the current extent of big data research in the construction

industry?

This research question will provide an indication of the area of

literature coverage on big data, particularly in the construction industry.

ii. What is the orientation of big data research in the construction industry?

This research question will provide an indication on the direction or

trends of big data in the construction industry.

iii. In relation to research question 2, what are the potential application of

big data in the construction industry?

This research question will provide an indication on the potential

application of big data in support of the construction industry

development.

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1.5 Research Aim and Objectives

This study aims to draw insights into the potential application of big data in the

construction industry thus the main objectives have been set as follows:

i. To analyse the current extent of big data research in the construction

industry.

ii. To map out the orientation of the current research on big data in the

construction industry.

iii. To validate the findings as the basis to identify the potential application

of big data in the construction industry.

1.6 Scope of Study

The focus of the study circulated on the current extent to which the construction

industry has engaged with big data. Thus, the scope of this research includes both

theories on the area of big data itself as well as the insight of real big data application

in the construction industry. For the first and second research objectives, it covered

articles on big data in the construction industry from the online database. Furthermore,

the third research objectives emphasized on the potential application of big data in the

construction industry based on the trends identified. The trends then were validated

through the interview with the industry’s personnel who has extended knowledge on

big data.

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1.7 Significance of Study

As big data is set to influence the industry, the research findings would be a

catalyst for creating the much-needed awareness to support the development of big

data for the construction industry. This would further lead the industry to gear up in

developing their capabilities in harnessing the potential of big data as well as

encouraging talent and infrastructure development to engage in the forthcoming wave

of big data technology in the construction industry.

1.8 Research Methodology

A structured and well-defined framework is the key to achieve the

research objectives. The research methodologies used in conducting this study

are as followings;

First Stage.

Preliminary study on the proposed research area was made for further

understanding on the topics in order to formulate the problem statement,

research questions, and objectives as well as its significance and scope of the

study.

Second Stage.

The second stage mainly focused on literature review where all the secondary

sources of knowledge or information regarding big data research in the

construction industry such as books, journal papers, online articles or e-news,

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and organization website were extracted to develop a further understanding on

the topic.

Third Stage.

This stage emphasized the research methodology that was used to achieve the

research objectives. For the first and second objectives, the best sources used is

through the secondary data which includes journal papers, articles, and

organization website. Next, data collection for the third objective was obtained

through a semi-structured interview with the industry’s personnel who has

extended knowledge on big data.

Fourth Stage.

At this stage, all data and information gained from the data collection (third

stage) were analysed and synthesized in order to resolve the research objectives.

Detail analytical study was used in attaining the first objective and for the second

objective, qualitative analysis through NVivo software was adopted to highlight

the trends. Last but not least, the content obtained from the interview was

analysed to achieve the third objective.

Fifth Stage.

Based on the data analysis output in the fourth stage, all the research questions

and objectives are resolved and the overall conclusion was made as well as

recommendations for further research were proposed in the fifth stage.

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1.9 Chapter Outline

Chapter 1 – Introduction

Chapter 1 is focused on the introductory of the research topic which includes

the issues as well as problem statement on big data particularly in the

construction industry, the research objectives, its significance and scope of the

study. This chapter discloses generally about big data and its involvement in

the construction industry.

Chapter 2 – Literature Review

In this chapter, it will review the literature readings made from the secondary

sources such as books, journal papers, articles and others in order to expand

the understanding of the topic. Literature reviews include the overview of big

data and its connection with data in the construction industry, trends in the

construction industry that trigger the advent of big data, big data application

across other industries and further dive into the current research of big data

application in the construction industry.

Chapter 3 – Research Methodology

Chapter 3 emphasized the research method used in collecting as well as

analysing the data. For this study, the data collection is made through both

primary (interview) and secondary sources (journals, articles, books, etc). The

data analysis is further explained in the next chapter.

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Chapter 4 – Data Analysis

All data and information collected in the previous chapter are analysed in

chapter 4. The data analysis adopted in this study to attain the research

objectives includes detail analytical study, qualitative analysis via NVivo

software and interview content analysis.

Chapter 5 – Conclusion and Recommendations

Last but not least, the outcome of the analysed data is concluded and

recommendations are proposed for further enhancement of the research topic

in this chapter.

1.10 Chapter Summary

In conclusion, this chapter has discussed briefly the background study with

emphasis on the problem statement. This chapter also serves as a guideline for the

entire following research activities. The next chapter will present reviews on the

previous literature in order to provide an overview of previous studies and theories

which are relevant to the research.

CHAPTER 2 LITERATURE REVIEW

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

Chapter 2 disclosed on the literature review regarding the overall context of big

data, a pinch of its arrival, its concept as well as the relationship with data in the

construction industry. Firstly, this chapter will present the introductory of the big data

evolution followed by an overview of big data. Secondly, it will go further to the

application of big data across other industries. Then, it started with the correlation

between the big data concept and data in the construction industry before dive further

into the emerging trends in the construction industry that trigger big data followed by

the current big data research made in the construction industry.

2.2 Big Data Evolution

Back to the early 1950s, the introductory of the first commercial mainframe

computers has triggered the activity of collecting and keeping huge amounts of data.

The data is highly structured and been used as an operational and information systems

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support (Lee, 2017). Generally, Information Technology (IT) and big data have a

forceful relationship as in according to Press (2013), Personal Computer (PC) was the

main contributor to the data growth as well as responsible for digitization. As for

example, computer networks were capable to shift the manual process of creating a

memo using a typewriter and assuring the message generation. This causes data

generated, stored, transferred and consumed to increase excessively.

Figure 2.1: Summarisation of Big Data Evolution Source: (Lee, 2017)

Figure 2.1 shows the stages of big data revolution from the 1990s to present

where it was initially triggered by the advent of World Wide Web (WWW). The

development of World Wide Web is seen as the carrier to the digitization of almost

every human activities thus responsible for the upswing amount of data created (Press,

2013). According to Viktor and Kenneth (2013), before the arrival of big data due to

the scarcity of data and costly process in collecting it. The decision was made based

on hypotheses guided by theories where data were gathered and correlation analysis

was conducted to certify the suitability of the proxies hence contributed to the slow

The advent of World Wide Web in the early 1990s

Big Data 1.0 (1994-2004)

- The appearance of e-commerce

- The development of web mining techniques

Big Data 2.0 (2005-2014)

- The arrival of Web 2.0 and social media

Big Data 3.0 (2015-present)

- Includes both data from Big Data 1.0 and Big Data 2.0

- The Internet of Things (IoT) as the major contributor

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process of data growth. As data is increasing remarkably, the usage of hypotheses as

decision-making means was no longer adequate hence the importance of the big data

to evolve.

2.2.1 Big Data 1.0 (1994 – 2004)

As stated in Figure 2.1, Big Data 1.0 was initially set off by the advent of World

Wide Web or called as Web 1.0 where it was a read-only web through internet

browsing without any allowance for the users to interact with the web content (Nath,

Dhar, & Basishtha, 2014). Lee (2017) said that users only have a minor contribution

to the web content due to the limited web applications. Thus, it gives rise to the

development of e-commerce where the web content was generated by the online firms.

It is the initial stage of Web 1.0 where business firms leveraged the power of basic

internet technologies to establish a web presence, building capacity to process large

data conducive to their efficiency improvements (Provost & Fawcett, 2013). This is

driven by the development of web mining techniques where it enables the users’ online

activities being monitored and analysed.

According to Lee (2017), web mining techniques can be sorted into three

categories. The first one is web usage mining where it enables the provision of

personalized services based on the users’ browsing style captured. Secondly, web

structure mining will elucidate the links and website structures. The third techniques

are the web content mining which involved the extraction of valuable information or

knowledge from the web content. However, in the Big Data 1.0 era, there were

limitations in the application of these techniques.

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2.2.2 Big Data 2.0 (2005 – 2014)

As Big Data 1.0 is related to Web 1.0, the age of Big Data 2.0 was in

conjunction with the arrival of Web 2.0 and social media phenomenon Lee (2017).

Web 2.0 as stated by (Nath et al., 2014) is an advanced way of using the present

internet technologies. As compared to the one-way communication of Web 1.0, Web

2.0 enables the users to interact and contribute to the web content. For Web 2.0, the

web page contents such as text, video, and photo were generally produced by its users

itself (Cormode & Krishnamurthy, 2008). This is complementary to the existing of

social media where it has been propulsive to the Big Data Revolution where Simon

(2013) describes Web 2.0 as ‘the social web’.

In this era, the web mining techniques developed in Big Data 1.0 is supported

by the social media analytics where human behaviours on social webs were analysed

and interpreted. This gives vision as well as drawing assumption based on the users’

interests, web navigating styles, feelings or thoughts as well as their expertise (Lee,

2017). Additionally, the usage of social media rose from the expansion of social

networks platform. As according to Simon (2013) wider networks and platform

supplement the users capacity on networks hence the data grows remarkably. This

further leads to the vast amount of unstructured data by social media and represents

the big data.

2.2.3 Big Data 3.0 (2015 – present)

Big Data 3.0 includes both data from Big Data 1.0 and Big Data 2.0 and is

driven mainly by the Internet of Things applications. Internet of Things or familiarly

called as IoT is referred as the interactions of daily things, which regularly include the

ubiquitous intelligence (Xia, Yang, Wang, & Vinel, 2012). Lee (2017) defines IoT as

an innovation in which gadgets and sensors have a particular identifier with the

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capacity to share information and interact over the internet-deprived of the human

interference. Moreover, according to Schwab (2017), the tipping point of IoT is the

amount of sensors connected to the internet that is now has reached 1 trillion. Given

this point, the linked gadgets and sensors are said to outweigh the web-based social

networking and e-commerce sites as the initial sources of big data (Lee, 2017).

In addition, one of the remarkable cases of machine-speed produced

information that could be gathered and dissected is the radio frequency identification

(RFID) (Zikopoulos, Parasuraman, Deutsch, Giles, & Corrigan, 2012). The RFID is

shifting the world to be considerably more instrumented and interconnected. The IoT

can create big data as the volume of data inferable from IoT is considerable. As sensors

collaborate with the world, RFID, for instance, will produce enormous volumes of data

in an exceptional velocity compared to the traditional way of data capturing (O'Leary,

2013). Consequently, this pushes the Internet of Things to generate big data.

2.3 An Overview of Big Data

With the arrival of big data, data will no longer be viewed as stagnant whose

worth is limited to the accomplishment of its gathering purposes (Viktor & Kenneth,

2013). Whereas, in order to cross the boundary of data collecting purposes, the data

need to be handled by means of advanced technologies and human skills as well as

data entry base. However, according to Akbar (2017), the current amount of digital

information had surpassed the ability of the present tools to process it. Figure 2.2

shows the gap between the information created and the availability of storage

illustrated by IDC.

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Figure 2.2: Information created surpassed the available storage

Source: (IDC, 2009; Cukier, 2010)

Cukier (2010) in a special report titled Data, data everywhere uses the chart

from Information Data Centre to highlight the state of data growth which is faster than

the network capability to support it all. He also highlighted that although there has

been a plenty of tools to gather, process and convey information such as sensors,

computers, and advanced gadgets, data generated has outstripped the available storage

space. This situation is described as “The Industrial Revolution of Data” by Joe

Hellerstein, a computer scientist at the University of California in Berkeley and it has

affected various public and private sectors (Cukier, 2010).

2.3.1 Definition of Big Data

Generally, there is no official agreement on the actual definition of big data as

there are various translations of the term in different literature. Part of the definitions

extracted from several literatures are as in Table 2.1.

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Table 2.1: Definitions of Big Data from several literatures

Source: As shown

No Authors Definition

1 (Manyika et al.,

2011)

“Big Data is a set of massive data quantity that the

technological capacity is unable to support the process of

storing, running and operating it”

2 (Mills et al.,

2012)

“Big Data is the hi-tech, high velocity, high-volume,

unpredictable and varieties of data to be collected, stored,

delivered and processed”

3 (I. Gartner, 2014) “Big Data is characterized as high volume, high speed as

well as high variety data resources that need a new way of

operation to improve decision making, understand

revelation and process development”

4 (Hashem et al.,

2015)

“Big Data is the group of techniques and technologies where

its new structures are incorporated to extract the values in

variety, complex and high volume data collections”

5 (Whitehorn,

2012)

“Big Data is the data which is not well suited to be fit into

tables and reacts inadequately to control by Structured

Query Language (SQL)”

6 (C. P. Chen &

Zhang, 2014)

“Big Data is a set of the vast volume of complex data that is

unmanageable efficiently by the cutting edge of data

processing technologies”

7 (O'Leary, 2013) “Big Data represents the skyrocketed volume of data

created, the endeavours in enabling it to be analysed and

utilize the value in order to enhance productivity,

technologies advancement and decision making”

8 (K. Davis, 2012) “Big Data is a high volume of data to deal with and analysed

by conventional database protocols such as Structured

Query Language (SQL)”

9 (Koseleva &

Ropaite, 2017)

“Big data is a set of data that are terabytes to petabytes and

even reached exabytes and the gigantic volume of data

surpass the ability for a normal database software to process

them adequately”

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Despite the fact that there is no certain description to actually define big data,

there are few keywords that can be grasped from the definitions mentioned in Table

2.1. Essentially, there is a connection between most of the definitions and it can be

concluded based on the authors’ ideas. It can be said that big data is broadly portrayed

by the 3Vs which are Volume, Velocity, and Variety of data which today’s technology

is incapable of processing it.

2.3.2 The V’s in Big Data

The renowned 3Vs characteristics which form the big data concept were

established by one of the Gartner analyst named Laney Doug in 2001. Respectively,

the Gartner’s IT Glossary defined big data as a high-volume, high-velocity and/or

high-variety information assets that demand cost-effective, innovative forms of

information processing that enable enhanced insight, decision making and process

automation (I. Gartner, 2014).

Definition of big data might varies in different literature, but the domain of the

concept is the 3Vs characteristics. Volume is the most important characteristic that

represents the extent of big data magnitude. According to C. P. Chen and Zhang (2014)

volume is epitomized as the size of the data itself that is generated by the advanced

technologies, networks and human interactions especially on the nets (Hammer,

Kostroch, & Quiros, 2017).

On the other hand, velocity signifies that data is produced at a remarkably high

speed which outstrips the conventional systems (Zikopoulos et al., 2012). Data

velocity is regarded as a supplementary to data volume as greater data volume requires

the data processing to be winged (Özköse, Arı, & Gencer, 2015). As Gartner (2015)

has profoundly predicted, there will be as much as 20.8 billion connected devices by

the year 2020 as compared to 6.4 billion as reported in 2016. This shows that the pace

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of data velocity will continue to speed up following the connected devices’ enhanced

features for data streaming (Lee, 2017).

Last but not least is variety which means the diversity and complexity of data

categories and sources (Zikopoulos et al., 2012). According to Özköse et al. (2015),

data may be derived from various resources both internally and externally. Similarly,

O'Reilly (2014) emphasized in his book that these data come from an assortment of

structures and it is often hard to obtain an impeccably, processing-ready data. Such

data can be categorized into structured, semi-structured or unstructured data. This

classification of data is derived from the existence of the social network, sensors,

mobile devices, GPS and other technological appliances (Portela et al., 2016).

Additionally, as data grows vastly and rapidly, it raises the concerns on the

quality of information obtained. With this in mind, Zikopoulos et al. (2012) added two

more V’s namely, Veracity and Value to the big data concept. Veracity is much related

to the accuracy, consistency, and reliability of the data. The various sources of data

production give the challenge to ensure its trustworthiness (Zikopoulos et al., 2012).

In order to address this, Özköse et al. (2015) suggested that the data sources should be

extracted from an authorized resource with the provision of its security. In short, the

data should only be accessible with permission.

Value as according to Zikopoulos et al. (2012) is the big data potential in

providing an added value to the enterprise’s technology in terms of cost benefits. In

fact, the value is the primary aim of big data technology (Emani et al., 2015).

Correspondingly, value basically means the worth of hidden bits of knowledge in big

data (C. P. Chen & Zhang, 2014). Thus, Lee (2017) underlined that in order to fully

utilized the benefits of big data, firms need to understand the significance of applying

big data towards their revenue, operational costs and services.

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Over time, the list of V’s continues to expand as big data has a forceful

connection towards most of the digital innovations in this big data 3.0 era which

includes artificial intelligence (AI), data science and the Internet of Things (IoT)

(Marr, 2017). Variability is added by SAS (2013) referred as a variation of speed for

data streaming. In conjunction with the rising of data velocity and variety, data streams

may change unpredictably. Apart from that, as big data are produced by changing

technology or business situations, it could prompt invalid results as well as to delicacy

in big data as data source hence Hammer et al. (2017) developed the next V named

Volatility.

Other than that, as proposed by (Özköse et al., 2015), big data does not really

necessary depends on the volume but also consider the data complexity. SAS (2013)

also highlighted complexity as the dimension of big data due to the high-variety of big

data sources hence complicate the gathering, storing and analysing the process of the

heterogeneous data. Last but not least, in recent research by Lee (2017) on the big data

dimension, he proposed additional features called Decay that denote the deteriorating

of the data value in future. Figure 2.3 shows the integration between all of the big data

characteristics that have been discussed.

Figure 2.3: Integration between Big Data Characteristics

Source: (Hammer et al., 2017; Laney, 2001; Lee, 2017; SAS, 2013; Zikopoulos et al.,

2012)

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Based on Figure 2.3, it is clearly seen that all of the characteristics discussed

over the years on the big data dimension is actually complement one another. In short,

the value should be generated through today’s hi-technology against the data volume,

variety and veracity at instant data processing otherwise the value could be depreciated

(Lee, 2017; Reeve, 2013; Zikopoulos et al., 2012).

2.3.3 Categorisation of Big Data

According to Özköse et al. (2015), data may be derived from various resources

both internally and externally. Similarly, O'Reilly (2014) emphasized in his book that

these data come from an assortment of structures and it is often hard to obtain an

impeccably, processing-ready data. Such data can be categorized into structured, semi-

structured or unstructured data. This classification of data is derived from the existence

of the social network, sensors, mobile devices, GPS and other technological appliances

(Portela et al., 2016).

Hashem et al. (2015) suggested that in order to further comprehend the

characteristics of big data, it is best to be divided into few categories. Table 2.2

illustrates the classifications and its examples.

Table 2.2: Categorisation of Big Data

Source: (Hammer et al., 2017; Hashem et al., 2015; Lee, 2017)

Category Examples

Data Sources Web & Social

a) Social networks (Facebook, Twitter, Linkedln)

b) collaborative projects over the web c) search engines (Google, Bing) d) mobile data content (call detail record, location update, text messages)

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Category Examples

Sensing

a) Fixed sensors (home automation, weather/pollution sensors, traffic sensors/webcam, scientific sensor) b) Mobile sensors (GPS, Satellite Images) Transactions – financial and work data, commercial transactions, e-

commerce, bank records, credit cards

Internet of Things (IoT)

a) A machine-generated data from hardware or software (computer, mobile devices, machines)

b) An enormous volume of data and information are created when

those devices connected to the Internet

Content

Format

Structured – manageable data and available in various types of conventional databases. (numbers, words, dates)

Semi-structured – unorganized structured data that do not compatible with the traditional database system. Unstructured – data with no specific format and lack of consistency for proficient computing. (text messages, location information, data from

social networks)

Data Stores Document-oriented Column-oriented Graph database

Key-value

Data Staging

Data Processing Batch

Real-time

In conclusion, the classification made is closely relatable to the big data

dimension. For instance, the format of today’s data varied from unstructured to

structured data due to the abundant data sources available (Variety). Indirectly, the

variations in data source cause the overflowing of various data size (Volume) to be

processed either in batch or real-time (Velocity) taking into consideration its idleness,

reliability, and bias (Veracity) (Hashem et al., 2015).

Cleaning

sorting the unnecessary

and incomplete data

Transform

converting data for analysis

Normalization

structuring database to reduce data repitition

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2.4 Big Data Application in Other Industries

In recent times, big data has been discussed across various sectors and is

considered as a game changer in major industries (Gaitho, 2017). For this reason, many

organizations have taken steps to change their plan of action in utilizing the big data

value effectively (Akbar, 2017). A survey made by Gartner in 2015 proved that

companies have incrementally increased their investment in big data to 75% from 58%

recorded by the same survey in 2012. The extensive scope of big data has provided a

massive scale of potential and value that can be generated across different industries

such as banking, healthcare, retail, manufacturing and the upstream industry.

2.4.1 Banking

Banks deal with numerous data of their client’s personal information was

generally the progress was monitored and evaluated through internet and data

analytics. However, with the advent of big data, this information can be monitored in

real time hence enabling banks to deliver the clients need (Mauricio, 2016). According

to Feschyn (2017), it is crucial to understand the clients’ needs and preferences as

clients usually have high standards on how they interrelate with the banks. By meeting

the clients’ requirement, it may actually improve the banks’ overall profitability

(Mauricio, 2016). Furthermore, big data technology could also provide a better

predictive system especially in engaging with the risk. Fraud is one of the major

concern in the banking sector and big data could be the reducing factor of this problem

(Mauricio, 2016). For instance, fraud signals could be sensed through the systems

empowered with big data. This system could analysed the signal in real time and

predict unauthorized users or transactions hence improving the banking security

(Feschyn, 2017).

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2.4.2 Healthcare

The high volume of big data along with its complexity, variety, and velocity

has also provided healthcare stakeholders promising new strings of information.

Traditionally, data collected in healthcare or medical industry is lack of integration as

it is controlled separately by various specialists' surgeries, hospitals, clinics and

administrative divisions. However, in recent big data projects by Pittsburgh Health

Data Alliance, a partnership between medical and data professionals is being

developed to excerpt data from numerous sources such as medical and insurance

records, wearable sensors, and genetic data as well as social media data. The

partnership enables a data integration to take place in providing doctors a better

prediction on the treatment type to be prescribed. This can be done by analysing and

comparing all data from the patient as well as other similar patients that will result in

the emphasizing of the issues and emerging trends (Marr, 2015). Hence, big data could

help in addressing the major errors in medical sector which is medication prescription

(Ayers, 2017).

2.4.3 Retail

The retail sector is among the earliest to recognise the potential of big data.

This follows from the upsurge of e-commerce during the big data 1.0 era (Laney,

2001). During that time retail businesses leveraged the power of basic internet

technologies to establish a strong web presence followed by building their capacity to

process a large data which was conducive to their efficiency improvements (Provost

& Fawcett, 2013). The potential was further extended in analysing the vast amount of

data to support the decision to expand businesses, improve cost efficiency and revenue

forecasting (Meneer, 2015).

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2.4.4 Manufacturing

Manufacturing is another leading sector that has moved towards big data

exploration in enhancing their product quality, and at the same time reducing the

operational costs (Oracle, 2015). External data, especially from social networks and

suppliers’ data combined with data from sensors and machines, has given valuable insights

into the existing information. In this respect, big data was utilized to analyse varieties in

enhancing the efficiency of manufacturing and the operational process by providing the

bird’s eye view of the processes which led to a better decision making. Apart from that, big

data technologies also assist in improving the product quality and reducing the overall cost

through production and quality data analysis along with customers’ returning data, capacity

consumption as well as machinery efficiency (Oracle, 2015).

2.4.5 Oil and Gas

The oil and gas industry has also gained a lot from big data. According to B.

Mathew (2016), in the current situation, data collected particularly in the operational

process is used mainly for detection and control purposes. Big data’s advanced

analytics assisted in the decision making where big data insights were used to plan for

predictive maintenance. In this case, it was reported that the technology has managed

to bring the maintenance cost down to about 13% (Choudhry, Mohammad, Tan, &

Ward, 2016). The benefits of digital monitoring and predictive maintenance extends

towards detecting errors in equipment and performing maintenance before they are entirely

damaged. It was reported by analytics firm, Kimberlite that an approximately $49 million

annually were wasted due to an unplanned downtime (Choudhry et al., 2016). Hence, big

data in this respect helped to enhance production and addressed the financial impacts before

it eventually occurs.

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2.5 Big Data and the Construction Industry

Construction is one of the major industry that is responsible towards a country

development. The construction works to be carried out in a project is dynamic (Wood,

2016) and involve a high volume of data exchange from various stakeholders to be

gathered and processed (Shrestha, 2013). Shrestha (2013) added that data is generated

throughout the various phases of construction projects from planning phase to

completion. As shown in Table 2.3, the stream of data includes design and financial

data, sensors and equipment data, photos and videos and others. This data is often

large in volume, highly diverse in format and dynamic. The multi-faceted data reflects

the multitude characteristics of data streaming from construction activities thus sits in

conformity with the 3V’s concept of big data.

Table 2.3: Big data Context in the Construction Industry

Source: Aouad, Kagioglou, Cooper, Hinks, and Sexton (1999); Bilal, Oyedele,

Qadir, et al. (2016)

Characteristics Contributors Examples

Volume A large volume of data

from different sources

Design data, cost data, financial data,

contractual data, Enterprise Resource

Planning (ERP) system, etc

Variety Diversity in the

content format

DWG (drawing), DXF (drawing

exchange format), DGN (design),

RVT (revit), ifcXML, ifcOWL,

DOC/XLS/PPT (Microsoft format),

RM/MPG (videos), JPEG (images)

Velocity Dynamic nature of

data sources

Sensors, RFIDs, Building

Management System (BMS)

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Further, Table 2.3 shows that the advancement of construction processes

through the widespread utilization of these data shall be the next frontier of

construction industry innovation and productivity. This is supported by Harenberg

(2017) who mentioned real-time data processing as the future booster of productivity

in construction.

2.6 Triggering Constituents of Big Data in the Construction Industry

The digitalized revolution has impacted the construction industry rather

significantly as the industry is dealing with the heterogeneous amount of data (Bilal,

Oyedele, Qadir, et al., 2016). These triggering constituents to big data are identified as

in Figure 2.4 and discussed in the following sub-headings.

Figure 2.4: Triggering Constituents of Big Data

Source: (Bilal, Oyedele, Qadir, et al., 2016)

Triggering Constituents of Big Data

BIM

Cloud Computing

IoT

Smart Buildings

Augmented Reality

Social Networking

Services

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2.6.1 Building Information Modelling (BIM)

BIM is anticipated to capture the multi-dimensional CAD data to deliberately

support the multidisciplinary and coordinated working environment among the

stakeholders involved in a project (Eadie, Browne, Odeyinka, McKeown, & McNiff,

2013). As BIM involves capturing the additional layers of information throughout the

entire building lifecycle, BIM is perceived to transform the construction industry

across various perspectives (Azhar, 2011). Though data volume has been the

characteristic of BIM, yet Humphreys (2016) argued that this data are not precisely

big data. This follows the huge files of BIM with the combination of the numerous

models is still promptly prepared only to be processed by BIM applications. Likewise,

the arrival of built-in devices and sensors has increased the amount of data generated

where it eventually leads to the wellsprings of Big BIM Data (Bilal, Oyedele, Qadir,

et al., 2016). Thus, this triggers the construction industry to penetrate the big data era.

2.6.2 Cloud Computing

Cloud computing is an internet computing trend which on request, give access

to the merge of configurable resources (Bughin, Chui, & Manyika, 2010). The main

purpose is to provide multiple users with access to data storage and computation

without each having to resort to an individual license. The acceleration of cloud

computing technology has contributed to the evolution of big data (Qubole, 2017). As

cloud computing is supporting the coordination of errands in the BIM-based

application, it has been broadly applied in the construction industry and big data

performance in this revolution is astounding (Bilal, Oyedele, Qadir, et al., 2016). In

addition, cloud computing and big data are said to be an ideal combo that contributes

to the cost efficiency and extensible infrastructure in supporting big data and business

analytics (Ferkoun, 2014).

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2.6.3 Internet of Things (IoT)

The Internet of Things (IoT) has been the main pillar that triggers the big data

3.0 era. Basically, IoT is a system of Internet-connected devices that gather and

transfer data through installed sensors (Meola, 2016). IoT application frequently

conveyed a substantial number of sensors devices for data accumulation. As the

industry presents boundless big data utilization cases for IoT, big data is inalienably

the subject of intrigue (Bilal, Oyedele, Qadir, et al., 2016). Among the prominent areas

of IoT applications includes logistics, transport, asset recording, intelligent homes and

buildings, energy and agriculture. Bilal, Oyedele, Qadir, et al. (2016) claimed that IoT

and big data are interdependent trends where a huge amount of data is created, accessed

and analysed in real-time in construction applications. Additionally, Pal (2015)

suggested that during the selection of big data processing technology, a huge flood of

information produced by IoT triggers big data on a reciprocal basis following the

selection of big data processing technology.

2.6.4 Smart Buildings

Smart building technology assimilates the contemporary technologies with

existing building systems to attract the economic trade-off between comfort

maximization and energy reduction (Khan & Hornbæk, 2011). Often, these systems

will produce an enormous volume of data and the greater part of this information often

stay undiscovered and eventually disposed of. According to Bilal, Oyedele, Qadir, et

al. (2016), this data needs to be interpreted to truly reflect smart buildings hence gives

big data analytics a significant role to play. The information and communication

technology (ICT)-based integration and development systems, particularly Internet of

Things is an important catalyst for various applications, both industry and the general

population in realizing the smart buildings (Perera, Zaslavsky, Christen, &

Georgakopoulos, 2014). In this sense, Moreno et al. (2016) opined that big data and

IoT are an impeccable combination in enhancing energy efficiency for smart buildings.

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2.6.5 Augmented Reality (AR)

Augmented Reality is a technology that coordinates virtual object images into

real-world images. These images can be taken from the camera or, by using a live

view, the audience can be added directly to the world (Reiners, Stricker, Klinker, &

Müller, 1998). According to Jiao, Zhang, Li, Wang, and Yang (2013) AR comes from

‘Virtual Reality’ (VR) and provides a half-depth environment that highlights the exact

alignment between actual scenes and virtual world images in real time. It is also

broadly recognized as an assuring technology to improve human viewpoint.

Additionally, the means to enhance prevailing big data visualization techniques is

correlated with AR and VR where it is relevant for human limited perception

capabilities (Olshannikova, Ometov, Koucheryavy, & Olsson, 2015). Consequently,

AR and big data are certainly unavoidable where the complexity related with big data

in construction is tremendous and must be overcome by advanced visualization

methods, specifically AR and VR (Bilal, Oyedele, Qadir, et al., 2016).

2.6.6 Social Networking Services

Social media is one of the exciting trends that could assist the construction

industry to improve the communication among project teams (Jiao, Wang, et al.,

2013). Yet, one of the main challenges is to accede the value and exploring ways of

analysing it (H. Chen, Chiang, & Storey, 2012). This follows from the enormous

volume of heterogeneous data produced by the social networks. Hence, to properly

analyse data from social media, the analytical techniques of data analysis need to be

modified and incorporated into the new enormous data for enormous information

processing (Bello-Orgaz, Jung, & Camacho, 2016). In relation to this, big data can be

utilized in developing appealing domain applications through the high volume,

velocity, and variety of social network data to improve stakeholders’ productivity.

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2.7 Current Big Data Research in the Construction Industry

Big data has begun to set foot in the construction industry in sync with other

sectors that have long benefited from big data. In this regard, the construction industry

could exploit big data in the same manner as anticipated by the other sectors or

industries. As discussed earlier, this includes enhancing efficiency, decision making,

and sensors monitoring. Bilal, Oyedele, Qadir, et al. (2016) maintained that the outlook

on the applicability of big data in construction could be magnified as the triggering

constituents discussed in section 2.4 advanced. Thus, the surge of these constituents

and trends could be the factors to propel the construction industry to the next level of

data-driven initiatives.

The current big data research or application excerpted from various literature

is summarized in Table 2 with the important concepts identified from the review

process are aggregated and accentuated in brackets. The findings will become the basis

to map the orientation of big data research in construction and subsequently suggesting

the probable direction for research to ensue.

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Table 2.4: Big Data Research from various literature

Source: As shown

No Big Data research area from the literature review Authors

1 BD with Visual Analytics used for (building performance) comparison that leads to renovation and

construction with low (energy) consumption.

(Ioannidis et al., 2015)

2 LEED uses actual data to verify the (building performance) (D. Davis, 2015)

3 Improve (project management) by using technologies or sensors for (performance) monitoring and

tracking

(Wood, 2016), (Bleby, 2015),

(Yang, Park, Vela, & Golparvar-

Fard, 2015)

4 Cost efficiency (design) through a real-time, data-focused predictive model. (Sadhu, 2016)

5 BD assist in (project management) to ensure the project is delivered on (time) and (minimize delays) (Sadhu, 2016), (Rijmenam, 2015),

(Faure, 2016), (Augur, 2016),

(Akbar, 2017)

6 Real-time data sharing to improve (communication) between stakeholders (Rijmenam, 2015), (Augur, 2016)

7 Resource tracking through sensors-equipped assets or machinery. (resource management) (Rijmenam, 2015), (Augur, 2016),

(Azzeddine Oudjehane & Moeini,

2017), Akhavian and Behzadan

(2015)

8 Deriving information from stakeholders to improve the (planning) process and (project management) (Caron, 2015)

9 Integration of information technologies with data handling in facilitating (decision-making) for

(project management)

(Martínez-Rojas, Marín, & Vila,

2015)

10 BD generate (prediction) system for construction businesses bankruptcy (Hafiz et al., 2015)

11 Drones use for construction progress monitoring (project management) (Azzeddine Oudjehane & Moeini,

2017), Knight (2015)

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12 Geospatial/geo-location data for (resources optimization) and (resource management) (Akbar, 2017)

13 Data simulation tool in reducing project (risk). (Akbar, 2017)

14 BD for construction (cost management) through tender price assessment system.

(project management)

(Y. Zhang, Luo, & He, 2015)

15 Visual BD to improve (communication) among project stakeholders. (K. K. Han & Golparvar-Fard, 2017)

16 Assess (Construction waste management) performance using BD (Lu, Chen, Ho, & Wang, 2016), (Lu,

Chen, Peng, & Shen, 2015)

17 Developing (waste) simulation tool using BD for (Construction waste management) (Bilal, Oyedele, Akinade, et al.,

2016)

18 Social network analysis and (energy) usage analyses as sources in establishing an integrated green

building (design) model

Redmond, El-Diraby, and Papagelis

(2015)

19 BD algorithms to accurately reduce the design space and enabled generative (design) tool (Bilal, Oyedele, Qadir, et al., 2016)

20 BD and VR for better building (design) decision (Bernstein, 2017), (Barista, 2014)

21 BD helps in generating a predictive model for (energy) consumption (Moreno et al., 2016)

22 BD algorithm for (building performance) in terms of (energy) consumption (P. A. Mathew et al., 2015)

23 Implementing prototype software called Project Dasher for (energy) data visualization and real-time

monitoring.

(Khan & Hornbæk, 2011)

24 BD analysis used to understand energy consumption behaviour thus help to improve (energy

efficiency) in building

(Koseleva & Ropaite, 2017), (Janda

et al., 2015)

25 Real-time (energy) consumption data monitoring and control to improve energy efficiency (Wei & Li, 2011)

26 BD-based platform to visualize workers’ unsafe (safety) act in real-time (SY Guo, Ding, Luo, & Jiang,

2016), (Shengyu Guo, Luo, & Yong,

2015)

27 Use wearable to track worker proximity to rolling (safety) exclusionary zones (Wood, 2016)

35


28 Use drones to check on site (safety) (Oudjehane & Moeini, 2017)

29 Real-time (safety) tracking and data visualization technologies improve (safety) understanding. (Teizer, Cheng, & Fang, 2013)

(Hampton, 2015)

30 Application of BD-driven BIM system in improving construction (safety) (S. Zhang, Teizer, Lee, Eastman, &

Venugopal, 2013)

31 Integrating BIM data with external data such as Linked Open Data (LOD) for better (project

management) and reduce project (risk)

(Curry et al., 2013)

32 Sensor-based fire-fighting system for skyscraper building in associate with the authorities help in a fire

detecting as well as evacuation process (safety)

(Stankovic, 2014)

33 Predicting site injury and workers’ behaviour towards (safety) through 3D skeleton motion model from

videos.

(S. Han, Lee, & Peña-Mora, 2012)

34 Data from robotics and automated equipment has the potential to improve job (safety) and enhance

construction (productivity).

(Skibniewski & Golparvar-Fard,

2016)

35 Capturing (safety), quality and performance data for real-time analysis in improving the site (safety)

and construction work (productivity).

(Bleby, 2015)

36 Big Data from mobile apps for a contractor to track (resource) and document schedule changes to

enhance (resource management).

(Sadhu, 2016)

37 (Energy) consumption prediction through computational models developed based on user behaviour

for better (energy management).

(C. Chen & Cook, 2012)

38 BD in (design) model comprises of architectural, structural, and building services data to enhance

(design) efficiency

(Porwal & Hewage, 2013)

39 Past project data-driven (design) to improve (design) decision and efficiency. (Barista, 2014)

36

36

2.8 Chapter Summary

In a nutshell, this chapter summarized the literature review on the overall

context of big data in general as well as the extent of big data in the construction

industry. It can be said that big data has invaded the construction industry and likely

to remain. Hence, it is important for the industry to exploit the benefits of big data as

it is forecasted to transform the way in which construction industry is dealing with the

high volume, velocity, and variety of data. The past studies discussed in this chapter

regarding the big data application in the construction industry will be the basis for

developing the research method.

0

0

CHAPTER 3 RESEARCH

METHODOLOGY

37

37

CHAPTER 3

RESEARCH METHODOLOGY

3.1 Introduction

Generally, research methodology is a process which describes the procedure of

the study that will be conducted. It is important to determine the most appropriate and

effective method in responding to the research questions. Hence, this chapter

represents the research methodology adopted in collecting data in order to achieve the

research objectives. Firstly, it highlighted the research design for this study which

includes the research approach and strategy. Next, the techniques applied in data

collection were justified as well as the method for data analysis and validation of

information were discussed.

3.2 Research Design

The qualitative research design was adopted for this study. According to

Bryman (2008), qualitative research is a research strategy that typically emphasizes on

words rather than the computation of data. In this regard, the aim is to provide a thick

explanation about phenomena following the specific issue identified from the literature

(Elo & Kyngas, 2008; Fellows & Liu, 2008). The decision for adapting the strategy

38

38

was also guided by the objectives of the study. As the research objectives

include analysing the current extent of big data research and mapping out its

orientation and potential application, these are better achieved by going deep through

an analytical explanation of the existing research (Creswell, 2005).

Generally, research design is described as the strategy to assimilate all the

components of the research in a comprehensible and rational way so that the evidence

obtained is guaranteed to address the research problem (Labaree, 2009). For this study,

the research design is categorized into four stages which are the preliminary study,

research methodology, data analysis, and conclusion. Figure 3.1 shows the research

methodology framework for this research in light of achieving the research objectives.

39

39

Figure 3.1: Research Methodology Framework

First Stage - Preliminary Study

1. Identify the issues regarding big data in the construction industry. 2. Specifying the problem statement 3. Identification of the research objectives. 4. Identification of scope and significance of the study.

Research Objective 1

To analyse the current extent of big data research in the construction industry

Literature Review

Literature review aims: 1. Enhance the understanding on overall context of big data

2. Identify the involvement of big data in the construction industry

Data Collection

Secondary data sourced from past journals, articles, books, dissertations and

other related sources – desk study

Research Objectives 1 & 2

Third Stage - Data Analysis & Findings

Fourth Stage – Conclusion and Recommendation

To validate the findings as the basis to identify the potential application of big data in the

construction industry

Research Objective 3 Research Objective 2

To map out the orientation of the current research on big data

in the construction industry

Second Stage – Research Methodology

Identifying data collection method and instrument

Research Objectives 3

Primary data - semi-structured interview with the industry personnel who have experienced and knowledge

on big data.

Literature review process with detail analytical study


Qualitative Analysis using NVivo Software


Interview content analysis with the assist of NVivo


40

3.2.1 Stage 1: Preliminary Study

Initially, a preliminary study was conducted in order to describe the

background study in detailed and further identify the issues regarding the chosen

research topic to form the problem statement. A research problem is important to be

emphasized as it will show the significance and direction of the study. The preliminary

study can be made through literature reviewing from various sources such as journals,

articles, books, and others to extend the view on the research topic as according to

Welman, Kruger, and Mitchell (2005), various parts of the study could be expanded

through a comprehensive literature review. For this research, the aim of the literature

review is specified which is to enhance the understanding on the overall context of big

data in the construction industry and identify the involvement of big data in the

construction industry. With this, the literature review from the mentioned sources

helps in explaining the background of the research, problem statement and importance

of the study as well as shaping the objectives of the study.

3.2.2 Stage 2: Research Methodology

This stage includes the identification of data collection method or techniques

as well as the instrument used. There are three types of data collection includes a

qualitative research that involves individuals such as in-depth, open-ended interviews,

coordinate perceptions and written documents (Patton, 2005). The data collection for

this study includes both primary and secondary data. The first method used is data

collected from secondary data through desk study. On the other hand, the second data

collection method used is from primary data that focused on the semi-structured

interview with the industry personnel who have experienced big data.

41

3.2.2.1 Desk Study

Desk study method was used to collect the data required for attaining the first

and the second objectives. According to Travis (2016) desk study relied on the

researcher’s skill to review the previous research findings in order to obtain an

expansive comprehension of the study area. This is done by reviewing the secondary

data sourced from past journals, articles, books, dissertations and other related sources

to achieve the objectives. UTM Library Online Database which contained access to

academic journals from Emerald, Science Direct, IEEE Xplore Digital Library and

SpringerLink was searched. The main keywords used in searching the literature were

“big data” and “construction industry”. Desk study is applied in attaining the first and

second objectives. A thorough review was made based on the secondary data that

emphasize the current big data research particularly in the construction industry. This

method was adopted as it provides the fastest and inexpensive method in understanding

the realm of the research, where a thorough review was made to obtain cross-sectional

insights on big data in the construction industry.

3.2.2.2 Semi-structured interview

A series of interviews were conducted to consolidate and validate the insights

that are to be gained from the desk study. The interviews were administered with

personnel who have experienced big data and is aimed to identify the potential

application of big data in construction. According to Rubin and Rubin (2011), the

qualitative interview is a discussion where the researcher aides a conversational

accomplice in a broadened exchange. The interviews allow the researcher to expand

the questions to the extent that they are willing to share. Accordingly, the desk study

is important in this regard as it gives the researcher a gist of the previous research

findings before the interviews are carried out to validate it.

42

Invitations were e-mailed to the respondents that are known to be experienced

with big data to validate the research findings through the desk study. The main

purpose of validation is to ascertain the congruity of the research’s findings with the

opinion of the respondents (Bryman, 2015). The interview is the most suitable method

used in soliciting agreement towards the findings made from the analysis of the desk

study industry as well as in obtaining further suggestion from the respondents. Open-

ended questions were established for capturing pertinent opinion and suggestion from

the research’s outcomes. The interview session takes about thirty minutes to one hour

where questions related to the findings on the direction of big data research area were

asked as well as their further opinion on the potential application of big data in the

construction industry. The details of the interview instrument are as follows:

Table 3.1: Details of interview instrument

No Main Aspects Content

A Big data concept How the concept is being practiced in

the context of the construction industry

B The direction of big data Validation of the research findings

made from desk study

C The potential application of big data Suggestion on the potential application

of big data in the construction industry

Table 3.1 shows the aspects considered for the purpose of validating the

outcomes of from the desk study analysis. The responses through the semi-structured

interview are able to capture the respondents’ views on the research findings presented

as it is interesting to contemplate the industry’s views on the findings obtained and

extending the findings by receiving further recommendations from the respondents.

The result includes what and how would the industry profit from the adoption of big

data.

43

3.2.2.3 Sampling

Mainly, in qualitative research, there are three types of qualitative sampling

which are purposeful sampling, quota sampling, and snowball sampling. Maxwell

(2012) stated that sampling is contemplated to be difficult because usually, sampling

is focusing on the population to be sampled. Hence, a purposeful sampling which also

known as the criterion-based selection is adopted instead of a probability and

convenience sampling.

Purposive sampling is the widely used as sampling strategy where the selection

of contributors is chosen in light of their criteria based on the research questions. One

of the examples of purposive sampling that is suitable to be adopted in this study is

expert sampling. According to Laerd Dissertation (2015), expert sampling is utilized

when a researcher needs to gather information from people with a specific expertise or

knowledge. Hence, this type of sampling was adopted for this study as the targetted

interviewee would be among the personnel who have experienced big data. From this,

the information gathered from the secondary data for the first two objectives were

validated as well as a depth view regarding the topic could be explored in order to

achieve the third objective.

Additionally, snowball sampling was also be applied to this study where

usually this type of sampling is used to assist the researchers in finding the most

eligible interviewee. Snowball sampling allows the initial interviewee to suggest other

individuals who have the potential to conceivably contribute to the research.

44

Figure 3.2: Exponential Non-Discriminative Snowball Sampling

Source: Dudovskiy (2016)

Exponential Non-Discriminative Snowball Sampling is one of the types of

snowball sampling that was used where the initial participants enrolled in the sample

group provides multiple referrals. Then, each new referral will be explored until the

primary data gathered attained the desired amount of samples. Creswell (2005)

proposed that the number of samples for qualitative data between 5 to 25 interviews is

sufficient enough to achieve the objective.

3.2.3 Stage 3: Data Analysis and Findings

Data collected were analysed in accordance to the scope of study by examining

all of the data and information obtained. Analysing and interpreting data is crucial as

it will affect the overall outcome of the study. As for this study, the analysis focused

on analysing and synthesizing the current research of big data application and mapping

its outcome. Then, a wider insight of big data in the construction industry is further

explored through an interview to validate the analyzed data and identify its potential

application. The data were analyzed through a detail analytical study and qualitative

analysis using NVivo software as well as interview content analysis.

45

3.2.3.1 Detail Analytical Study

Detail analytical study is conducted in analysing the literature findings by

following the steps in SALSA framework (an acronym for Search, Appraisal,

Synthesis, and Analysis), an approach introduced by Booth, Sutton, and Papaioannou

(2016). In this process, the review of the searched literature such as journals, articles,

books and other related materials were analysed in light of achieving the first objective.

During literature review, the ideas are being mapped before it was further analysed

and synthesized. According to Hart (1998), the aim of mapping the ideas is to

dynamically reduce the huge amount of information extracted from the analytical

study and highlighted the main point of the argument. For this study, a table is used

to organize the data extracted from previous research related to the application of big

data in the construction industry where it is the research main concerns. It is then

further analyzed where based on Hart (1998), it can be done by distributing the

literature evidence into categories and portray its relationship.

Next, synthesis is part of the analysis process where it is described by Hart

(1998) as the art of forming connections between different components recognized

during the analysis. Synthesis can be classified into aggregative, comparative,

replicative or interpretive. In this study, the aggregative type of synthesis was carried

out as it assist in grouping the information from the analysis and generalize it. For

instance, all of the mentioned application of big data in the construction industry from

the current research were grouped into their area categories.

3.2.3.2 Qualitative Analysis using NVivo Software

Furthermore, Qualitative Analysis software – NVivo was used to map the

outcome of the analysed and synthesised data in order to achieve the second objective.

By using this software, the frequently mentioned area was mapped out through the

46

automating coding with word frequency. In the ‘Word Frequency Query’ command, it

counted the frequency of a particular word or phrase or a set of alternative words

immediately from the analysis. It then was generated and saved as a node. In relation

to the study underpinning this paper, ‘Word Frequency Query’ in NVivo was used to

produce a specified area of big data that has been acknowledged the most in the form

of the word cloud. From this, the predilection of big data in the construction industry

was obtained hence attaining the second objective.

3.2.3.3 Interview Content Analysis

The method used to analyze the primary data obtained from the semi-structured

interview is the interview content analysis method. It is the most common method used

to analyze communication content such as the interview transcript. The content

analysis for the study underpinning this paper is related to the third objective regarding

the potential application of big data in the construction industry. In addition, the

interview content is actually to validate the results obtained from the detail analytical

study and qualitative analysis software. For this study, qualitative analysis software is

used to aid the interview content analysis in producing project map and word cloud.

3.2.4 Stage 4: Conclusion and Recommendation

Last but not least, the research objectives have been attained following the

process of research design. At this stage, the study was evaluated by providing a

rationale wrap up based on the analyzed data as well as suggesting relevant

recommendations for future research.

47

3.3 Chapter Summary

This chapter summarises the process of research methodology in the attempt

of attaining the research objectives. This research is a qualitative research where the

data is collected from both primary and secondary data. The method adopted to collect

these data include the desk study approach and a semi-structured interview. Moreover,

the data collected will be further detailed and analysed in the next chapter through

content analysis method as well as qualitative analysis software.

0

0

CHAPTER 4 DATA ANALYSIS

AND DISCUSSION

48

48

CHAPTER 4

DATA ANALYSIS AND DISCUSSION

4.1 Introduction

This chapter represents the adopted research analysis of this research topic. It

starts with the detail analytical study done following the SALSA (Search, Appraisal,

Synthesis, and Analysis) framework steps and the data is further analysed with the

assist of NVivo software, which managed to achieve the first two research objectives.

The findings were validated through interview content analysis to identify the potential

application of big data in the construction industry.

4.2 Detail Analytical Study

The important concepts on big data excerpted from the review were structurally

analysed by following the steps in the framework known as SALSA where the acronym

stands for Search, Appraisal, Synthesis, and Analysis. A complete application of the

SALSA framework was illustrated in a study by Shamsulhadi, Fadhlin, and Hamimah

(2015) and was further methodologically discussed by Shamsulhadi and Fadhlin

(2016) and Zafira, Shamsulhadi, and Roslan (2018). In the studies mentioned, it was

49

observed that the NVivo software was predominantly deployed to assist in the

analytical process. Part of the analytical outcomes as presented in Table 2 had followed

the processes as outlined by the previous studies and include the usage of the NVivo

software as well. This approach was intentional to maintain the rigour as justified in

the illustrated research. Details of the processes carried out for the study are further

explained in the following sections.

4.2.1 Searching

The exploratory nature of this study had naturally required the researcher to

search the relevant literature concerning big data in construction. For this purpose, the

researcher had first established the search parameter and subsequently drawn the

relevant keywords from the aim and objectives of the study. A snowballing technique

was then exercised where literature was identified through the backward and forward

approaches (Webster & Watson, 2002).

To achieve this, the UTM Library Online Database which contained access to

academic journals from Emerald, Science Direct, IEEE Xplore Digital Library, and

SpringerLink was searched. The main keywords used in searching the literature were

“big data” and “construction industry”. Additionally, the Boolean operators, truncation

characters, and wildcards were also used in selecting the relatable journal articles.

Based on the search results, large numbers of big data articles were displayed from

both constructions as well as other domains. However, the results were again filtered

where only the content that portrays the presence of big data in the construction

industry was of particular interest.

50

4.2.2 Mapping Ideas and Analysis

Mapping involves putting together different strands that make up the topic to

enable analysis and synthesis to be undertaken. The process involves accumulating the

literature content from the review and sorting the list into categories for the purpose of

establishing connections (Hart, 1998). According to Hart (1998), the aim of this

process is to dynamically reduce the huge amount of information extracted from the

review with due emphasized given to extract the main points of the argument. For this

study, a featured map, in a form of a table proposed by Hart (1998) was developed and

showed in Table 2.4. The table showed the results of the analysis which has taken

place by reflecting the words (or terms) derived from the extracted data. These were

reflected as the features which had characterised the literature and a structural form of

recognition of the leading concepts. Despite, at this stage, it appears that the concepts

derived were rather disjointed and had followed the individual reflection from the

sources. This necessitates the next step in the process - synthesis.

4.2.3 Synthesis, Mapping, and Discussion of the outcomes

Concepts that arose from the analysis were synthesized through the aggregative

approach in which the concepts were grouped into relatable themes or area. This

process was carried out by using the NVivo software where apart from its ability in

mapping out the outcome, proved to be useful in espousing the weightage which could

exaggerate a certain number of concepts. The frequently mentioned concepts were

mapped out through the word frequency command. It counts the frequency of a

particular word or phrase or a set of alternative words fed from the analysis. In relation

to this study, the ‘Word Frequency Query’ in NVivo was used to reveal a specified

concept of big data that have been mentioned the most in the form of the word cloud.

Hence, the predilections of big data in construction were obtained thus attaining the

second objective.

51

51

Figure 4.1: Generated model representing the frequency of big data research area

1 5

2

3

4

52

52

A model which was developed from the synthesis is presented in Figure 4.1. It

shows that prior research on big data in construction had centered around

‘management’ which contributed to the area of ‘project’ management, ‘energy’

management and ‘resource’ management. Based on the discussion, the theoretical

orientations obtain from the analytical processes could be summarised in sequence as

(1) project management; (2) safety; (3) energy management; (4) decision making

design framework and (5) resource management. Table 3 recapitulates the

interpretative context of the most frequent big data research area in relation to the

findings previously presented in Table 4.1.

Table 4.1: Detailed context of big data research area

The context of big

data research area

Important

keywords

Detail of research area

Construction Project

Management

Monitoring Progress/performance monitoring

through IoT devices

time, cost Better time and cost management

Decision-

making

Making decision using predictive data

that leads to lower project risk and better

management for improved productivity

Safety Site safety,

workers’ safety behaviour

Big data generated through IoT devices

in tracking and visualize site safety

conditions as well as workers’ behaviour towards safety

Energy management Consumption,

building

performance

Enhancing energy efficiency and

building performance through an

understanding of building energy

consumption

Decision-making

design framework

Decision-

making

Big data for prompt and informed

decision-making

Resource management Resources

tracking

Resources tracking through IoT devices

to improve resources utilization

efficiency

Big data in ‘project management’ involves those linked-construction data in

cloud base that provides broad understanding of a complex project. It was submitted

that big data leads to a better ‘project management’, especially in ensuring that cost

53

efficiency was achieved as well as minimizing delays. Likewise, big data initiated by

the IoT devices such as drones, sensors or smartphones aid in recording construction

work progress and monitoring work performance. It was postulated that a real-time

data was able to be provided so that actionable actions could be taken in enhancing the

project productivity. Big data also contribute to a better project management through

data wise enhancing ‘decision-making’ process especially in predicting the project

orientation that leads to lower project risk as well as improved productivity.

Additionally, the IoT devices also generate data on the ‘safety’ aspect such as

workers’ safety behaviour on site and site safety conditions through sensors, automated

equipment, tracking devices as well as visualization technologies. Big data application

towards safety is mainly focusing on enhancing safety behaviour understanding in

real-time so that the data could be analysed for immediate actions to be taken hence

improving the site safety condition.

On the other hand, ‘energy management’ encompasses the integration of IoT

or BIM with big data analytics in understanding the building energy consumption to

increase energy efficiency and add to building performance. Energy analyses further

assist in decision making ‘design’ framework where the results could be the

determinant in generating integrated models for building design. Also, big data provide

an aerial view on all aspects of the built environment that facilitates a better decision-

making design framework.

Correspondingly, resources tracking and monitoring through sensors or mobile

apps helped to enhance the decision-making for ‘resources management’ and ensure

resource optimization. Other big data potential application reviewed from the literature

includes construction waste management as well as data-sharing efficiency to improve

communication.

54

4.4 Validation of the Findings

This sub-section deliberate on the validation towards the results developed

from the NVivo software on the direction of big data in the construction industry. It

features the respondents who have participated in the semi-structured interview

session to discuss the findings. This validation process was carried out to identify the

appropriateness of findings as a useful basis for a further recommendation on the

potential application of big data in the construction industry. The semi-structured

interviews were conducted to contemplate the industry’s views on the findings

obtained and extending the findings by receiving further recommendations from the

respondents.

4.5 Respondents’ Background

The interviews to validate the findings and obtaining further suggestion on the

potential application of big data in the construction industry were conducted among

the personnel that has experience with big data as well as the construction industry

personnel that has an extended knowledge on big data. Invitations were sent out to 16

potential interviewees that comes from Malaysia, Singapore, and Hong Kong with the

aim to gather information on the potential application of big data in the construction

industry through their perspectives. Out of the 16 invitations sent, only 5 respondents

were agreed to be interviewed mainly from Malaysia. All of the respondents has

reached 10 years and above of working experience except for RV2 with the working

experience of 8 years and all of them are known to be familiar with the concept of big

data through the emerging technologies. Table 4.2 shows the background of the 5

respondents that were being interviewed.

55

55

Table 4.2: Respondents’ Background

No Organization

/Company Position

Working

Experience

(Years)

Knowledge/Experience on Big Data

RV1

Department of Statistics

Malaysia

(DOSM)

Director, Industrial Production and

Construction Statistics

Division

17

Respondent RV1 has involved in one big data project called price intelligence where it is a web

scraping project in collecting price data from online businesses to improve the consumers’ price index. This project is still on-going and has started since 2014. RV1 thinks that the construction

data collected by DOSM can be utilized for extensive usage and the opinions given is based on the experience obtained from the involvement with the big data project.

RV2 Setia Precast

Sdn Bhd

Senior Manager

(Design) 8

Respondent RV2 has well-familiarized with the technologies and trends in the construction industry that causes the emerging of big data but does not aware that the data involved is called big data. Respondent also shared that he has been to several conference and knowledge sharing on the application of technologies in the construction industry thus the opinions given were based

on the knowledge he obtained from the session.

RV3

Ministry of

Works, Malaysia

Chief Assistant,

Contractors and Entrepreneur

Development Division

11

Respondent RV3 has moderate knowledge on big data and is highly aware of the emerging trends

in the construction industry that causes the spread of big data. Currently respondent RV3 with his team is trying to propose the extensive usage of drone on construction site as well as establishing

a platform to assist the selection of the best contractor for construction projects.

RV4 Public Private Partnership

Unit

Principal Assistant Director, Quantity

Surveyor Department

16

Respondent RV4 has moderate knowledge on big data and is highly aware of the emerging trends in the construction industry that causes the spread of big data. She had involved in the project that utilize the usage of visual cameras on site for project monitoring. Her opinions on big data in the

construction industry is based on her knowledge and experience with the emerging technologies in the industry.

RV5

Malaysia Productivity

Corporation (MPC)

Director, Productivity & Competitiveness Development

28

Respondent RV5 has involved in several conference on big data by giving talk on the possibility of having big data in Malaysia. RV5 also made survey on the openness of data sharing by different

organizations as she described open data as big data. Her main focus of big data in the construction industry is on how big data would be useful in improving the industry’s productivity.

56

56

4.6 Interview Findings

Questions mainly on the concept of big data in the construction industry,

validation to the direction of big data research gained from the detail analytical study

as well as the potential application of big data in the construction industry were asked

to the respective respondents. The questions focused on their interpretations of the

findings obtained on the study of big data in the construction industry as well as their

extended suggestions on its potential application. The responses have been recorded

in tables and were analysed to generate the word cloud and project map with the assist

of NVivo software.

4.6.1 Concept of big data in the context of the construction industry

The respondents were asked about how the 3V’s concept of big data which is

Volume, Velocity, and Variety is related to the data involved in the construction

industry. Table 4.3 shows the responses given.

Table 4.3: Respondents’ responses towards the big data concept

Respondent Big data concept in the context of the construction industry

RV1 Traditionally, data is being collected by doing survey whereas

today’s advanced technology especially the advent of the Internet

of Things (IoT) enables us to gather the data in real-time and

utilize it at an instant. Big data comprises two types of data which

are structured and unstructured data. Data involved in every

industry, as well as the construction industry, are in big volume

and it comes in these both form thus reflecting the big data concept.

However, the utilisation of unstructured data needs to be revised

carefully as to ensure the reliability of the data.

57

Respondent Big data concept in the context of the construction industry

RV2 Generally, the advent of big data in the construction industry is

supported by the usage of BIM in a construction project as BIM

gather all relevant data or information from various sources

regarding the project onto one platform thus shows the big volume

and variety of data gathered for a project. The reachability of the

same data among different stakeholders also shows the big data

concept of velocity. However, the slow internet speed may

influence its velocity.

RV3 Knowingly, the construction industry had to deal with the

voluminous amount of data which comes from different sources

and the data is actually needed for various uses especially towards

improving the efficiency of the industry. However, it seems that the

level of data integration or sharing is low as all of the data available

was usually siloed thus resulting in low accuracy of data and data-

driven decision making.

RV4 Basically, construction data is huge in terms of its volume and

undoubtedly the data is generated from various different

sources. However, the data is not synchronized as it is siloed and

does not integrate with each other. The emerging of BIM adoption

does help in enhancing the data integration during construction

planning and big data technology is seen to be the next tool to

encourage better use of construction data.

RV5 The concept of big data is based on the availability of the data

over the net as well as the technology or other sources that

generate data where it can be excerpt by everyone at an instant.

The high velocity and real-time data created indicate that the big

data involved both structured and unstructured data. Thus, this

arouses the concern on the certainty of the data whether it can be

used or not.

The respondents’ answers were exported to NVivo software and a project map

on the big data concept in the context of the construction industry is generated to

summarise the responses. The illustration can be seen in Figure 4.2.

58

Figure 4.2: Generated model representing the frequency of big data concept by

respondents

Figure 4.3: NVivo project map on big data concept in the context of the construction

industry

Figure 4.3 summarised the responses given in Table 4.3 in project map from

the Nvivo software. Based on the summarisation, the respondents agreed that the 3Vs

concept of big data which is Volume, Velocity, and Variety reflects the data associated

59

with the construction industry. This can be seen from Table 4.3 and Figure 4.2 that

most of the respondents mentioned on the voluminous data that is available in the

construction industry, the diversity of data sources and forms which include structured

and unstructured data as well as the rapidity of the data generated and be reached in

real-time. RV1 and RV5 added the fourth V of big data which is Veracity whereas can

be seen in Table 4.3 and Figure 4.3, it is due to the increased volume of unstructured

data that drove the concern towards reliability and certainty of the data. Some of the

respondents also mentioned on the emerging trends in the construction industry that

pushes the initiation of big data into the industry such as Building Information

Modelling (BIM) and the Internet of Things (IoT). This hence validated the findings

made based on a literature review that big data in the construction industry is triggered

by the emerging trends listed in Chapter 2 sub-heading 2.6.

4.6.2 Orientation of big data in the construction industry

The respondents were asked to validate the big data research orientation in the

construction industry based on the theoretical orientation discovered from the review

process with the assist of NVivo software. Table 4.4 presents the responses given.

Table 4.4: Respondents’ responses towards the big data orientation

Respondent Orientation of big data in the construction industry

RV1 Respondent shared that the implementation of big data in the

construction industry particularly in the developed countries is

directed towards the usage of IoT devices especially sensors to

monitor the performance of the construction process. Others

include the machinery and equipment tracking to enhance its

efficiencies usage on site. Thus, the respondent agreed that the

orientation of big data in the construction industry is centered on

construction project management particularly during the

construction stage. Also, respondent thinks that the orientation of

big data in the construction industry could be projected towards

decision making to improve time and cost of a project.

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Respondent Orientation of big data in the construction industry

RV2 Respondent thinks that the area of big data in the construction

industry discovered from the review process has been well covered

and agreed that the focused area of big data is towards

construction project management as the way a project is being

managed will determine the success of a project. Also, the

respondent mentioned that the direction of big data could also lead

towards safety as well as a decision making design framework.

The respondent elaborate that the emerging of BIM has encouraged

the application of big data for a better design decision.

RV3 Respondent feels that it is good to direct the big data research

towards construction project management especially on project

monitoring. Basically, the current system that the government had

on project monitoring is in the form of manual supervision and

reports resulting in a slow extraction of the data and its value.

Therefore, the government has always looked up to enhance the

project monitoring system for better project management,

particularly for government projects. Other than that, the future

direction could also move toward resource management in terms

of faster delivery of materials to the site.

RV4 Big data undoubtedly have more potential towards construction

project management. In fact, respondent shared on the projects

that the respondent involved where cameras are installed on site for

the upper management to monitor the work progress and to use the

information for further action in enhancing the construction

progress. Respondent also suggested that the direction could also

be projected towards safety as it is an important aspect to be taken

care of in every construction projects.

RV5 Decision making on project management is the direction

highlighted by the respondent based on the theoretical orientation

discovered. The respondent added that big data for decision making

could aid in enhancing the productivity of construction industry

particularly through a decision made for cost and time

management. Besides that, the resource management could also

lead the orientation of big data in terms of improving the process

of material delivery as one of the factors causes the low

productivity of the construction industry is a long waiting-time for

materials and supplies delivery.

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Figure 4.4: NVivo project map on big data orientation in the construction industry

The project map in Figure 4.4 summarised the responses made by the

respondents on the big data orientation in the construction industry. This includes

construction project management on decision making and project monitoring,

decision-making design framework, resource management and safety. The responses

were also being analysed with the assist of NVivo software to illustrate the direction

of big data in the construction industry based on the respondents’ sequence. Word

frequency function in the NVivo software was used again to find the projection in the

form of the word cloud. The results can be seen in Figure 4.5.

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Figure 4.5: Generated model representing the frequency of big data

orientation by respondents

Table 4.5: Summarisation of respondents’ responses towards big data orientation

Big data orientation Respondents

RV1 RV2 RV3 RV4 RV5

Construction Project

Management

Project monitoring

Decision-making

Safety

Energy management

Decision-making design framework

Resource management

From the word cloud in Figure 4.5 and Table 4.5, it can be seen that ‘project

management’ appeared more which means it is the most mentioned direction by the

respondents. This hence validates the theoretical findings made on the big data

research orientation that construction project management is leading compared to

another area. Additionally, the respondents supported their justification on the big data

orientation by sharing their knowledge as well as experiences on the current big data

application in that area which has been stated in Table 4.4. Respondent RV2 also

11

12

33

14

63

mentioned that the core for a particular project to success is based on how the project

is being managed thus the potential application of big data in the construction project

management need to be explored in harnessing its further benefits.

Moreover, as portrayed in Figure 4.5 and Table 4.5, it can be concluded that

the respondents’ view on the big data orientation in sequence is towards (1)

construction project management, (2) resource management, (3) safety and (4)

decision-making design framework. However, none of the respondents responded in

the direction of big data in the construction industry towards energy management. This

contrast with the theoretical orientation discovered based on the big data research made

where energy management is the third focused area of big data research. Also, based

on the theoretical orientation, big data research is not really centered on resource

management. However, from the interviews made, respondents suggested that further

attention should also be given towards resource management besides construction

project management.

4.6.3 Potential application of big data in the construction industry

The respondents were asked about the potentiality of big data to be applied in

the construction industry prior to suggesting its potential application. All of the

respondents answered that big data has the potential in construction but it may be

challenged by few factors such as budget as well as lack of knowledge and skill in big

data handling. Next, the respondents give recommendations on the potential

application where the suggestions made were based on the respondents’ experiences

and knowledge on big data. The responses on the potential applications highlighted by

the respondents were summarised and illustrated with the assist of NVivo software and

four categories were encapsulated as in Figure 4.6 and Table 4.6 which are (1) Big

data application through IoT devices; (2) Predictive model for decision making; (3)

Pricing system; and (4) Contractors’ information system.

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64

Figure 4.6: NVivo project map on the potential application of big data in the construction industry

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65

Table 4.6: Suggested potential application of big data in the construction industry

Potential application of big data Respondents

RV1 RV2 RV3 RV4 RV5

Big data application through IoT devices for;

Project progress

Machinery control

Materials delivery

Safety

Workers’ productivity

A predictive model for decision making on;

Project planning through past project data

Construction type through buyers’ demand

for property

Pricing system for;

Pricing evaluation at the bidding stage

Construction cost decision

Contractors’ information platform for;

Performance assessment

Contractor selection process

Based on the project map in Figure 4.6 and Table 4.6, it can be seen that the

application of big data in the construction industry was propagated by the emerging of

IoT devices as the majority of the suggested potential application of big data outlined

by the respondents are through IoT devices. Next, the potential application of big data

in the construction industry suggested by the respondents were then be related back to

the theoretical orientation of big data research discovered in Table 4.1. The correlation

can be seen in Figure 4.7 and Table 4.7.

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66

Figure 4.7: Correlation between suggested potential applications with the theoretical big data orientation

67

67

Table 4.7: Correlation between the suggested potential applications with the

theoretical big data orientation

Potential application of big data

Theoretical orientation

Con

stru

ctio

n P

roje

ct

Man

agem

ent

Sa

fety

En

ergy m

an

agem

ent

Dec

isio

n-m

ak

ing

des

ign

fra

mew

ork

Res

ou

rce

man

agem

ent

Big data application through IoT devices for;

Project progress

Machinery control

Materials delivery

Safety

Workers’ productivity

A predictive model for decision making on;

Project planning through past project data

Construction type through buyers’ demand for property

Pricing system for; Pricing evaluation at the bidding stage

Construction cost decision

Contractors’ information platform for;

Performance assessment

Contractor selection process

Based on the correlation, it can be seen that the application of big data in the

construction industry appeared most around the construction project management area

thus reaffirm the theoretical orientation obtained from the literature review process

where construction project management is the most intensified area for big data in the

construction industry. The elaboration on the correlation between the suggested

potential applications of big data in the construction industry by the respondents with

the theoretical orientation discovered from the literature review process can be further

reviewed as in following paragraphs.

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i. Construction project management

The potential application of big data in the construction industry on

construction project management especially for project monitoring is mainly driven

by the Internet of Things devices such as drones, visual camera, and other similar

devices. The application of these devices could provide a real-time data needed for a

better management in construction work progress which resulted in minimizing delays.

Respondent RV3 suggested on the extended employment of drones beyond the current

utilisation which is to capture work progress photo. Instead, according to respondent

RV3, the drones could be exploited to visualize the real-time site condition and

connecting it with the upper management or client so that they can envisage immediate

actions to be taken to improve the construction work progress. This would extend the

traditional way of progress work reporting that resulted to and low project management

and discourage significant improvement to be made. Additionally, respondent RV4

suggested the use of the visual camera on construction site for project monitoring

where the function is similar to drones. Conversely, according to respondent RV3, the

adoption of drones would give wider views on the construction site compared to the

use of the visual camera. Nevertheless, respondent RV3 added despite the high cost,

the application of drones may require several procedures and approval from the

authority before it can be adopted.

Other than that, respondent RV1 and RV5 also suggested developing a pricing

system where it could help in the bidding process. Respondent RV1 suggested that the

pricing system could be developed where all prices such as for materials, labours and

other construction cost-related could be logged and accessed in real-time. The data

could be then utilised for price-related decision making. Respondent RV4

recommended the pricing system as to set a price benchmark so that all contractors

will price in accordance to the specified price on the system. This will further evade

the case of underpriced and overpriced by the contractors. Furthermore, the

respondents viewed the system as an assistant to speed up and facilitate the process of

evaluating the contractor’s bid compared to the traditional way of evaluating the

process. Also, respondent RV1 and RV4 think that with the help of the data from the

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system, the updated cost-related data could assist on the decision making for better

budget determination and improved cost estimation made for a particular project hence

ensure that the construction cost will not exceed beyond its actual cost. This is related

to the construction project management in terms of cost management as besides time,

the cost is the most crucial aspect to be monitored throughout the project period.

Furthermore, the suggested potential application by respondent RV3 on

developing a contractors’ information system is generally to assist the client in

assessing the contractors’ performance for them to choose the best contractor.

According to respondent RV3, with the support of this system, the process of

contractors’ selection could become more efficient by reducing the time taken to

evaluate the contractors as in a conventional way. This is related to the construction

project management where by selecting the best contractor with excellent performance

for a particular project would affect the whole construction process as well as may

contribute to good project management.

ii. Resource management

The emerging of the Internet of Things has also initiated big data for resource

management. The respondent suggested that benefits of IoT devices in generating big

data could be harnessed to improve the delivery process of materials. Respondent RV3

and RV5 proposed that it can be done through the utilisation of geo-tagging and

sensors technology to produce a real-time data to the contractors to monitor the

movement of the materials to be delivered to the site. From this, the contractors can

monitor the process of materials delivered to the site through the real-time data

generated from the IoT devices. The data can also be used for contractors to detect any

problems and take action to improve the delivery process. Respondents viewed the

application will evade delayed in materials delivery and thus avoid any interruptions

to the construction works progress. Respondent RV5 also added that enhanced delivery

materials process will result in a better resource management and indirectly improve

the productivity.

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Also, the application of these devices on machinery and equipment could

enhance the efficiency of construction works need to be done by the machinery or

equipment. For instance, respondent RV2 proposed the utilisation of geo-tagging and

sensors technology that is installed on the machinery and setting out the work need to

be done by the machinery by keying required data to the control device. The data input

will enable remote control of the machinery or equipment to carry out the construction

activity. Other than that, respondent RV2 also perceive wearable devices as a

mechanism to improve the management of the workers’ activity. This can be done

through the real-time data provided by the devices on the workers’ whereabouts and

activities. The data could be extracted and analysed for a further actionable solution to

be made in improving the workers’ productivity. From the big data application

proposed for resource management area where most of it is by the employment of IoT

devices, it reaffirms the literature review made on the current era of big data (Big Data

3.0) where big data is driven mainly by the Internet of Things applications.

iii. Safety

Based on the respondents’ responses, the application of big data in the

construction safety area is also initiated by the IoT devices. For instance, despite

project monitoring, respondent RV3 and RV4 suggested that drones and visual

cameras could also be used to monitor the safety condition of a particular site. In this

case, these devices will provide a real-time data on the safety aspects especially on the

safety behaviour of the workers so that an immediate action to prevent or rectify the

safety-related problems on site could be taken. Similar to the workers’ activity tracking

via wearable devices, the respondent suggested that the use of the device could also

resort to the workers’ safety behaviour monitoring where it will facilitate the reporting

process of unsafe acts made by the workers thus appropriate action could be taken

immediately.

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iv. Decision-making design framework

For this area, respondent RV2 mentioned on the potential in developing a

predictive model to assist the decision making for upcoming projects in terms of design

or other related decision to be made prior to the project commencement. The predictive

model could be structured based on the data from previous projects gathered on a

single platform and utilise it for decision making in project planning and management.

The decision taken based on the predictive model could prevent any repeatable

mistakes made and thus lowering the project risk. Additionally, respondent RV2 and

RV5 proposed that the predictive model could also be developed for decision making

on the type of construction to be built. This can be done by collecting information or

data regarding buyers’ trend of demand on the property where according to respondent

RV5, this kind of information include those data from the social networking services

thus makes the information be categorised under big data. The data may comprise of

preferable design or material, price and other related information that reflects the

buyers’ property requirements where it indirectly will affect the type of development

or building construction to be proposed.

Other than that, respondent RV5’s extended view on the pricing system

proposed is that it could also assist in decision-making for cost-efficient building

design. The respondent thinks that from the system, a better decision on materials

selection with an equitable price could be made. This would further contribute to a

cost-efficient building design produced.

4.7 Chapter Summary

This chapter analysed the overall findings on the current extent of big data

research in the construction industry as well as its orientation. Generally, the findings

were being analysed based on the literature review process called SALSA framework

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which stands for Search, Appraisal, Synthesis, and Analysis and with the assist of

NVivo software. Overall, based on the analysed made using the word frequency

function in the NVivo software, it could be seen that the sequence of big data

orientation in the construction industry is as follows; (1) Construction project

management; (2) Safety; (3) Energy management; (4) Decision-making design

framework; and (5) Resource management.

This chapter also analysed the interview content to validate the theoretical

orientation and seeking recommendations on the potential application of big data in

the construction industry. Throughout the analysis, the findings show that most of the

respondents agreed that construction project management is the most focused area for

the big data orientation in the construction industry. Also, the recommendations made

on the potential application of big data in the construction industry reflects that the

application propagated more in the construction project management area thus

validating the theoretical orientation obtained.

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0

CHAPTER 5 CONCLUSION AND

RECOMMENDATION

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CHAPTER 5

CONCLUSION AND RECOMMENDATION

5.1 Introduction

This chapter explains the conclusion of the research based on the three

objectives mentioned in earlier chapters, which is to analyse the current extent of big

data research in the construction industry, to map out the orientation of big data

research in construction as well as to identify the potential application of big data in

the construction industry. This chapter also discusses the problems encountered

throughout the research process and the recommendations for future research.

5.2 Achievements of Research Objectives

Based on the analysis made, it can be concluded that the research objectives

presented in the early study were achieved. The summarisation can be seen in

following paragraphs.

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Objective 1 & 2: The current extent of big data research and its orientation in

the construction industry

As the foregoing discussions have shown, a structured analytical framework

has been employed to analyse the resources obtained, assisted by the use of NVivo

software. This has permitted a wider inclusion of resources, thus had broadened the

base for the qualitative analysis to take place. Based on the findings made from the

study, the current extent and orientation of the present construction big data research

cover a diverse research area. The study had suggested that the current direction of

construction big data research could be translated into five specific areas which are

(1) Construction project management; (2) Safety; (3) Energy management; (4)

Decision making design framework; and (5) Resource management. Of the five areas

mentioned, big data for construction project management was identified as the area

which research is really intensified especially on project monitoring, progress tracking

and decision making to enhance cost and time management. This follows as the

construction industry is a data-dependent industry hence data must be managed

efficiently with the right tool to ensure the success of a project.

Additionally, a semi-structured interview was conducted to validate the

theoretical orientation findings. From the responses, it can be concluded that most of

the respondents agreed that the big data orientation is cast on construction project

management notably on project monitoring and decision-making. With the aid of

NVivo software, the interview content on the big data orientation was further analysed

to find the sequence of the most frequently mentioned area by the respondents. The

findings concluded that the orientation of big data based on the respondents’ responses

is slightly differed which are (1) Construction project management; (2) Resource

management; (3) Safety; and (4) Decision-making design framework. Construction

project management is still being emphasized by the respondents in the big data

projection. However, the respondents also suggested that the orientation on resource

management should also be intensified as resource management is closely related to

the construction project management. In order for a particular project to excel in its

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management, resources such as materials, machinery and equipment, labours and other

resources also need to be well-managed. This is due to poor resources management

could affect the planned cost and time for the projects thus lead to low productivity.

Objective 3: Potential application of big data in the construction industry

The potential application of big data in the construction industry was obtained

from the semi-structured interview where the interviewees were asked to give their

extended views or suggestions on the big data potential application in construction.

The responses given were analysed and summarised with the assist of project map

function on the NVivo software. Based on the analysis, the potential application could

be categorised into four main themes which are (1) Big data application through IoT

devices; (2) Predictive model for decision making; (3) Pricing system; and (4)

Contractors’ information system. The potential application is suggested by the

respondents based on their experiences and knowledge on big data. Table 5.1

recapitulates the interpretative context of the recommended potential application of

big data mentioned by the respondents.

Table 5.1: Detailed context of the suggested potential application

Suggested potential

application

Details

Big data application

through IoT devices

Drones and visual camera for project and site safety

condition monitoring

Geo-tagging and sensors for resource management such

as the improved delivery process of materials and remote

control of machinery

Wearable devices to track workers’ activities as well as their safety behaviour

A predictive model

for decision-making

Data and information from past projects used for decision

making during project planning and management

Information on buyers’ property demand to assist in decision-making for the type of

construction/development

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Suggested potential

application

Details

Pricing system

To assist in tender price evaluation during the bidding

stage

To improve the construction cost decision made

Contractors’ information platform

To asses contractors’ performance thus facilitate the contractors’ selection process

Besides that, the suggested potential application was also being interrelated

back to the theoretical orientation made and based on the correlation, it can be seen

that the application of big data in the construction industry appeared most around the

construction project management area thus reaffirm the theoretical findings obtained

on the big data orientation from the literature review process. Apparently, this suggests

the rapid pace of big data development in construction and the on-going interest to

harness the technology for common good.

Recommendation:

The findings have given an insight on the potential application of big data in

the construction industry on respective areas. However, the adoption of big data in the

construction industry is slower compared to other industries and according to the

respondents, it is due to the unwillingness of the industry to invest in it and poor data

management. Thus, the following recommendation to push the adoption of big data is

made:

i. The government should find a way to bring the technology with the

lower cost so that the market could adopt the technology. Other than

that, the budget should be allocated to giving incentives to the company

that utilises technology in their project.

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ii. Develop talents with skill and knowledge on handling big data and

analytical capability in analysing the data. This could impulse the

adoption of the potential application of big data and support the

development of big data for the construction industry.

5.3 Research Limitations

Throughout the research process, there were few limitations encountered

upon completing it. The limitations were listed as follows:

i. The current extent of big data research could be explored further but due to the

time constraint, only those mentioned in Table 2.4 was being analysed to obtain

research objective 2 which is the orientation of big data in the construction

industry.

ii. Identifying suitable interviewees was a challenge as it is difficult to recognise

the personnel who had experienced and extended knowledge of big data as big

data is the new face in the construction industry.

iii. As most of the respondents identified were holding a high position in the

particular organisations, thus it is quite difficult to make appointments with

them. Also, due to their busyness and time constraint, only 5 respondents were

managed to be interviewed.

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iv. The approach has been made to obtain extensive views from the respondents

of another country that has been adopting big data such as Singapore and Hong

Kong. However, none of them were willing or free to be interviewed.

5.4 Recommendations for future research

The study has managed to draw important insights on the specific areas in

construction big data research. These were achieved through the accomplishment of

the following objectives: (1) to analyse the current extent of construction big data

research; (2) to map out the orientation of the current construction big data research;

and (3) to identify the potential application of big data in the construction industry. As

the study has shown, construction big data research offers a potentially good prospect

to improve the industry. It is a step ahead of the current digitalisation effort and brings

a new wave in obtaining insights from the voluminous amount of data. Thus it is

recommended for a study to be conducted on;

i. The challenges impeding the adoption of big data in the construction

industry. This would provide an indication on the challenges need to be

addressed by the industry in order to harness the benefits of big data adoption.

ii. The construction industry’s readiness in embracing to big data wave. This

effort shall increase the depth and breadth of the current knowledge which

could further bolster the industry’s understanding of big data.

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REFERENCES

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REFERENCES

Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-

chain management: A literature review. Computers & Industrial Engineering,

101, 528-543.

Akbar, S. (2017). Is Big Data The Most Trending Thing In Construction Now.

Retrieved from https://geniebelt.com/blog/big-data-in-construction

Akhavian, R., & Behzadan, A. H. (2015). Construction Equipment Activity

Recognition for Simulation Input Modeling using Mobile Sensors and Machine

Learning Classifiers. Advanced Engineering Informatics, 29(4), 867-877.

doi:10.1016/j.aei.2015.03.001

Aouad, G., Kagioglou, M., Cooper, R., Hinks, J., & Sexton, M. (1999). Technology

management of IT in construction: a driver or an enabler? Logistics

Information Management, 12(1/2), 130-137.

Augur, H. (2016). Big Data is Transforming Commercial Construction. Retrieved

from http://dataconomy.com/2016/07/big-data-is-transforming-commercial-

construction/

Ayers, R. (2017). 5 Ways the Healthcare Industry Could Use Big Data - and Why It's

Not.

Azhar, S. (2011). Building information modeling (BIM): Trends, benefits, risks, and

challenges for the AEC industry. Leadership and management in engineering,

11(3), 241-252.

Azzeddine Oudjehane, & Moeini, S. (2017). Big Data in Construction Projects Risk

and Opportunity Management.

Barista, D. (2014). The Big Data revolution: How data-driven design is transforming

project planning. Building Design+Construction.

https://geniebelt.com/blog/big-data-in-construction

http://dataconomy.com/2016/07/big-data-is-transforming-commercial-construction/


80

Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent

achievements and new challenges. Information Fusion, 28, 45-59.

Bernstein, P. (2017). How Big Data and VR in Architecture Will Greatly Improve

Design. Retrieved from https://www.autodesk.com/redshift/big-data-vr-in-

architecture/

Bilal, M., Oyedele, L. O., Akinade, O. O., Ajayi, S. O., Alaka, H. A., Owolabi, H. A.,

. . . Bello, S. A. (2016). Big data architecture for construction waste analytics

(CWA): A conceptual framework. Journal of Building Engineering, 6, 144-

156.

Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., . . . Pasha,

M. (2016). Big Data in the construction industry: A review of present status,

opportunities, and future trends. Advanced Engineering Informatics, 30(3),

500-521. doi:10.1016/j.aei.2016.07.001

Bleby, M. (2015). Big Data boosts construction. Retrieved from

http://www.afr.com/real-estate/commercial/big-data-boosts-constrcution-

20150109-12l8xx

Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a

successful literature review: Sage.

Bryman, A. (2008). Mixed methods research: combining quantitative and qualitative

research. Social research methods, 3, 608-626.

Bryman, A. (2015). Social research methods: Oxford university press.

Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten

tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75-86.

https://www.autodesk.com/redshift/big-data-vr-in-architecture/


http://www.afr.com/real-estate/commercial/big-data-boosts-constrcution-20150109-12l8xx


81

Burger, R. (2017). How the Construction Industry is Using Big Data. Retrieved from

https://www.thebalance.com/how-the-construction-industry-is-using-big-

data-845322

Caron, F. (2015). Data management in project planning and control. International

Journal of Data Science, 1(1), 42-57.

Chen, C., & Cook, D. J. (2012). Behavior-Based Home Energy Prediction. IEEE

Internet of Things Journal.

Chen, C. P., & Zhang, C.-Y. (2014). Data-intensive applications, challenges,

techniques and technologies: A survey on Big Data. Information Sciences, 275,

314-347.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics:

From big data to big impact. MIS quarterly, 36(4).

Choudhry, H., Mohammad, A., Tan, K. T., & Ward, R. (2016). The next frontier for

digital technologies in oil and gas. Retrieved from

https://www.mckinsey.com/industries/oil-and-gas/our-insights/the-next-

frontier-for-digital-technologies-in-oil-and-gas

Cormode, G., & Krishnamurthy, B. (2008). Key differences between Web 1.0 and

Web 2.0. First Monday, 13(6).

Creswell, J. (2005). Planning, conducting, and evaluating quantitative and qualitative

research: Educational research. In: Prentice Hall: Upper Saddle River, NJ

Creswell, JW (2007). Research method: Qualitative, quantitative and mixed

methods approaches (2nd ed.). Thousand Oaks, CA: Sage.

Cukier, K. (2010). Data, data everywhere: A special report on managing information:

Economist Newspaper.

https://www.thebalance.com/how-the-construction-industry-is-using-big-data-845322


https://www.mckinsey.com/industries/oil-and-gas/our-insights/the-next-frontier-for-digital-technologies-in-oil-and-gas


82

Curry, E., O’Donnell, J., Corry, E., Hasan, S., Keane, M., & O’Riain, S. (2013).

Linking building data in the cloud: Integrating cross-domain building data

using linked data. ADVANCED ENGINEERING INFORMATICS, 27(2), 206-

219.

Davis, D. (2015). How Big Data is Transforming Architecture.

Davis, K. (2012). Ethics of Big Data: Balancing risk and innovation: " O'Reilly Media,

Inc.".

Dissertation, L. (2015). Purposive Sampling. In: Retrieved November.

Dudovskiy, J. (2016). The Ultimate Guide to Writing a Dissertation in Business

Studies: A step by-step assistance. Research-methodology. net.

Eadie, R., Browne, M., Odeyinka, H., McKeown, C., & McNiff, S. (2013). BIM

implementation throughout the UK construction project lifecycle: An analysis.

Automation in Construction, 36, 145-151.

Elo, S., & Kyngas, H. (2008). The qualitative content analysis process. Journal of

Advanced Nursing, 62(1), 107-115.

Elragal, A. (2014). ERP and Big Data: The Inept Couple. Procedia Technology,

16(Supplement C), 242-249. doi:https://doi.org/10.1016/j.protcy.2014.10.089

Emani, C. K., Cullot, N., & Nicolle, C. (2015). Understandable big data: A survey.

Computer science review, 17, 70-81.

Faure, A. (2016). Big data is revolutionizing business: the construction industry in

general and building sites in particular. Retrieved from

https://blog.serenacapital.com/big-data-is-revolutionizing-business-the-

construction-industry-in-general-and-building-sites-in-de7a53a20516

https://doi.org/10.1016/j.protcy.2014.10.089

https://blog.serenacapital.com/big-data-is-revolutionizing-business-the-construction-industry-in-general-and-building-sites-in-de7a53a20516


83

Fellows, R., & Liu, A. (2008). Research Methods for Construction (3rd. ed.). Oxford:

Wiley-Blackwell.

Ferkoun, M. (2014). Cloud Computing and Big Data: An ideal combination. Retrieved

from https://www.ibm.com/blogs/cloud-computing/2014/02/cloud-

computing-and-big-data-an-ideal-combination/

Feschyn, A. (2017). THE IMPACT OF BIG DATA ON BANKING AND

FINANCIAL SYSTEMS. Retrieved from

http://dataconomy.com/2017/07/big-data-banking-financial-systems/

Gaitho, M. (2017). How Applications of Big Data Drive Industries. Retrieved from

https://www.simplilearn.com/big-data-applications-in-industries-article

Gartner. (2015). 6.4 billion connected “Things” will be in use in 2016, up 30 percent

from 2015. Gart. Inc.

Gartner, I. (2014). Glossary.(2013). Big Data.

Guo, S., Ding, L., Luo, H., & Jiang, X. (2016). A Big-Data-based platform of workers’

behavior: Observations from the field. Accident Analysis & Prevention, 93,

299-309.

Guo, S., Luo, H., & Yong, L. (2015). A big data-based workers behavior observation

in China metro construction. Procedia Engineering, 123, 190-197.

Hafiz, A., Lukumon, O., Muhammad, B., Olugbenga, A., Hakeem, O., & Saheed, A.

(2015). Bankruptcy prediction of construction businesses: towards a big data

analytics approach. Paper presented at the Big Data Computing Service and

Applications (BigDataService), 2015 IEEE First International Conference on.

Hammer, C., Kostroch, D. C., & Quiros, G. (2017). Big Data; Potential, Challenges

and Statistical Implications. Retrieved from

https://www.ibm.com/blogs/cloud-computing/2014/02/cloud-computing-and-big-data-an-ideal-combination/


http://dataconomy.com/2017/07/big-data-banking-financial-systems/


84

Hampton, T. V. (2015). Big Data Boosts Safety in Brazil. Retrieved from

https://www.enr.com/articles/9640-big-data-boosts-safety-in-

brazil?v=preview

Han, K. K., & Golparvar-Fard, M. (2017). Potential of big visual data and building

information modeling for construction performance analytics: An exploratory

study. Automation in Construction, 73, 184-198.

Han, S., Lee, S., & Peña-Mora, F. (2012). A machine-learning classification approach

to automatic detection of workers' actions for behavior-based safety analysis.

In Computing in Civil Engineering (2012) (pp. 65-72).

Harenberg, M. (2017). 5 ways to boost construction productivity. Retrieved from

https://www.planradar.com/construction-productivity/

Hart, C. (1998). Doing a literature review: Releasing the social science research

imagination: Sage.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U.

(2015). The rise of “big data” on cloud computing: Review and open research

issues. Information Systems, 47, 98-115.

Humphreys, R. (2016). How Big Data Complements Information Management.

Retrieved from https://sourceable.net/how-big-data-complements-

information-management/

Ioannidis, D., Fotiadou, A., Krinidis, S., Stavropoulos, G., Tzovaras, D., &

Likothanassis, S. (2015). Big Data and Visual Analytics for Building

Performance Comparison. Paper presented at the IFIP International

Conference on Artificial Intelligence Applications and Innovations.

Janda, K. B., Patrick, J., Granell, R., Bright, S., Wallom, D., & Layberry, R. (2015). A

WICKED approach to retail sector energy management. Paper presented at the

Proceedings of ECEEE Summer Study.

https://www.enr.com/articles/9640-big-data-boosts-safety-in-brazil?v=preview



https://sourceable.net/how-big-data-complements-information-management/


85

Jiao, Y., Wang, Y., Zhang, S., Li, Y., Yang, B., & Yuan, L. (2013). A cloud approach

to unified lifecycle data management in architecture, engineering, construction

and facilities management: Integrating BIMs and SNS. ADVANCED

ENGINEERING INFORMATICS, 27(2), 173-188.

Jiao, Y., Zhang, S., Li, Y., Wang, Y., & Yang, B. (2013). Towards cloud augmented

reality for construction application by BIM and SNS integration. Automation

in Construction, 33, 37-47.

Khan, A., & Hornbæk, K. (2011). Big data from the built environment. Paper presented

at the Proceedings of the 2nd international workshop on Research in the large.

Knight, W. (2015). New boss on construction sites is a drone. In: MIT Technology

review.

Koseleva, N., & Ropaite, G. (2017). Big Data in Building Energy Efficiency:

Understanding of Big Data and Main Challenges. Procedia Engineering, 172,

544-549.

Labaree, R. V. (2009). Research Guides: Organizing Your Social Sciences Research

Paper: Types of Research Designs.

Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety.

META Group Research Note, 6, 70.

Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business

Horizons, 60(3), 293-303.

Lu, W., Chen, X., Ho, D. C., & Wang, H. (2016). Analysis of the construction waste

management performance in Hong Kong: the public and private sectors

compared using big data. Journal of Cleaner Production, 112, 521-531.

Lu, W., Chen, X., Peng, Y., & Shen, L. (2015). Benchmarking construction waste

management performance using big data. Resources, Conservation and

Recycling, 105, 49-58.

86

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A.

H. (2011). Big data: The next frontier for innovation, competition, and

productivity.

Marr, B. (2015). How Big Data is Changing Healthcare. Retrieved from

https://www.forbes.com/sites/bernardmarr/2015/04/21/how-big-data-is-

changing-healthcare/#215a20d72873

Marr, B. (2017). The Complete Beginner’s Guide to Big Data in 2017. In: Forbess.

Martínez-Rojas, M., Marín, N., & Vila, M. A. (2015). The role of information

technologies to address data handling in construction project management.

Journal of Computing in Civil Engineering, 30(4), 04015064.

Mathew, B. (2016). How Big Data is reducing costs and improving performance in the

upstream industry. Retrieved from

http://www.worldoil.com/news/2016/12/13/how-big-data-is-reducing-costs-

and-improving-performance-in-the-upstream-industry

Mathew, P. A., Dunn, L. N., Sohn, M. D., Mercado, A., Custudio, C., & Walter, T.

(2015). Big-data for building energy performance: Lessons from assembling a

very large national database of building energy use. Applied Energy, 140, 85-

93.

Mauricio. (2016). The role of big data In the banking industry. Retrieved from

http://bigdata-madesimple.com/role-big-data-banking-industry/

Maxwell, J. A. (2012). Qualitative research design: An interactive approach (Vol.

41): Sage publications.

Meneer, D. (2015). How Big Data is Transforming Construction Businesses. Retrieved

from https://www.constructionmonitor.com/blog/2015/06/26/how-big-data-is-

transforming-construction-businesses/

https://www.forbes.com/sites/bernardmarr/2015/04/21/how-big-data-is-changing-healthcare/#215a20d72873

https://www.forbes.com/sites/bernardmarr/2015/04/21/how-big-data-is-changing-healthcare/#215a20d72873

http://www.worldoil.com/news/2016/12/13/how-big-data-is-reducing-costs-and-improving-performance-in-the-upstream-industry


http://bigdata-madesimple.com/role-big-data-banking-industry/

https://www.constructionmonitor.com/blog/2015/06/26/how-big-data-is-transforming-construction-businesses/


87

Meola, A. (2016). What is the Internet of Things (IoT)? Retrieved from

http://www.businessinsider.com/what-is-the-internet-of-things-definition-

2016-8/?IR=T

Mills, S., Lucas, S., Irakliotis, L., Rappa, M., Carlson, T., & Perlowitz, B. (2012).

DEMYSTIFYING BIG DATA: a practical guide to transforming the business

of Government. TechAmerica Foundation, Washington.

Moreno, M. V., Dufour, L., Skarmeta, A. F., Jara, A. J., Genoud, D., Ladevie, B., &

Bezian, J.-J. (2016). Big data: the key to energy efficiency in smart buildings.

Soft Computing, 20(5), 1749-1762.

Nath, K., Dhar, S., & Basishtha, S. (2014). Web 1.0 to Web 3.0-Evolution of the Web

and its various challenges. Paper presented at the Optimization, Reliabilty, and

Information Technology (ICROIT), 2014 International Conference on.

O'Leary, D. E. (2013). BIG DATA’, THE ‘INTERNET OF THINGS’AND THE

‘INTERNET OF SIGNS. Intelligent Systems in Accounting, Finance and

Management, 20(1), 53-65.

O'Reilly. (2014). Big data now: 2014 Edition: O'Reilly Media, Incorporated.

Olshannikova, E., Ometov, A., Koucheryavy, Y., & Olsson, T. (2015). Visualizing

Big Data with augmented and virtual reality: challenges and research agenda.

Journal of Big Data, 2(1), 22.

Oracle. (2015). Improving Manufacturing Performance with Big Data: Architect’s

Guide and Reference Architecture Introduction. Retrieved from

http://www.oracle.com/us/technologies/big-data/big-data-manufacturing-

2511058.pdf

Oudjehane, A., & Moeini, S. (2017). Big data in construction projects: Risk and

opportunity management.

http://www.businessinsider.com/what-is-the-internet-of-things-definition-2016-8/?IR=T


http://www.oracle.com/us/technologies/big-data/big-data-manufacturing-2511058.pdf


88

Özköse, H., Arı, E. S., & Gencer, C. (2015). Yesterday, today and tomorrow of big

data. Procedia-Social and Behavioral Sciences, 195, 1042-1050.

Pal, K. (2015). The Impact of the Internet of Things on Big Data. Retrieved from

http://data-informed.com/the-impact-of-internet-of-things-on-big-data/

Patton, M. Q. (2005). Qualitative research: Wiley Online Library.

Peiffer, E. (2016). 3 concepts that will shape the future of construction. Retrieved from

https://www.constructiondive.com/news/3-concepts-that-will-shape-the-

future-of-construction/417049/

Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Sensing as a

service model for smart cities supported by internet of things. Transactions on

Emerging Telecommunications Technologies, 25(1), 81-93.

Portela, F., Lima, L., & Santos, M. F. (2016). Why Big Data? Towards a Project

Assessment Framework. Procedia Computer Science, 98, 604-609.

Porwal, A., & Hewage, K. N. (2013). Building Information Modeling (BIM)

partnering framework for public construction projects. Automation in

Construction, 31, 204-214.

Press, G. (2013). 3 Big Data Milestones. Forbes Tech Magazine, May, 9.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and

data-driven decision making. Big Data, 1(1), 51-59.

Qubole. (2017). Big Data Cloud Database & Computing. Retrieved from

https://www.qubole.com/resources/big-data-cloud-database-and-computing/

Reckoning, T. D. (2013). The six most fascinating technology statistics today.

Retrieved from https://dailyreckoning.com/the-six-most-fascinating-

technology-statistics-today/


https://www.constructiondive.com/news/3-concepts-that-will-shape-the-future-of-construction/417049/



https://dailyreckoning.com/the-six-most-fascinating-technology-statistics-today/

https://dailyreckoning.com/the-six-most-fascinating-technology-statistics-today/

89

Redmond, A., El-Diraby, T., & Papagelis, M. (2015). Employing an Exploratory

Research Stage to Evaluate Green Building Technologies for Sustainable

Systems. Submitted to international journal; under review.

Reeve, A. (2013). Managing data in motion: data integration best practice techniques

and technologies: Newnes.

Reiners, D., Stricker, D., Klinker, G., & Müller, S. (1998). Augmented reality for

construction tasks: Doorlock assembly. Proc. IEEE and ACM IWAR, 98(1),

31-46.

Rijmenam, M. v. (2015). Big Data Can Help Construction Companies Deliver Projects

On Time. Retrieved from https://datafloq.com/read/big-data-construction-

companies-deliver-projects-t/143

Rubin, H. J., & Rubin, I. S. (2011). Qualitative interviewing: The art of hearing data:

Sage.

Sadhu, R. (2016). Big Data For Construction: How It Can Help You Deliver Your

Project On Time. Retrieved from https://www.planacademy.com/big-data-

construction/

Santiago Castagnino, Christoph Rothballer, & Gerbert, P. (2016). What's the future of

the construction industry? Retrieved from

https://www.weforum.org/agenda/2016/04/building-in-the-fourth-industrial-

revolution/

SAS. (2013). Five Big Data Challenges. Retrieved from

https://www.sas.com/content/dam/SAS/en_us/doc/other1/five-big-data-

challenges-106263.pdf

Schwab, K. (2017). The fourth industrial revolution: Crown Business.

https://datafloq.com/read/big-data-construction-companies-deliver-projects-t/143


https://www.planacademy.com/big-data-construction/


https://www.weforum.org/agenda/2016/04/building-in-the-fourth-industrial-revolution/


https://www.sas.com/content/dam/SAS/en_us/doc/other1/five-big-data-challenges-106263.pdf

https://www.sas.com/content/dam/SAS/en_us/doc/other1/five-big-data-challenges-106263.pdf

90

Shamsulhadi, B., & Fadhlin, A. (2016). Documenting its application in Quantity

Surveying research: A Review. International Journal of Built Environment and

Sustainability, 3(1), 10-17. doi:10.11113/ijbes.v3.n1.105

Shamsulhadi, B., Fadhlin, A., & Hamimah, A. (2015). Research into the Bills of

Quantities: Where can it be focused. The Malaysian Surveyor Journal, 50(3),

15-24.

Shrestha, J. (2013). Big Data, Predictive Analytics, and Data Visualization in The

Construction Engineering. CCEE Graduate Student Research Showcase and

Poster Competition.

Simon, P. (2013). Too Big To Ignore. Canada: John Wiley & Sons, Inc.

Skibniewski, M., & Golparvar-Fard, M. (2016). Toward a Science of Autonomy for

Physical Systems: Construction. arXiv preprint arXiv:1604.03563.

Stankovic, J. A. (2014). Research directions for the internet of things. IEEE Internet

of Things Journal, 1(1), 3-9.

Teizer, J., Cheng, T., & Fang, Y. (2013). Location tracking and data visualization

technology to advance construction ironworkers' education and training in

safety and productivity. Automation in Construction, 35, 53-68.

Travis, D. (2016). Desk research: the what, why and how. Dostupno na http://www.

userfocus. co. uk/articles/desk-research-the-what-why-and-how. html,

pristupljeno, 10, 2017.

Viktor, M.-S., & Kenneth, C. (2013). Big data: A revolution that will transform how

we live, work, and think. Houghton Mifflin Harcourt.

Waal-Montgomery, M. d. (2015). World's data volume to grow 40% per year & 50

times by 2020: Aureus. Retrieved Syyskuu, 28, 2016.

http://www/

91

Webster, J., & Watson, R. T. (2002). Analysing the past to prepare for the future:

writing a literature review. MIS Quarterly, 26(2), 1-11.

Wei, C., & Li, Y. (2011). Design of energy consumption monitoring and energy-saving

management system of intelligent building based on the Internet of things.

Paper presented at the Electronics, Communications and Control (ICECC),

2011 International Conference on.

Welman, C., Kruger, F., & Mitchell, B. (2005). Research methodology: Oxford

University Press Cape Town.

Whitehorn, M. (2012). Big Data bites back: How to handle those unwieldy digits when

you can't just cram it into tables. Retrieved from

https://www.theregister.co.uk/2012/08/27/how_did_big_data_get_so_big/

Wood, C. (2016). Betting on big data: How construction firms are leveraging digitized

job sites. Retrieved from https://www.constructiondive.com/news/betting-on-

big-data-how-construction-firms-are-leveraging-digitized-job-si/431047/

Xia, F., Yang, L. T., Wang, L., & Vinel, A. (2012). Internet of things. International

Journal of Communication Systems, 25(9), 1101.

Yang, J., Park, M.-W., Vela, P. A., & Golparvar-Fard, M. (2015). Construction

performance monitoring via still images, time-lapse photos, and video streams:

Now, tomorrow, and the future. ADVANCED ENGINEERING

INFORMATICS, 29(2), 211-224.

Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., &

Vasilakos, A. V. (2016). Big data: From beginning to future. International

Journal of Information Management, 36(6), 1231-1247.

https://www.theregister.co.uk/2012/08/27/how_did_big_data_get_so_big/

https://www.constructiondive.com/news/betting-on-big-data-how-construction-firms-are-leveraging-digitized-job-si/431047/


92

Zafira, N. M., Shamsulhadi, B., & Roslan, A. (2018). Potential opportunities and

future directions of big data in the construction industry. Paper presented at

the The 12th SEATUC Symposium 2018, Universitas Gadjah Mada,

Yogyakarta.

Zhang, S., Teizer, J., Lee, J.-K., Eastman, C. M., & Venugopal, M. (2013). Building

information modeling (BIM) and safety: Automatic safety checking of

construction models and schedules. Automation in Construction, 29, 183-195.

Zhang, Y., Luo, H., & He, Y. (2015). A system for tender price evaluation of

construction project based on big data. Procedia Engineering, 123, 606-614.

Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2012).

Harness the power of big data The IBM big data platform: McGraw Hill

Professional.

APPENDIX A

APPENDIX A INTERVIEW QUESTION

APPENDIX A

QUANTITY SURVEYING DEPARTMENT

FACULTY OF BUILT ENVIRONMENT

UNIVERSITI TEKNOLOGI MALAYSIA

SEMI-STRUCTURED INTERVIEW FORM FOR

AN APPRAISAL INTO THE POTENTIAL APPLICATION OF

BIG DATA IN CONSTRUCTION INDUSTRY

Objective of Interview:

1. To seek opinions on the direction of big data in the construction

industry.

2. To seek opinions and suggestions on the potential application of

big data in the construction industry.

Prepared by; Siti Aisyah binti Ismail (A13BE0148) 941101-08-6232 4SBEQ (Bachelor of Quantity Surveying) Email : [email protected] / [email protected]

Tel: 018-9714460

Supervisor : Dr Shamsulhadi bin Bandi

NOTES: This interview session is used to collect data for the above study. All the

information given will be kept as PRIVATE & CONFIDENTIAL and for the use of

academic purposes only. The interview will only be recorded with the consent of the

interviewees.

mailto:[email protected]


APPENDIX A

SECTION A: INTERVIEW SESSION DETAILS

Date

Day

Venue

SECTION B : INTERVIEWEE DETAILS

Name

Designation

Background

SECTION C

Big data concept is normally based on the 3Vs (volume, variety and velocity) where

according to Gartner IT Glossary, big data is ‘a high-volume, high-velocity and/or


information processing that enable enhanced insight, decision making, and process

automation’.

1. How do you think the concept is being practiced in the context of construction

industry?

Based on literature review, the applicability of big data in construction industry is

magnified by the emerging trends in the industry such as BIM, Internet of Things

(IoT), smart buildings, cloud computing and augmented/virtual reality.

2. What is your opinion on this?

3. Manufacturing and retail industry is among the industry that has already

benefited from the big data adoption. From your opinion, is big data has the

potential to be applied in the construction industry as well?

APPENDIX A

Based on my theoretical analysis of various literature, the results of the big data

research orientation are as follows;

Table 1: Detail context of big data research area

Context of big

data research area

Important

keywords


Construction Project Management

monitoring Progress/performance monitoring through IoT devices

time, cost Better time and cost management Decision-making Making decision using predictive data that

leads to lower project risk and better management

Safety Site safety, workers’ safety

behaviour

Big data generated through IoT devices in tracking and visualize site safety conditions as well as workers’ behaviour towards safety

Energy management

Consumption, building

performance

Enhancing energy efficiency and building performance through an understanding of building energy consumption


Decision-making Big data for prompt and informed decision-making

Resource management

Resources tracking Resources tracking through IoT devices to improve resources utilization efficiency

4. Based on Figure 1 and Table 1, what is your opinion on the findings?

1 5

2

3

4

Figure 1: Generated model by NVivo software representing the

frequency of big data research area

APPENDIX A

5. Based on your own point of view, where do you think is the future direction of


6. Do you have any suggestion on the potential application of big data in the

construction industry?

---- End of questions. Thank you ----

APPENDIX B

APPENDIX B INTERVIEW TRANSCRIPT

APPENDIX B









industry.




Tel: 018-9714460





interviewees.



APPENDIX B


Date 19th March 2018, 2.30PM

Day Monday

Venue

Bilik Perbincangan BPPIB,

Aras 5, Blok C6, Kompleks C,

Putrajaya

SECTION B: INTERVIEWEE DETAILS

Name Puan Jamaliah binti Jaafar

Designation

Director, Industrial Production and Construction Statistics

Division

Department of Statistics Malaysia (DOSM)

Background

- Working experience : 17 years

- Involved in big data project called price intelligence using

web scraping to collect price data not only from physical shop

but also from online businesses to develop consumers’ price index

- The big data project started in 2014 and is still on-going

SECTION C





automation’.


industry?

Seperti yang diketahui, data-data sebelum ni kita kumpulkan melalui survey.

Contohnya, kalau nak tahu harga seseuatu barang tu, kita akan pergi kedai ke kedai

untuk dapatkan harga jualan. Tapi, dengan teknologi harini, data harga tu kita boleh

dapat melalui internet juga. Benda ni sama juga kalau kita kaitkan dengan construction

industry. Penggunaan teknologi dalam pembinaan juga membolehkan data itu

available untuk dikumpul, dianalisis dan digunapakai dengan cepat. Big data ni dia

APPENDIX B

melibatkan pelbagai sumber data structured, unstructured dan juga data-data dari

media social pun diambil kira. Oleh itu, data tu perlu diteliti terlebih dahulu sebelum

nak gunakan dia means tak boleh amik terus. Data tu kena kaji dahulu before making

decision to utilize it supaya ianya boleh dipercayai.





Betul. Big data memang sebenarnya disokong oleh trend2 lain yang semakin

berkembang. Cloud computing contoh yang paling mudah dimana data-data sekarang

kita boleh dapat dari internet. Data-data yang banyak dan dating daripada pelbagai

sumber tu kita kumpulkan, proses atau analisis dan seterusnya diterbitkan on internet

untuk kegunaan pelbagai pihak. Selain itu, penggunaan IoT devices untuk big data

juga banyak tetapi banyak di Negara luar kalau untuk construction industry. Malaysia

masih belum apply lagi kalau ada pun mungkin hanya company yang berkeupayaan

sahaja yang mampu. Sebab teknologi IoT ni usually costly.




Yes, memang berpotensi. Sebenarnya big data ni memang berpotensi untuk pelbagai

industri sebabnya setiap industri pun akan ada data, akan deal with data yang menepati

ciri big data tu. So, construction industry memang tidak terkecuali.

APPENDIX B




Context of big

data research area

Important

keywords







behaviour


Energy management


performance




Resource management


1 5

2

3

4



APPENDIX B


Setuju dengan construction project management. Sebab kalau kita lihat pun negara-

negara luar memang dah mula guna big data contoh dalam penentuan bajet pembinaan,

mengurangkan risiko projek supaya projek tidak delay dengan menggunakan pelbagai

sumber maklumat sesuai dengan ciri-ciri big data itu sendiri. Mereka juga

menggunakan big data ni untuk organize construction site. Contohnya seperti saya

katakan tadi, penggunaan IoT devices seperti sensors. Dari apa yang saya tahu, negara

luar, mereka dah mula apply sensors dekat jentera-jentera yang digunakan di dalam

pembinaan untuk track location jentera tersebut dan penggunaan jentera itu.

Maksudnya macam kalau jentera tu tidak digunakan atau sedang digunakan, dia boleh

tahu. Dari situ dia boleh manage jentera-jentera di tapak pembinaan. Tetapi, rasanya

dalam Malaysia belum apply lagi. If ada pun maybe company yang berkeupayaan

sahaja yang mampu.



Construction project management memang will be the future sebab macam contoh

yang saya berikan tadi dari Negara luar, semua tu ke arah pengurusan projek, dan

aplikasi yang sama mungkin boleh di apply di Malaysia. Selain itu, mungkin untuk

membantu dalam decision making. Sebab data-data yang banyak dan dari pelbagai

sumber ni kalau kita dapat extract the value, memang ia dapat membantu dalam

membuat sesuatu keputusan. Contohnya, kalau dalam hidupan harian, jika kita nak

buat sesuatu keputusan, mesti kita akan meneliti setiap maklumat yang berkenaan

sampai kita dapat keputusan yang betul.



Kalau ikutkan big data project yang saya involve sekarang adalah berkenaan web

scraping dimana data-data berkenaan harga yang dikumpul melalui survey dan juga

data daripada internet digabungkan di dalam satu platform. Di sini, harga-harga ni

dapat dianalisis untuk membangunkan indeks harga. Mungkin konsep yang sama

APPENDIX B

boleh digunakan. Contohnya, buat satu system harga untuk barang-barang pembinaan

di mana sistem tu boleh digunakan untuk menentukan bajet pembinaan. Data-data

harga tu adalah data yang updated dari masa ke semasa mengikut perubahan harga

supaya ianya reliable untuk dirujuk. Dengan adanya sistem ni, penentuan harga tidak

akan melebihi harga sebenar dan keputusan dalam pemilihan kontraktor semasa proses

pembidaan projek boleh improve. Maksudnya di sini, bila semua kontraktor bid

menggunakan indeks harga yang ditetapkan dalam sistem tu, so harga masa bidding

process tu akan jadi lebih competitive.


APPENDIX B









industry.




Tel: 018-9714460





interviewees.



APPENDIX B


Date 30th March 2018, 10.00AM

Day Friday

Venue

S P Setia Bhd Corporate HQ,

No. 12, Persiaran Setia Dagang,

Setia Alam, Seksyen U13,

40170 Shah Alam,

Selangor Darul Ehsan, Malaysia


Name M. Shahrul Azri Bin Mamat

Designation Senior Manager

Design

Background


- Never involve in big data project

- Familiar with the technologies that causes the emerging of big

data but never thought that the data generated from the

technologies is called as big data

- Went to few conferences and knowledge sharing on the

utilization of advanced technologies in the construction

industry

SECTION C





automation’.


industry?

Big data in the construction industry kebanyakannya digunakan untuk BIM di mana

daripada BIM itu sendiri all construction data/informations are gathered. Data itu

adalah dalam bentuk material (dari supplier), data dari segi costing atau price. Data-

data ini dirangkumkan untuk biasanya kalau BIM digunakan secara menyeluruh

APPENDIX B

contohnya BIM level up to 5D 6D will include cost and project scheduling. So bila ia

melibatkan all of that, maksudnya banyak information will be included in the BIM

contohnya dari segi volume – materials data or any other data will be gathered all

together. Variety – kebanyakan jenis2 data yang ada dalam model itu sendiri to form

a model and model itu digunakan untuk construction purposes. Velocity – In

construction industry, particularly overseas, big data is already matured - dah banyak

apply BIM even stage BIM itu sendiri dah achieve up to 6D 7D while in Malaysia

we’re still at 3D 4D. Dekat sana dah boleh ada data yang banyak dikumpul and even

in a meeting pun they will use the same data. Bila meeting just buka all the data they

have dalam satu platform. Data yang sama reach the stakeholders at instant compared

dengan conventional way different dimana stakeholders akan guna data yang

personally emailed or given to them which may differ from the data yang diterima oleh

stakeholders lain.





Yes, seperti yang saya katakan tadi, penggunaan BIM di dalam construction industry

memang influence the big data.




Yes, memang berpotensi to be applied dan akan memberi big impact to the

construction industry but adoption very slow sebab technology. Kita adopt technology

from outside so it is costly. That is why now we adopt one by one. Contohnya, kita

start dengan 3D modelling dulu only then baru start dengan project coordination using

IoT. Macam ada certain overseas country yang dah ada satu base using IoT dimana

guna satu platform untuk do the checking on site and keluarkan RFI, keluarkan semua

EI/AI through one platform so everything tracked. Sekarang dekat Malaysia, benda ini

APPENDIX B

ada but terlalu sedikit. Dah ada BIM level tapi tak sampai project scheduling and

project tracking lagi sebab we need technology for this. And it also requires internet.

The thing is internet speed sini memang slow lagi compared to kat luar which is 2 to

3 times slower. So that influence the adoption of big data analysis and etc. And untuk

upgrade semua itu – cost. Cost untuk Malaysia mahal, sebab satu is currency kalau

currency kita sama macam Singapore maybe adoption boleh jadi cepat. So, one thing

is cost. Kesimpulannya, memang big data akan bring huge impact but at the same time

kita belum ready in terms of budget as well as knowledge and skill.




Context of big

data research area

Important

keywords





1 5

2

3

4



APPENDIX B

Context of big

data research area

Important

keywords


Decision-making Making decision using predictive data that leads to lower project risk and better management


behaviour


Energy management


performance




Resource management



I think the findings are well covered. Construction industry is straightforward. Apa

yang kita buat itulah dia. In fact, sebenarnya kita dah apply contohnya camera installed

on construction site untuk monitor progress. Even in the overseas, camera on site is

applied not just untuk monitor the work progress but also digunakan untuk safety.

Contohnya 360 degree camera, dekat overseas mereka pakai camera untuk detect

anything happen on site, capture keadaan yang tidak selamat and use that photos for

induction and discuss on its prevention as well as take action. Dekat overseas memang

banyak benda ni even drones pun memang dah banyak digunakan di dalam

construction industry. But I think the use of drones pun setakat progress photos, in

terms of surveillance kurang and tak fully utilized. Dekat Malaysia, ada potensi untuk

teruskan apply benda ini, but then it actually depends on the contractor whether to

invest ataupun tidak. But macam yang saya katakan tadi, it is about cost. Malaysia

adalah Negara yang import technology, so if contractor biasa nak adopt agak susah

and maybe big contractors yang ada capital sahaja yang willing to invest.



APPENDIX B

Saya rasa if daripada findings yang you dapat, saya merasakan memang banyak

tertumpu kepada construction project management sebab kalau kita nak memastikan

sesebuah projek itu berjaya, the management of the project itself kena bagus. Selain

itu, safety and decision-making design framework. Safety macam yang saya katakan

tadi, instead of just gunakan camera dan drones untuk kegunaan project progress

monitoring, utilize it more untuk monitor safety. Design pula sebab Malaysia sekarang

is getting familiarized with BIM, so sedikit sebanyak dia menyumbang kepada

decision making untuk design. But if BIM is been taking to the next level macam 5D,

6D or 7D, secara tidak langsung application big data itu sendiri pun akan berkembang

beyond design.



Dalam construction industry banyak guna data untuk past analysis. Contohnya, detect

semua masalah dalam model, resolve dekat situ then baru start construction. So benda

tu dah termasuk dalam data, dimana benda tu boleh digunapakai untuk next project.

Contoh macam government sebenarnya dah start guna pakai data yang diaorang ada

untuk learn from past project so that next project takkan buat kesalahan yang sama lagi

and dalam pada masa yang sama dari situ kita dapat kurangkan risiko. Risiko mesti

ada, sebab apa-apa pun mesti ada risiko. Tetapi if benda ni is applied, dia dapat bantu

untuk kurangkan risiko sebab less mistakes means less risks. Dari segi property pula,

contohnya untuk market diaorang kumpul data ni dalam bentuk keinginan pembeli

tidak kira samaada dari electronic survey ke, facebook ke, semua akan dikumpulkan.

Information macam pembeli nak rumah macammana, katmana, design, environment,

open spaces, facilities – data-data ni dikumpul untuk create market untuk property

macam development, data tu digunapakai untuk cater for certain-certain demand. Ini

dalam context property lah tapi benda ni sebenarnya merangkumi construction juga

macam lepas kita analyse all the data kita akan dapat tahu the demand so construction

have to follow it in terms of the design, material semua will be various. Selain monitor

guna camera atau drones, boleh juga pakai teknologi wearable devices. Wearable

device sekarang ni dah dapat perhatian juga. Tapi yang kita nampak harini mungkin

banyak guna untuk personal use macam smart watch tu. Kalau untuk construction

industry, wearable device ni boleh jadi safety boot kita, safety hat di mana kita boleh

APPENDIX B

track pekerja kita samaada apa yang mereka sedang buat ataupun monitor safety

behaviour mereka. So, pihak atas dapat monitor secara langsung and tindakan pun

boleh diambil dengan cepat sebab information tu dapat cepat. Also, kalau boleh adopt

yang lebih advanced, mungkin boleh guna teknologi macam masa saya pergi satu talk,

orang tu share dekat Australia, excavator tu dia kawal pakai iPad. Contohnya plot

tempat yang kita nak excavate tu, depth dia atau data-data lain berkenaan excavation

tu semua masukkan dalam iPad then dengan teknologi geo-location juga, excavator tu

boleh bekerja sendiri. But this needs a big capital lah. Dekat Australia, labour cost

diaorang boleh cecah 60% dari construction cost that is why diaorang go for

technology. Construction industry is labour intensive, if labour tu still cheap, compare

to technology, the industry will choose labour instead of technology sebab dia murah.


APPENDIX B









industry.




Tel: 018-9714460





interviewees.



APPENDIX B


Date 16th April 2018, 10.00AM

Day Monday

Venue

Ministry of Works

Level 10, Menara Usahawan,

Persiaran Perdana, Presint 2,

62652 Putrajaya Malaysia


Name Encik Hamidon Bin Sajari

Designation Chief Assistant,

Division of Contractors and Entrepreneur Development

Background


- Knowledge on big data : Moderate but is well aware of the

technologies in the construction industry that trigger big data

- Currently trying to propose the extensive usage of drone for

construction site and working on developing a platform to

assist the selection of the best contractor for construction

projects.

SECTION C





automation’.


industry?

Kita dalam industri pembinaan memang banyak data and memang kita sedia tahu data

yang banyak ni bukan dari satu sumber sahaja tapi dari pelbagai sumber dan dalam

pelbagai bentuk. Value data-data ini sebenarnya sangat diperlukan untuk pelbagai

kegunaan terutamanya dalam meningkatkan industri pembinaan. Setiap agensi

samaada Kerajaan ataupun swasta ada data-data sendiri. JKR ada data sendiri, CIDB

ada data dia sendiri, tapi data-data ni walaupun semuanya data yang berkaitan dalam

APPENDIX B

construction industry, dia tak intergrate antara satu sama lain. Pemantauan projek pun

tak integrate so benda ni membuatkan data accuracy tu low. Sebab semua orang

menggunakan data yang berlainan sampai tak tahu which data is really to be used.





Kat sini kalau yang saya tahu BIM memang sekarang JKR pun pakai untuk selesaikan

issue in construction contohnya issue dari segi services macam ducting for M&E apa

semua bila ada BIM ia jadi mudah. Smart buildings memang kita sekarang pun sedang

ke arah sustainability so makin banyak green buildings and with the advanced of

technology, memang dia akan membawa kepada perkembangan big data.




Semestinya berpotensi tapi sekarang it is still in the introductory stage. But then, if

cakap pasal government projects, we are tight to cost, so to adopt technology bukan

tak boleh atau tidak berpotensi, banyak benda yang perlu consider sebab benda mahal

kan.

APPENDIX B




Context of big

data research area

Important

keywords







behaviour


Energy management


performance




Resource management


1 5

2

3

4



APPENDIX B


Memang bagus sebab memang kita mengharap benda ni akan ada. JKR memang dh

lead dalam project management di mana JKR ada system sendiri untuk pemantauan

projek yang dipanggil sebagai sistem kawal dan lapor (SKALA). It is based on the

system where all the JKR punya projects will be in the system start from planning until

penyerahan kepada client and up to defect liability period. Costing, time, decision

making all can be made through the system. Tapi, sistem ni still direkod secara manual,

maksudnya menggunakan kertas atau report form. So, government memang perlu

benda macamni yang boleh meningatkan sistem pemantauan project.



Sistem pemantauan projek. JKR ada client dia so pelaporan tu nak cepat and nak

maklumat yang tepat dan sama. Agensi lain takde sistem so proses pelaporan adalah

manual iaitu melalui report then submit manually jadi proses tu lambat sedikit.

Resource management dari segi penghantaran materials yang cepat. Kalau sekarang,

we have IBS di mana sedikit sebanyak membantu dalam mempercepatkan proses

penghantaran dan pemasangan. Tapi in big data context, materials lain yang biasa if

kita adopt the technology, proses penghantaran dia juga boleh jadi cepat.



Untuk meningkatan sistem pemantauan projek sebenarnya kita ada juga berbincang

berkenaan drones adoption but ia perlu melalui beberapa procedure, kena diluluskan

oleh MoF (Ministry of Finance) sebab drones mahal, so untuk investment terhadap

technology usually need to be reviewed dulu kepentingan dia, how big the impact is if

we adopt it. Tapi sebenarnya drones kalau digunakan pun mesti on progress photo. It

should be more than that, contohnya drones boleh provide real-time punya site

visualisation to the office so that the upper management or client may monitor the

construction site themselves. Selain itu, kita ada juga nak wujudkan satu program

APPENDIX B

untuk cari the best contractor so we need the data. For now, sumber kita adalah

maklumat kontraktor daripada sistem CIDB. If the contractors can provide the

information on the system, benda ni akan memudahkan untuk menilai setiap kontraktor

dan mempercepatkan proses pemilihan kontraktor. Sistem ini boleh menggantikan

sistem manual untuk evaluate semua contractors dan secara tidak langsung dapat

mengelakkan atau mengurangkan keputusan pemilihan kontraktor yang biased. Untuk

materials delivery, penggunaan sensors technology atau IoT devices yang diletakkan

dengan materials atau its transporter dapat membantu untuk memantau pergerakan

resources dan meningkatkan proses penghantaran.


APPENDIX B









industry.




Tel: 018-9714460





interviewees.



APPENDIX B


Date 22nd March 2018, 2.30PM

Day Thursday

Venue

Public Private Partnership Unit

Department of Prime Minister

Level 7, Menara Usahawan,

Persiaran Perdana, Presint 2,

62652 Putrajaya Malaysia


Name Sr Rodziah Ismail

Designation

Principal Assistant Director,

Quantity Surveying Department,

Public Private Partnership Unit

Background


- Previously worked with JKR as a quantity surveyor

- Knowledge on big data : Moderate but is well aware of the

technologies in the construction industry that trigger big data

- Has involved in construction project that employ visual

cameras on site for project monitoring by the upper

management

SECTION C





automation’.


industry?

Big data kalau ikutkan memang the big volume of the data lah that makes it a big data

dan juga sumber-sumber data yang pelbagai tak kira lah samaada data in different

forms maksudnya macam dari different software atau format dan juga data yang datang

dari different organization. Tapi sebenarnya data-data yang banyak ni is not

APPENDIX B

synchronize with each other. Selalu je dalam construction mesti ada

miscommunication and the main problem is sebab information yang sampai tu tak

sama. Sama juga macam penggunaan software. Setiap stakeholders sometime will be

using different software and at last the information from each stakeholders tu susah

nak intergrate sebab tak compatible.





Kedatangan BIM actually membantu in the data integration. Sebab using BIM, semua

data tu will be on the model and semua stakeholders will be connected to that one

particular platform maksudnya dia guna data yang sama. Benda ni memang boleh

overcome the miscommunication problem. BIM juga boleh mengatasi problem during

construction, planning, dimana kita boleh nampak dalam design process. Tapi,

investment that has to be made is high and most of the industry players couldn’t afford.




If talk about the potentiality memang ada potensi but maybe we’re not ready in terms

of infrastructure. Sebab the cost will be high to adopt new technology. If in terms of

talent, saya rasa memang we are moving there. Sebab generasi Y sekarang ni memang

towards the technology so rasanya in terms of talent tiada masalah but maybe boleh

diasah lebih lagi.

APPENDIX B




Context of big

data research area

Important

keywords







behaviour


Energy management


performance




Resource management


1 5

2

3

4



APPENDIX B


Dari pengalaman saya for now memang ke arah construction project management. Ada

juga few JKR projects yang memang kita apply the use of visual camera on site untuk

capture the construction work progress. The idea datang sebab bos JKR masa tu nak

monitor sendiri the progress from his office. But then benda ni tak berterusan. Ada

projek yang dah stop gunakan camera untuk real-time punya project monitoring. So,

ia nampak macam tak efficient and membazir sebab tak diguna pakai. Selain itu,

penggunaan camera untuk safety pun penting sebab dah banyak dah kes-kes berkenaan

safety di tapak bina. So, dengan adanya big data ni, maybe it can help to lower down

the figures.



Bagi saya mungkin untuk cost atau rates iaitu untuk predict construction cost dan juga

untuk bidding process. Potential sangat besar and in fact before this CIDB ada initiate

nak buat data centre untuk collect data dari industry but then tiada yang ready to share.

Even CIDB bersedia untuk finance the project pun still the readiness is low.



Potential application mungkin boleh teruskan dengan penggunaan visual camera sebab

actually it helps untuk kurangkan masa to report the progress to the upper management.

Senang, diaorang monitor sendiri. Cepat dan efficient sebab action can be taken

immediately. Kalau conventional way, project manager kena pergi tangkap gambar

progress then report pula, then tunggu the upper management review and only then

decision or any action can be taken. It’s a bit old school and time consuming. Sama

juga kalau kita apply for safety monitoring. Workers punya safety behaviour kita boleh

detect real-time so tindakan boleh diambil segera and secara tidak langsung membantu

mengurangkan kes keselamatan dekat site. If boleh and mampu nak adopt technology

untuk improve the efficiency why not kita buat. Lagipun, not just progress and safety

APPENDIX B

monitoring, data dari visual camera ni pun can be used for the upper management atau

client untuk assess performance contractors and data ni boleh digunakan untuk

membuat keputusan pemilihan kontraktor. Untuk cost atau rates tadi, maybe a system

can be developed untuk set price benchmark. Dengan adanya benchmark ni, kontraktor

akan letak harga yang berpatutan semasa menender. Maksudnya penentuan harga tu

tansparent and benda ni boleh elak kontraktor untuk letak harga sesuka hati samaada

terlalu rendah ataupun terlalu tinggi. Harga-harga tender tu juga boleh dinilai and

compare melalui sistem tu untuk memilih kontraktor yang layak.


APPENDIX B









industry.




Tel: 018-9714460





interviewees.



APPENDIX B


Date 20th March 2018, 2.30PM

Day Tuesday

Venue Ampang, Kuala Lumpur


Name Puan Hajah Rauzah Zainal Abidin

Designation

Director,

Productivity & Competitiveness Development,

Malaysia Productivity Corporation (MPC)

Background


- Currently is retired in April 2018

- Has been given talk during several conferences regarding

the possibility of having big data in Malaysia

- Made survey on the openness of data sharing by different

organizations as she described open data as big data.

- Main focus of big data in the construction industry is on

how big data would be useful in improving the industry’s productivity.

SECTION C





automation’.


industry?

Government memang encourage big data because all over the world are talking about

big data, about open government so memang it was encouraged all over the world.

When talks about open data means it talks about big data. Kalau nak big data or open

data semua agencies has to be open in sharing data. Tapi, big data bukan sahaja

government yang park the database. Big data can come from google, internet and

APPENDIX B

bukan Malaysia construction only, all over the world. Tu concept big data tu which is

the availability of internet, google, technology and the possibility of getting database

tu mudah dan cepat. However, big data comes in structured and unstructured. So the

data may be used or not.





Ya, betul. Big data tak datang stand alone. It actually came from these trends yang

push kewujudan big data ni.




Yes, possibility of having big data in Malaysia’s construction industry memang ada.

Tapi sejauhmana pihak-pihak di dalam industri itu sendiri untuk data sharing, how

open they are kita tidak tahu.

APPENDIX B




Context of big

data research area

Important

keywords







behaviour


Energy management


performance




Resource management


1 5

2

3

4



APPENDIX B


Project management dari segi decision making on cost and time dan juga resource

management. As I am focusing dekat produktiviti industri, dua perkara ini jika diberi

perhatian dapat membantu dalam memperbaiki produktiviti construction industry.



Bagi saya, big data can help in decision making especially untuk time and cost decision

dan secara tidak langsung contribute to the productivity. Contohnya, producivity

adalah output over input. Output must be bigger. When you have big data sebagai

contoh nak buat rumah satu tingkat seluas 20 x 20 we know that the material, labour

cost berapa per square feet so all those information should be available on the net so

that you can make decision on how much you should spend so that kita ada benchmark

or senang kata bajet. That’s the use of big data then you can make decision on how to

improve in reducing cost so that it meets the benchmark. So dari situ kita akan evaluate

lah workflow, tengok cara buat kerja, employment – people that are well trained or not

well trained to reduce input dan juga kita tengok process flow atau time. Next, untuk

increase output first we can add value to the product so that you can sell at high price.

Contoh design rumah, IBS – boleh jual RM5k but with IBS you can jual at 3k but

market share besar thus output increased.

So, that is how you can improve productivity which is by improve the efficiency or

the value. Selain itu, resource management pun penting especially dalam materials

delivery sebab one of the problems yang menyebabkan produktiviti tu low is because

the long wait of the materials delivery.



Untuk decision making on cost, macam saya cerita tadi, if nak buat sebuah rumah kita

mesti tahu the price untuk tentukan harga. So, maybe all the data regarding price ni

APPENDIX B

can be gathered on a single platform to assist the decision making for construction

cost. Actually, information tu ada CIDB dah compiled to help contractor in project

planning. But then, dia tak menyeluruh sebab macam saya cakap tadi, big data means

open data. If the data is not being open or shared, penggunaan dia pun tak berapa

efficient. But if can be done, it’s good di mana di dalam sistem tu nanti will be a

benchmark on the material or labour cost and make it available to be used. Selain itu,

decision making such as google extract our information, park it in a database and use

it to predict the consumer pattern. This concept boleh pakai untuk gather buyers’

information extract dari internet on buat satu predictive model di mana ia dapat

membantu in making decision untuk construction type apa yang boleh dibina dan

dibeli di mana ia termasuk lah the design, cost and other related information that

reflects the buyers’ property demand. As for materials delivery, ia boleh

dipertingkatkan dengan penggunaan sensors and geo-tagging technology. Overseas

dah start pakai dah technology macam ni. Kita je still lag behind but probably it is

because of the cost. Penggunaan sensor ni contohnya kita boleh track location

materials tu ada dimana, so secara tidak langsung, it will help to reduce the risk of

materials delay, sebab setiap kali detect any problem, action can be taken immediately.

APPENDIX C PUBLISHED ARTICLE

IN IJBES

International Journal of Built Environment and Sustainability

IJBES

E-ISSN:2289-8948 ISSN: 1511-1369

Volume 5, Issue 2, 2018 May 2018

Published by

Faculty of Built Environment Universiti Teknologi Malaysia

In collaboration with

Penerbit UTM Universiti Teknologi Malaysia

81310, Johor Bahru, Malaysia

ii

IJBES International Journal of Built Environment and Sustainability

http://ijbes.utm.my

EDITORIAL BOARD

Roslan Amirudin, Chief Editor, Universiti Teknologi Malaysia Ariva Sugandi Permana, Managing Editor, Universiti Teknologi Malaysia Shamsul Hadi Bandi, Assistant Managing Editor, Universiti Teknologi Malaysia

International Editorial Board

ATM Nurul Amin, Northsouth University, Bangladesh Anoma Kumarasuriayar, Queensland University of Technology, Australia Ansar-Ul-Haque Yasar, Hasselt University, Belgium Awais Piracha, University of Western Sydney, Australia Bhishna Bajracharya, Bond University, Australia Chandrasekar Parsuvanathan, Velammal Engineering College, India Jayant K. Routray, Asian Institute of Technology, Thailand Jung-Geun Ahn, Gyeongsang National University, Korea Kwon Tae Ho, Semyung University, Korea Ludovic Lacrosse, Executive Chairman of Full Advantage Company, Thailand Michail Kagioglou, University of Huddersfield, United Kingdom Michihiko Shinozaki, Shibaura Institute of Technology, Japan Mohammed A. Husain, Modibbo Adama University of Technology, Nigeria Muhammad Abu Yusuf, Asian Institute of Technology, Thailand Ranjith Perera, Sultan Qaboos University, Oman Roos Akbar, Bandung Institute of Technology (ITB), Indonesia Shahed Khan, Curtin University of Technology, Australia Soheil Sabri, University of Melbourne, Australia Sudaryono Sastrosasmito, Gadjah Mada University, Indonesia Tetsu Kubota, Hiroshima University, Japan Vilas Nitivattananon, Asian Institute of Technology, Thailand

National Editorial Board

Mansor Ibrahim, International Islamic University, Malaysia Shamsul Hadi Bandi, Universiti Teknologi Malaysia Julaihi Wahid, Universiti Sains Malaysia Zalina Shari, Universiti Putra Malaysia Zahid Sultan, Universiti Teknologi Malaysia Ariva Sugandi Permana, Universiti Teknologi Malaysia Ismail Said, Universiti Teknologi Malaysia M. Rafee Majid, Universiti Teknologi Malaysia Kherun Nita Ali, Universiti Teknologi Malaysia Hamidah Ahmad, Universiti Teknologi Malaysia Hasanuddin Lamit, Universiti Teknologi Malaysia Mahmud Mohd Jusan, Universiti Teknologi Malaysia Raja Nafida bt Raja Shahminan, Universiti Teknologi Malaysia Tareef Hayat Khan, Universiti Teknologi Malaysia Khairul Anwar Khaidzir, Universiti Teknologi Malaysia Gurupiah Mursib, Universiti Teknologi Malaysia Wan Hashimah Wan Ismail, Universiti Teknologi Malaysia Fadhlin Abdullah, Universiti Teknologi Malaysia Nur Emma Mustapa, Universiti Teknologi Malaysia Razali Adul Hamid, Universiti Teknologi Malaysia Muzani Mustaffa, Universiti Teknologi Malaysia

iii

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The views expressed in this publication do not necessarily reflect the views of the Faculty of Built

Environment, Universiti Teknologi Malaysia.

The IJBES is an international peer-reviewed Journal Published in collaboration between Faculty of Built Environment and Penerbit UTM

E-ISSN: 2289-8948 ISSN: 1511-1369

iv

IJBES Volume 5, Issue 2, 2018

Table of Contents

1. Evaluating the Critical Success Factors of Industrialised Building System Implementation in

Nigeria: The Stakeholders’ Perception Edo OO, MH Osman, AB Abdul Rahman, N Bakhary

3. Facility Management of Nigerian Universities: Case of University of Lagos, Lagos and the

Bells University of Technology, Ota, Nigeria Oyedeji, Joseph Oyewale

4. An Appraisal into the Potential Application of Big Data in the Construction Industry

SA Ismail, S Bandi, ZN Maaz

5. Investigation of the Use of Energy Efficient Bulbs in Residential Buildings in Ile-Ife, Osun State, Nigeria

Wahab Akeem Bolaji

127-133

134-144

145-154

155-162

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1. Introduction

Big data has been buzzing among many industries around the world on its potential in dissolving most of the industries’ common issues and transform them into a smarter way of operating. The advent of big data era is initiated by the data explosion resulted from the presence of advanced technology in today’s world. According to Waal-Montgomery (2015) prediction, the world’s data volume will rise at approximately 40% per year, and will continue to intensify fifty times from the current volume by the year 2020. The pace in which data is being generated has lead towards data explosion hence big data gain its traction. Basically, big data is often termed based on the 3Vs namely (i) Volume - amount of the data itself, (ii) Velocity – the speed where the data is generated and (iii) Variety – the diversity and complexity of data sources. The construction industry is known to deal with enormous amount of data that reflects the 3Vs and the utilization of these data could be the next frontier for construction industry development.

Peiffer (2016) asserted big data as one of the significant driving factor in configuring the direction which should lead towards improving the industry’s efficiency. Though the construction industry is acknowledged as one of the indicator for economic wellbeing, productivity and

efficiency are at an all-time low which Harenberg (2017) sorely contended in comparison to when it was in the year 1993. This inefficiency, according to Santiago Castagnino, Christoph Rothballer, and Gerbert (2016) was the result of the slow movement made by the industry in adopting new technologies. This is supported by the MGI’s digitization index that put construction sector as the least digitized industry in the world. Santiago Castagnino et al. (2016) added the deliberate changes made by the industry is caused by the insufficient data-driven decision making.

Data is said to be the poster child in enhancing the industry’s productivity. This follows as a real-time data exchange could lead to a broadened insight into the industry’s operational performance thus making way for a smarter working (Peiffer, 2016). However, albeit of the massive amount of data that is generated in the construction industry, the big data is usually siloed and not being fully utilized for a bigger picture. According to Burger (2017), the inefficiencies of data usage is due to the limited ability in dealing with unstructured data such as free text, images or sensors reading. This is where big data could be the saviour in improving the utilization of data.

INTERNATIONAL JOURNAL OF BUILT ENVIRONMENT AND SUSTAINABILITY Published by Faculty of Built Environment, Universiti Teknologi Malaysia

Website: http://www.ijbes.utm.my

IJBES 5(2)/2018, 145-154

An Appraisal into the Potential Application of Big Data in the Construction Industry Siti Aisyah Ismail; Shamsulhadi Bandi and Zafira Nadia Maaz Department of Quantity Surveying, Faculty of Built Environment, Universiti Teknologi Malaysia Email: [email protected]

ABSTRACT The volume of data generated by the construction industry has increased exponentially following an intense use of modern technologies. The data explosion thus lead towards the big data phenomenon which is envisioned to revolutionize the construction like never before. Like any other technologies, big data is a disruptive paradigm and inevitably will give impact to the construction industry. As the industry is refocusing towards an improved productivity, the appeal to embrace big data is certain given the value it offers. This certainly will benefit construction akin to the manufacturing and the retail industry alike. Nevertheless, a review of the literature suggested a limited coverage on the potential application of big data in construction as compared to other industries. This limits understanding of its potential, where the industry is seemingly unaware thus could not relate and extract its real value. Hence, this study aims to draw insights on the specific areas of construction big data research. The research objectives include: (1) to analyse the current extent of construction big data research; (2) to map out the orientation of the current construction big data research; and (3) to suggest the current directions of construction big data research. The qualitative method through a desk study approach has been carried out to attain the first two objectives. It involved a structured review process which covered articles from the online databases assisted by the Nvivo software. This resulted in the theoretical orientation which was conceptualized as: (1) project management; (2) safety (3) energy management; (4) decision making design framework and (5) resource management. The theoretical orientation discovered from the review process will form the basis to suggest the prospective directions of research on big data in construction. This exploration is substantial as a precursor to a much deeper study on big data. As big data is set to influence the industry, the finding made would be a catalyst for creating an awareness to support the development of big data for the construction industry.

History:

Received: 5 April 2018 Accepted: 26 April 2018 Available Online: 31 May 2018 Keywords:

Big Data, Construction Industry, Disruptive Technology, Nvivo, Qualitative research. Corresponding Author Contact: +607-5537378 DOI:

10.11113/ijbes.v5.2.274

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According to the Construction Industry Development Board Malaysia (CIDB), reliable and quality big data is currently in demand to align with the board’s initiatives under the aspiration of the Construction Industry Transformation Programme (CITP). In conjunction with this, it is essential to identify the level of big data needs for the industry. The current move by CIDB is justified as the most typical error made by organizations was to utilize big data without assessing whether their needs could be satisfied by the use of the technology (Portela, Lima, & Santos, 2016). Likewise, Addo-Tenkorang and Helo (2016), added that there appear to be a limited understanding on the value and the potential of big data for construction. This had resulted in a consequential discouragement in the progress for the adoption of big data in construction industry as compared to other industries. Data and the construction industry are indivisible as the industry are dealing with a huge amount of heterogeneous data. This follows as data related to construction industry has been predicted by Bilal, Oyedele, Qadir, et al. (2016) to rise exponentially with the advancement of technologies and the Internet of Things (IoT). According to Addo-Tenkorang and Helo (2016), new opportunities in the form of valuable insights can be developed by excerpting the huge amount of data obtained. Despite, a study that focuses on the potential application of big data particularly in the construction industry has not been comprehensively undertaken (Bilal, Oyedele, Qadir, et al., 2016). This limits understanding of its potential, where the industry is seemingly unaware thus could not relate and extract its real value. Hence, this study aims to draw insights on the specific areas of construction big data research. The research objectives include: (1) to analyse the current extent of construction big data research; (2) to map out the orientation of the current construction big data research; and (3) to suggest the current directions of construction big data research. As big data is set to influence the industry, the research findings would be a catalyst for creating the much-needed awareness to support the development of big data for the construction industry. This would further lead the industry to gear up in developing their capabilities in harnessing the potential of big data as well as encouraging talent and infrastructure development to engage in the forthcoming wave of big data technology in the construction industry.

2. Literature Review 2.1 An overview of Big Data The renowned 3Vs characteristics which form the big data concept were established by one of the Gartner analyst named Laney Doug in 2001. Respectively, the Gartner’s IT Glossary defined big data as a high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making and process automation (Gartner, 2014). With the arrival of big data, data will no longer be viewed as stagnant whose worth is limited to the accomplishment of its gathering purposes (Viktor & Kenneth, 2013). Whereas, in order to cross the boundary of data collecting purposes, the data need to be handled by means of advanced technologies and human skills as well as data entry base. However, according to Akbar (2017), the current amount of digital information had surpassed the ability of the present tools to process it. This situation is described as “The Industrial Revolution of Data” by Joe Hellerstein, a computer scientist at the University of California in Berkeley and it has affected various public and private sectors (Cukier,

2010). Definition of big data might varies in different literature, but the domain of the concept is the 3Vs characteristics. Volume is the most important characteristic that represents the extent of big data magnitude. According to C. P. Chen and Zhang (2014) volume is epitomized as the size of the data itself that are generated by the advanced technologies, networks and human interactions especially on the nets (Hammer, Kostroch, & Quiros, 2017). On the other hand, velocity signifies that data is produced at a remarkably high speed which outstrips the conventional systems (Zikopoulos, Parasuraman, Deutsch, Giles, & Corrigan, 2012). Data velocity is regarded as a supplementary to data volume as greater data volume requires the data processing to

be winged (Özköse, Arı, & Gencer, 2015). As Gartner (2015) has

profoundly predicted, there will be as much as 20.8 billion connected devices by the year 2020 as compared to 6.4 billion as reported in 2016. This shows that the pace of data velocity will continue to speed up following the connected devices’ enhanced features for data streaming (Lee, 2017). Last is variety which means the diversity and complexity of data categories and sources (Zikopoulos et al., 2012). According to Özköse et al. (2015), data may be derived from various resources both internally and externally. Similarly, O'Reilly (2014) emphasized in his book that these data come from an assortment of structures and it is often hard to obtain an impeccably, processing-ready data. Such data can be categorized into structured, semi-structured or unstructured data. This classification of data is derived from the existence of the social network, sensors, mobile devices, GPS and other technological appliances (Portela et al., 2016). 2.2 Current Big Data application in other sectors In recent times, big data has been discussed across various sectors and is considered as a game changer in major industries (Gaitho, 2017). For this reason, many organizations have taken steps to change their plan of action in utilizing the big data value effectively (Akbar, 2017). A survey made by Gartner in 2015 proved that companies have incrementally increased their investment in big data to 75% from 58% recorded by the same survey in 2012. The extensive scope of big data has provided a massive scale of potential and value that can be generated across different sectors such as retail sector, manufacturing as well as the upstream industry. Retail sector is among the earliest to recognise the potential of big data. This follows from the upsurge of e-commerce during the big data 1.0 era (Laney, 2001). During that time retail businesses leveraged the power of basic internet technologies to establish a strong web presence followed by building their capacity to process a large data which was conducive to their efficiency improvements (Provost & Fawcett, 2013). The potential was further extended in analysing the vast amount of data to support decision to expand businesses, improve cost efficiency and revenue forecasting (Meneer, 2015). Manufacturing is another leading sector that has moved towards big data exploration in enhancing their product quality, and at the same time reducing the operational costs (Oracle, 2015). External data especially from social networks and suppliers’ data combined with data from sensors and machines has given valuable insights to the existing information. In this respect, big data was utilized to analyse varieties in enhancing the efficiency of manufacturing and the operational process by providing the bird’s eye view of the processes which led to a better decision making. Apart from that, big data technologies also assist in improving the product quality and reducing the overall cost through production and quality data analysis

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along with customers’ returning data, capacity consumption as well as machinery efficiency (Oracle, 2015). The oil and gas industry has also gained a lot from big data. According to B. Mathew (2016), in the current situation, data collected particularly in the operational process is used mainly for detection and control purposes. Big data’s advanced analytics assisted in the decision making where big data insights were used to plan for predictive maintenance. In this case, it was reported that the technology has managed to bring the maintenance cost down to about 13% (Choudhry, Mohammad, Tan, & Ward, 2016). The benefits of digital monitoring and predictive maintenance extends towards detecting errors on equipment and performing maintenance before they are entirely damaged. It was reported by analytics firm, Kimberlite that an approximately $49 million annually were wasted due to an unplanned downtime (Choudhry et al., 2016). Hence, big data in this respect helped to enhance production and addressed the financial impacts before it eventually occurs. 2.3 Big Data and the Construction Industry Construction is one of the major industry that is responsible towards a country development. The construction works to be carried out in a project is dynamic (Wood, 2016) and involve a high volume of data exchange from various stakeholders to be gathered and processed (Shrestha, 2013). Shrestha (2013) added that data is generated throughout the various phases of construction projects from planning phase to completion. As shown in Table 1, the stream of data includes design and financial data, sensors and equipment data, photos and videos and others. This data is often large in volume, highly diverse in format and dynamic. The multi faceted data reflects the multitude characteristics of data streaming from construction activities thus sits in comformity with the 3V’s concept of big data.

Further, Table 1 shows that the advancement of construction processes through the widespread utilization of these data shall be the next frontier of construction industry innovation and productivity. This is supported by Harenberg (2017) who mentioned real-time data processing as the future booster of productivity in construction. 2.4 Triggering Constituents of Big Data in the Construction

Industry The digitalized revolution has impacted the construction industry rather significantly as the industry is dealing with heterogeneous amount of data (Bilal, Oyedele, Qadir, et al., 2016). These triggering contituents to big data are identified and discussed as the following:

Characteristics Contributors Examples

Volume Large volume of data from different sources

Design data, cost data, financial data, contractual data, Enterprise Resource Planning (ERP) system, etc

Variety Diversity in the content format

DWG (drawing), DXF (drawing exchange format), DGN (design), RVT (revit), ifcXML, ifcOWL, DOC/XLS/PPT (Microsoft format), RM/MPG (videos), JPEG (images)

Velocity Dynamic nature of data sources

Sensors, RFIDs, Building Management System (BMS)

Table 1 Big Data context in Construction Industry

Source : Aouad, Kagioglou, Cooper, Hinks, and Sexton (1999); Bilal, Oyedele, Qadir, et al. (2016)

2.4.1 Building Information Modelling (BIM) BIM is anticipated to capture the multi-dimensional CAD data to deliberately support the multidisciplinary and coordinated working environment among the stakeholders involved in a project (Eadie, Browne, Odeyinka, McKeown, & McNiff, 2013). As BIM involves with capturing the additional layers of information throughout the entire building lifecycle, BIM is perceived to transform the construction industry across various perspectives (Azhar, 2011). Though data volume has been the characteristic of BIM, yet Humphreys (2016) argued that this data are not precisely big data. This follows as the huge files of BIM with the combination of the numerous models is still promptly prepared only to be processed by BIM applications. Likewise, the arrival of built-in devices and sensors has increased the amount of data generated where it eventually leads to the wellsprings of Big BIM Data (Bilal, Oyedele, Qadir, et al., 2016). Thus, this triggers the construction industry to penetrate the big data era. 2.4.2 Cloud Computing Cloud computing is an internet computing trend which on request, give access to the merge of configurable resources (Bughin, Chui, & Manyika, 2010). The main purpose is to provide multiple users with access to data storage and computation without each having to resort for an individual license. The acceleration of cloud computing technology has contributed to the evolution of big data (Qubole, 2017). As cloud computing is supporting the coordination of errands in the BIM-based application, it has been broadly applied in the construction industry and big data performance in this revolution is astounding (Bilal, Oyedele, Qadir, et al., 2016). In addition, cloud computing and big data are said to be an ideal combo that contributes to the cost efficiency and extensible infrastructure in supporting Big Data and Business Analytics (Ferkoun, 2014). 2.4.3 Internet of Things (IoT) The Internet of Things (IoT) has been the main pillar that triggers the big data 3.0 era. Basically, IoT is a system of Internet-connected devices that gather and transfer data through installed sensors (Meola, 2016). IoT application frequently conveyed a substantial number of sensors devices for data accumulation. As the industry presents boundless big data utilization cases for IoT, big data is inalienably the subject of intrigue (Bilal, Oyedele, Qadir, et al., 2016). Among the prominent areas of IoT applications includes logistics, transport, asset recording, intelligent homes and buildings, energy and agriculture. Bilal, Oyedele, Qadir, et al. (2016) claimed that IoT and big data are interdependent trends where a huge amount of data is created, accessed and analysed in real-time in construction applications. Additionally, Pal (2015) suggested that during the selection of big data processing technology, huge flood of information produced by IoT triggers big data on a reciprocal basis following the selection of big data processing technology. 2.4.4 Smart Buildings Smart Building technology assimilates the contemporary technologies with existing building systems to attract the economical trade-off between comfort maximization and energy reduction (Khan & Hornbæk, 2011). Often, these systems will produce an enormous volume of data and the greater part of this information often stay undiscovered and eventually disposed of. According to Bilal, Oyedele,

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Qadir, et al. (2016), this data needs to be interpreted to truly reflect smart buildings hence gives big data analytics a significant role to play. The information and communication technology (ICT)-based integration and development systems, particularly Internet of Things is an important catalyst for various applications, both industry and the general population in realizing the smart buildings (Perera, Zaslavsky, Christen, & Georgakopoulos, 2014). In this sense, Moreno et al. (2016) opined that big data and IoT are an impeccable combination in enhancing energy efficiency for Smart Buildings. 2.4.5 Augmented Reality (AR) Augmented Reality is a technology that coordinates virtual object images into real-world images. These images can be taken from the camera or, by using a live view, the audience can be added directly to the world (Reiners, Stricker, Klinker, & Müller, 1998). According to Jiao, Zhang, Li, Wang, and Yang (2013) AR comes from ‘Virtual Reality’ (VR) and provides a half-depth environment that highlights the exact alignment between actual scenes and virtual world images in real time. It is also broadly recognized as an assuring technology to improve human viewpoint. Additionally, the means to enhance prevailing big data visualization techniques is correlated with AR and VR where it is relevant for human limited perception capabilities (Olshannikova, Ometov, Koucheryavy, & Olsson, 2015). Consequently, AR and big data are certainly unavoidable where the complexity related with big data in construction is tremendous and must be overcome by advanced visualization methods, specifically AR and VR (Bilal, Oyedele, Qadir, et al., 2016). 2.4.6 Social Networking Services Social media is one of the exciting trends that could assist the construction industry to improve the communication among project teams (Jiao, Wang, et al., 2013). Yet, one of the main challenge is to accede the value and exploring ways of analysing it (H. Chen, Chiang, & Storey, 2012). This follows from the enormous volume of heterogeneous data produced by the social networks. Hence, to properly analyse data from social media, the analytical techniques of data analysis need to be modified and incorporated into the new enormous data for enormous information processing (Bello-Orgaz, Jung, & Camacho, 2016). In relation to this, big data can be utilized in developing appealing domain applications through the high volume, velocity, and variety of social network data to improve stakeholders’ productivity. 2.5 Current Big Data research in the Construction Industry Big data has begun to set foot in the construction industry in sync with other sectors that have long benefited from big data. In this regard, the construction industry could exploit big data in the same manner as anticipated by the other sectors or industries. As discussed earlier, this includes enhancing efficiency, decision making, and sensors monitoring. Bilal, Oyedele, Qadir, et al. (2016) maintained that the outlook on the applicability of big data in construction could be magnified as the triggering contituents discussed in section 2.4 advanced. Thus, the surge of these contituents and trends could be the factors to propel the construction industry to the next level of data driven initiatives. The current big data research or application excerpted from various literature is summarized in Table 2 with the important concepts identified from the review process are aggregated and accentuated in brackets. The findings will become the basis to map the orientation of

big data research in construction and subsequently suggesting the probable direction for research to ensue.

3. Research Methodology The qualitative research design was adopted for this study. According to Bryman (2008), qualitative research is a research strategy that typically emphasizes on words rather than the computation of data. In this regard, the aim is to provide a thick explanation about a phenomena following the specific issue identified from the the literature (Elo & Kyngas, 2008; Fellows & Liu, 2008). The decision for adopting to the strategy was also guided by the objectives of the study. As the research objectives include analysing the current extent of big data research and mapping out its orientation and potential application, these are better achieved by going deep through an analytical explanation of the existing research (Creswell, 2005). Desk study method was used to collect the data required for attaining the first and the second objectives. According to Travis (2016) desk study relied on the researcher’s skill to review the previous research findings in order to obtain an expansive comprehension of the study area. This method was adopted as it provides the fastest and inexpensive method in understanding the realm of the research, where a thorough review was made to obtain a cross sectional insights on big data in the construction industry. As the study is currently on going, a series of interviews are planned to consolidate and validate the insights that are to be gained from the desk study. The interviews are planned to be administered with personnel who have experienced big data and is aimed to identify the potential application of big data in construction. According to Rubin and Rubin (2011), the qualitative interview is a discussion where the researcher aides a conversational accomplice in a broadened exchange. The interviews will allow the researcher to expand the questions to the extent that they are willing to share. Accordingly, the desk study is important in this regard as it gives the researcher a gist of the previous research findings before the interviews are carried out. For this reason, this paper is organised to highlight the analytical method employed in the desk study and the findings derived therein. These are concurently presented and discussed in the ensuing sections.

4. Findings and Discussion The important concepts on big data excerpted from the review were structurally analysed by following the steps in the framework known as SALSA. The acronym stands for Search, Appraisal, Synthesis, and Analysis and was introduced by Booth, Sutton, and Papaioannou (2016). A complete application of the SALSA framework was illustrated in a study by Shamsulhadi, Fadhlin, and Hamimah (2015) and was further methodologically discussed by Shamsulhadi and Fadhlin (2016) and Zafira, Shamsulhadi, and Roslan (2018). In the studies mentioned, it was observed that the Nvivo software was predominantly deployed to assist in the analytical process. Part of the analytical outcomes as presented in Table 2 had followed the processes as outlined by the previous studies and include the usage of the Nvivo software as well. This approach was intentional to maintain the rigour as justified in the illustrated research. Details of the processes carried out for the study are further explained in the following sections. 4.1 Searching The exploratory nature of this study had naturally required the

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researcher to search the relevant literature concerning big data in construction. For this purpose, the researcher had first established the search parameter and subsequently drawn the relevant keywords from the aim and objectives of the study. A snowballing technique was then exercised where literatures were identified through the backward and forward approaches (Webster & Watson, 2002). To achieve this, the UTM Library Online Database which contained access to academic journals from Emerald, Science Direct, IEEE Xplore Digital Library, and Springerlink was searched. The main keywords used

in searching the literature were “big data” and “construction industry”. Additionally, the Boolean operators, truncation characters and wildcards were also used in selecting the relatable journal articles. Based on the search results, a large numbers of big data articles were displayed from both construction as well as other domains. However, the results were again filtered where only the content that portrays the presence of big data in the construction industry was of particular interest.


1 BD with Visual Analytics used for (building performance) comparison that leads to renovation and construction with low (energy) consumption.

(Ioannidis et al., 2015)

2 LEED uses actual data to verify the (building performance) (Davis, 2015)

3 Improve (project management) by using technologies or sensors for (performance) monitoring and tracking (Wood, 2016), (Bleby, 2015), (Yang, Park, Vela, & Golparvar-Fard, 2015)

4 Cost efficiency (design) through a real-time, data-focused predictive model. (Sadhu, 2016)

5 BD assist in (project management) to ensure the project is delivered on (time) and (minimize delays) (Sadhu, 2016), (Rijmenam, 2015), (Faure, 2016), (Augur, 2016), (Akbar, 2017)

6 Real-time data sharing to improve (communication) between stakeholders (Rijmenam, 2015), (Augur, 2016)

7 Resource tracking through sensors-equipped assets or machineries. (resource management) (Rijmenam, 2015), (Augur, 2016), (Azzeddine Oudjehane & Moeini, 2017), Akhavian and Behzadan

8 Deriving information from stakeholders to improve the (planning) process and (project management) (Caron, 2015)

9 Integration of information technologies with data handling in facilitating (decision-making) for (project man-agement)

(Martínez-Rojas, Marín, & Vila, 2015)

10 BD generate (prediction) system for construction businesses bankruptcy (Hafiz et al., 2015)

11 Drones use for construction progress monitoring for (project management) (Azzeddine Oudjehane & Moeini, 2017), Knight (2015)

12 Geospatial/geo-location data for (resources optimization) and (resource management) (Akbar, 2017)

13 Data simulation tool in reducing project (risk). (Akbar, 2017)

14 BD for construction (cost management) through tender price assessment system (project management) (Y. Zhang, Luo, & He, 2015)

15 Visual BD to improve (communication) among project stakeholders. (K. K. Han & Golparvar-Fard, 2017)

16 Assess (Construction waste management) performance using BD (Lu, Chen, Ho, & Wang, 2016), (Lu, Chen, Peng, & Shen, 2015)

17 Developing (waste) simulation tool using BD for (Construction waste management) (Bilal, Oyedele, Akinade, et al., 2016)

18 Social network analysis and (energy) usage analyses as sources in establishing an integrated green building (design) model

Redmond, El-Diraby, and Papagelis (2015)

19 BD algorithms to accurately reduce the design space and enabled generative (design) tool (Bilal, Oyedele, Qadir, et al., 2016)

20 BD and VR for better building (design) decision (Bernstein, 2017), (Barista, 2014)

21 BD helps in generating a predictive model for (energy) consumption (Moreno et al., 2016)

22 BD algorithm for (building performance) in terms of (energy) consumption (P. A. Mathew et al., 2015)

23 Implementing prototype software called Project Dasher for (energy) data visualization and real-time monitoring. (Khan & Hornbæk, 2011)

24 BD analysis used to understand energy consumption behavior thus help to improve (energy efficiency) in build-ing

(Koseleva & Ropaite, 2017), (Janda et al., 2015)

25 Real-time (energy) consumption data monitoring and control to improve energy efficiency (Wei & Li, 2011)

26 BD-based platform to visualize workers’ unsafe (safety) act in real-time (SY Guo, Ding, Luo, & Jiang, 2016), (Shengyu Guo, Luo, & Yong, 2015)

27 Use wearable to track worker proximity to rolling (safety) exclusionary zones (Wood, 2016)

28 Use drones to check on site (safety) (Oudjehane & Moeini, 2017)

29 Real-time (safety) tracking and data visualization technologies improve (safety) understanding. (Teizer, Cheng, & Fang, 2013) (Hampton, 2015)

30 Application of BD-driven BIM system in improving construction (safety) (S. Zhang, Teizer, Lee, Eastman, & Venugopal, 2013)

31 Integrating BIM data with external data such as Linked Open Data (LOD) for better (project management) and reduce project (risk)

(Curry et al., 2013)

32 Sensor based fire-fighting system for skyscraper building in associate with the authorities help in fire detecting as well as evacuation process (safety)

(Stankovic, 2014)

33 Predicting site injury and workers’ behavior towards (safety) through 3D skeleton motion model from videos. (S. Han, Lee, & Peña-Mora, 2012)

34 Data from robotics and automated equipment has the potential to improve job (safety) and enhance construction (productivity).

(Skibniewski & Golparvar-Fard, 2016)

35 Capturing (safety), quality and performance data for real-time analysis in improving site (safety) and construction work (productivity).

(Bleby, 2015)

36 Big Data from mobile apps for contractor to track (resource) and document schedule changes to enhance (resource management).

(Sadhu, 2016)

37 (Energy) consumption prediction through computational models developed based on user behavior for better (energy management).

(C. Chen & Cook, 2012)

38 BD in (design) model comprises of architectural, structural, and building services data to enhance (design)

efficiency (Porwal & Hewage, 2013)

39 Past project data-driven (design) to improve (design) decision and efficiency. (Barista, 2014)

Table 2 Big Data research from various literature

Source: As shown

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4.2 Mapping Ideas and Analysis Mapping involves putting together different strands that make up the topic to enable analysis and synthesis to be undertaken. The process involves accumulating the literature content from the review and sorting the list into categories for the purpose of establishing connections (Hart, 1998). According to Hart (1998), the aim of this process is to dynamically reduce the huge amount of information extracted from the review with due emphasized given to extract the main points of the argument. For this study, a featured map, in a form of a table proposed by Hart (1998) was developed and showed in Table 2. The table showed the results of the analysis which has taken place by reflecting the words (or terms) derived from the extracted data. These were reflected as the features which had characterised the literature and a structural form of recognition of the leading concepts. Despite, at this stage, it appears that the concepts derived were rather disjointed and had followed the individual reflection from the sources. This necessitates the next step in the process - synthesis. 4.3 Synthesis, Mapping and Discussion of the Outcomes Concepts that arised from the analysis were synthesized through the aggregative approach in which the concepts were grouped into relatable themes or area. This process was carried out by using the Nvivo software where apart from its ability in mapping out the outcome, proved to be useful in espousing the weightage which could exaggerated certain number of concepts. The frequently mentioned concepts were mapped out through the word frequency command. It counts the frequency of a particular word or phrase or a set of alternative words fed from the analysis. In relation to this study, the ‘Word Frequency Query’ in Nvivo was used to reveal a specified concepts of big data that have been mentioned the most. Hence, the predilections of big data in construction were obtained thus attaining the second objective. A model which was developed from the synthesis is presented in Figure 1. It shows that prior research on big data in construction had centered around ‘management’ especially ‘project’ management, ‘energy’ management and ‘resource’ management. In this context, big data in ‘project management’ involves those linked-construction data in cloud base that provides broad understanding on complex project. It was submitted that big data leads to a better ‘project management’, especially in ensuring that cost efficiency was achieved as well as minimizing delays. Likewise, big data initiated by the IoT devices such as drones, sensors or smartphones aid in recording construction work progress and monitoring work performance. It was postulated that a real-time data was able to be provided so that actionable actions could be

taken in enhancing the project productivity. Additionally, the IoT devices also generates data on the ‘safety’ aspect such as workers’ safety behaviour on site and site safety conditions through sensors, automated equipments, tracking devices as well as visualization technologies. Big data also contribute to a better project management through data wise enhancing ‘decision-making’ process especially in predicting the project orientation that leads to lower project risk. On the other hand, ‘energy management’ encompasses the integration of IoT or BIM with big data analytics in understanding the building energy consumption to increase energy efficiency and add to building performance. Energy analyses further assist in decision making ‘design’ framework where the results could be the determinant in generating integrated models for building design. Also, big data provide an aerial view on all aspects of the built environment that facilitates a better decision-making design framework. Correspondingly, resources tracking and monitoring through sensors or mobile apps helped to enhance the decision-making for ‘resources management’ and ensure resource optimization. Other big data potential application reviewed from the literature includes construction waste management as well as data-sharing efficiency to improve communication. Based on the discussion, the theoretical orientations obtain from the analytical processes could be summarised as: (1) project management; (2) safety; (3) energy management; (4) decision making design framework and (5) resource management. Table 3 recapitulates the interpretative context of the most frequent big data research area in relation to the findings previously presented in Table 2. The findings from this study had revealed five current directions of construction big data research. Despite being bounded with the number of articles that were obtainable from the search, the findings nevertheless had shed some lights on the areas currently being pursued by researchers in construction domain. This information could be harnessed by the current and future researcher in charting their path and further justifying the significance of their research.

5. Conclusion and Recommendation The study has managed to draw important insights on the specific areas in construction big data research. These were achieved through the accomplishment of the following objectives: (1) to analyse the current extent of construction big data research; (2) to map out the orientation

Figure 1: Generated model representing the frequency of big data research area

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of the current construction big data research; and (3) to suggest the current directions of construction big data research. As the foregoing discussions have shown, a structured analytical framework has been employed to analyse the resources obtained, assisted by the used of NVivo. This has permitted a wider inclusion of resources, thus had broadened the base for the qualitative analysis to take place. As the study has shown, the current extent and orientation of the present construction big data research covers a diverse research area. It reflects from the analysis that big data research on monitoring, tracking and decision making are intensively being pursued by researcher in construction. Apparently, this suggests the rapid pace of big data development in construction and the on-going interest to harness the technology for common good. Besides, the study had also suggested that the current directions of construction big data research could be translated into five specific areas. This covers construction project management; safety; energy management; decision making design framework and resource management. Of the five areas mentioned, big data for construction project management was identified as the area which research is really intensified. This follows as the construction industry is a data-dependent industry hence data must be managed efficiently with the right tool to ensure the success of a project. As the study has shown, construction big data research offers a potentially good prospect to improve the industry. It is a step ahead of the current digitalisation effort and bring a new wave in obtaining insights from the voluminous amount of data. As the study reported in this paper is still on-going, it is interesting to contemplate the industry’s views on the findings discussed here. This shall include what and how would the industry profit from the adoption of big data. The authors recommend a study to be conducted on the challenges impeding the adoption of big data in construction as well as readiness in embracing to big data. This effort shall increase the depth and breadth of the current knowledge which could further bolster the industry’s understanding on big data.

Acknowledgements: The authors wish to thank the anonymous reviewers for their constructive comments and effort to review this paper. References Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528-543.

Akbar, S. (2017). Is Big Data The Most Trending Thing In Construction Now. Retrieved from https://geniebelt.com/blog/big-data-in-construction

Akhavian, R., & Behzadan, A. H. (2015). Construction Equipment Activity Recognition for Simulation Input Modeling using Mobile Sensors and Machine Learning Classifiers. Advanced Engineering Informatics, 29(4), 867-877. doi:10.1016/j.aei.2015.03.001

Aouad, G., Kagioglou, M., Cooper, R., Hinks, J., & Sexton, M. (1999). Technology management of IT in construction: a driver or an enabler? Logistics Information Management, 12(1/2), 130-137.

Augur, H. (2016). Big Data is Transforming Commercial Construction. Retrieved from http://dataconomy.com/2016/07/big-data-is-transforming-commercial-construction/

Azhar, S. (2011). Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadership and management in engineering, 11(3), 241-252.

Azzeddine Oudjehane, & Moeini, S. (2017). Big Data in Construction Projects Risk and Opportunity Management.

Barista, D. (2014). The Big Data revolution: How data-driven design is transforming project planning. Building Design+Construction.

Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59.

Bernstein, P. (2017). How Big Data and VR in Architecture Will Greatly Improve Design. Retrieved from https://www.autodesk.com/redshift/big-data-vr-in-architecture/

Bilal, M., Oyedele, L. O., Akinade, O. O., Ajayi, S. O., Alaka, H. A., Owolabi, H. A., . . . Bello, S. A. (2016). Big data architecture for construction waste analytics (CWA): A conceptual framework. Journal of Building Engineering, 6, 144-156.

Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., . . . Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500-521. doi:10.1016/j.aei.2016.07.001

Bleby, M. (2015). Big Data boosts construction. Retrieved from http://www.afr.com/real-estate/commercial/big-data-boosts-constrcution-20150109-12l8xx

Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review: Sage.

Bryman, A. (2008). Mixed methods research: combining quantitative and qualitative research. Social research methods, 3, 608-626.

Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75-86.

Burger, R. (2017). How the Construction Industry is Using Big Data. Retrieved from https://www.thebalance.com/how-the-construction-industry-is-using-big-data-845322

Context of big data research

Important key-words


Construction Pro-ject Management



Decision-making Making decision using predictive data that leads to lower project risk and

Safety Site safety, work-ers’ safety behav-

iour

Big data generated through IoT devices in tracking and visualize site safety conditions as well as workers’ behav-iour towards safety

Energy management Consumption, building perfor-

mance

Enhancing energy efficiency and build-ing performance through an under-standing of building energy consump-tion



Resource manage-ment

Resources tracking Resources tracking through IoT devices to improve resources utilization effi-ciency

Table 3 The context of big data research area and details

Source : Researcher

https://geniebelt.com/blog/big-data-in-construction










152

Caron, F. (2015). Data management in project planning and control. International Journal of Data Science, 1(1), 42-57.

Chen, C., & Cook, D. J. (2012). Behavior-Based Home Energy Prediction. IEEE Internet of Things Journal.

Chen, C. P., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).

Choudhry, H., Mohammad, A., Tan, K. T., & Ward, R. (2016). The next frontier for digital technologies in oil and gas. Retrieved from https://www.mckinsey.com/industries/oil-and-gas/our-insights/the-next-frontier-for-digital-technologies-in-oil-and-gas

Creswell, J. (2005). Planning, conducting, and evaluating quantitative and qualitative research: Educational research: Prentice Hall: Upper Saddle River, NJ Creswell, JW (2007). Research method: Qualitative, quantitative and mixed methods approaches (2nd ed.). Thousand Oaks, CA: Sage.

Cukier, K. (2010). Data, data everywhere: A special report on managing information: Economist Newspaper.

Curry, E., O’Donnell, J., Corry, E., Hasan, S., Keane, M., & O’Riain, S. (2013). Linking building data in the cloud: Integrating cross-domain building data using linked data. ADVANCED ENGINEERING INFORMATICS, 27(2), 206-219.

Davis, D. (2015). How Big Data is Transforming Architecture.

Eadie, R., Browne, M., Odeyinka, H., McKeown, C., & McNiff, S. (2013). BIM implementation throughout the UK construction project lifecycle: An analysis. Automation in Construction, 36, 145-151.

Elo, S., & Kyngas, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115.

Faure, A. (2016). Big data is revolutionizing business: the construction industry in general and building sites in particular. Retrieved from https://blog.serenacapital.com/big-data-is-revolutionizing-business-the-construction-industry-in-general-and-building-sites-in-de7a53a20516

Fellows, R., & Liu, A. (2008). Research Methods for Construction (3rd. ed.). Oxford: Wiley-Blackwell.

Ferkoun, M. (2014). Cloud Computing and Big Data: An ideal combination. Retrieved from https://www.ibm.com/blogs/cloud-computing/2014/02/cloud-computing-and-big-data-an-ideal-combination/

Gaitho, M. (2017). How Applications of Big Data Drive Industries. Retrieved from https://www.simplilearn.com/big-data-applications-in-industries-article

Gartner. (2014). Glossary.(2013). Big Data.

Gartner. (2015). 6.4 billion connected “Things” will be in use in 2016, up 30 percent from 2015. Gart. Inc.

Guo, S., Ding, L., Luo, H., & Jiang, X. (2016). A Big-Data-based platform of workers’ behavior: Observations from the field. Accident Analysis & Prevention, 93, 299-309.

Guo, S., Luo, H., & Yong, L. (2015). A big data-based workers behavior observation in China metro construction. Procedia Engineering, 123, 190-197.

Hafiz, A., Lukumon, O., Muhammad, B., Olugbenga, A., Hakeem, O., & Saheed, A. (2015). Bankruptcy prediction of construction businesses: towards a big data analytics approach. Paper presented at the Big Data Computing Service and Applications (BigDataService), 2015 IEEE First International Conference on.

Hammer, C., Kostroch, D. C., & Quiros, G. (2017). Big Data; Potential, Challenges and Statistical Implications. Retrieved from

Hampton, T. V. (2015). Big Data Boosts Safety in Brazil. Retrieved from https://www.enr.com/articles/9640-big-data-boosts-safety-in-brazil?v=preview

Han, K. K., & Golparvar-Fard, M. (2017). Potential of big visual data and building information modeling for construction performance analytics: An exploratory study. Automation in Construction, 73, 184-198.

Han, S., Lee, S., & Peña-Mora, F. (2012). A machine-learning classification approach to automatic detection of workers' actions for behavior-based safety analysis Computing in Civil Engineering (2012) (pp. 65-72).

Harenberg, M. (2017). 5 ways to boost construction productivity. Retrieved from https://www.planradar.com/construction-productivity/

Hart, C. (1998). Doing a literature review: Releasing the social science research imagination: Sage.

Humphreys, R. (2016). How Big Data Complements Information Management. Retrieved from https://sourceable.net/how-big-data-complements-information-management/

Ioannidis, D., Fotiadou, A., Krinidis, S., Stavropoulos, G., Tzovaras, D., & Likothanassis, S. (2015). Big Data and Visual Analytics for Building Performance Comparison. Paper presented at the IFIP International Conference on Artificial Intelligence Applications and Innovations.

Janda, K. B., Patrick, J., Granell, R., Bright, S., Wallom, D., & Layberry, R. (2015). A WICKED approach to retail sector energy management. Paper presented at the Proceedings of ECEEE Summer Study.

Jiao, Y., Wang, Y., Zhang, S., Li, Y., Yang, B., & Yuan, L. (2013). A cloud approach to unified lifecycle data management in architecture, engineering, construction and facilities management: Integrating BIMs and SNS. Advanced Engineering Informatics, 27(2), 173-188.

Jiao, Y., Zhang, S., Li, Y., Wang, Y., & Yang, B. (2013). Towards cloud augmented reality for construction application by BIM and SNS integration. Automation in Construction, 33, 37-47.

Khan, A., & Hornbæk, K. (2011). Big data from the built environment. Paper presented at the Proceedings of the 2nd international workshop on Research in the large.

Knight, W. (2015). New boss on construction sites is a drone: MIT Technology review.

Koseleva, N., & Ropaite, G. (2017). Big Data in Building Energy Efficiency: Understanding of Big Data and Main Challenges. Procedia Engineering, 172, 544-549.

Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6, 70.

Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303.

Lu, W., Chen, X., Ho, D. C., & Wang, H. (2016). Analysis of the construction waste management performance in Hong Kong: the public and private sectors compared using big data. Journal of Cleaner Production, 112, 521-531.

Lu, W., Chen, X., Peng, Y., & Shen, L. (2015). Benchmarking construction waste management performance using big data. Resources, Conservation and Recycling, 105, 49-58.















153

Martínez-Rojas, M., Marín, N., & Vila, M. A. (2015). The role of information technologies to address data handling in construction project management. Journal of Computing in Civil Engineering, 30(4), 04015064.

Mathew, B. (2016). How Big Data is reducing costs and improving performance in the upstream industry. Retrieved from http://www.worldoil.com/news/2016/12/13/how-big-data-is-reducing-costs-and-improving-performance-in-the-upstream-industry

Mathew, P. A., Dunn, L. N., Sohn, M. D., Mercado, A., Custudio, C., & Walter, T. (2015). Big-data for building energy performance: Lessons from assembling a very large national database of building energy use. Applied Energy, 140, 85-93.

Meneer, D. (2015). How Big Data is Transforming Construction Businesses. Retrieved from https://www.constructionmonitor.com/blog/2015/06/26/how-big-data-is-transforming-construction-businesses/

Meola, A. (2016). What is the Internet of Things (IoT)? Retrieved from http://www.businessinsider.com/what-is-the-internet-of-things-definition-2016-8/?IR=T

Moreno, M. V., Dufour, L., Skarmeta, A. F., Jara, A. J., Genoud, D., Ladevie, B., & Bezian, J.-J. (2016). Big data: the key to energy efficiency in smart buildings. Soft Computing, 20(5), 1749-1762.

O'Reilly. (2014). Big data now: 2014 Edition: O'Reilly Media, Incorporated.

Olshannikova, E., Ometov, A., Koucheryavy, Y., & Olsson, T. (2015). Visualizing Big Data with augmented and virtual reality: challenges and research agenda. Journal of Big Data, 2(1), 22.

Oracle. (2015). Improving Manufacturing Performance with Big Data: Architect’s Guide and Reference Architecture Introduction. Retrieved from http://www.oracle.com/us/technologies/big-data/big-data-manufacturing-2511058.pdf

Oudjehane, A., & Moeini, S. (2017). Big data in construction projects: Risk and opportunity management.

Özköse, H., Arı, E. S., & Gencer, C. (2015). Yesterday, today and tomorrow of big data. Procedia-Social and Behavioral Sciences, 195, 1042-1050.

Pal, K. (2015). The Impact of the Internet of Things on Big Data. Retrieved from http://data-informed.com/the-impact-of-internet-of-things-on-big-data/

Peiffer, E. (2016). 3 concepts that will shape the future of construction. Retrieved from https://www.constructiondive.com/news/3-concepts-that-will-shape-the-future-of-construction/417049/

Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Sensing as a service model for smart cities supported by internet of things. Transactions on Emerging Telecommunications Technologies, 25(1), 81-93.

Portela, F., Lima, L., & Santos, M. F. (2016). Why Big Data? Towards a Project Assessment Framework. Procedia Computer Science, 98, 604-609.

Porwal, A., & Hewage, K. N. (2013). Building Information Modeling (BIM) partnering framework for public construction projects. Automation in Construction, 31, 204-214.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.

Qubole. (2017). Big Data Cloud Database & Computing. Retrieved from https://www.qubole.com/resources/big-data-cloud-database-and-computing/

Redmond, A., El-Diraby, T., & Papagelis, M. (2015). Employing an Exploratory Research Stage to Evaluate Green Building Technologies for Sustainable Systems. Submitted to international journal; under review.

Reiners, D., Stricker, D., Klinker, G., & Müller, S. (1998). Augmented reality for construction tasks: Doorlock assembly. Proc. IEEE and ACM IWAR, 98(1), 31-46.

Rijmenam, M. v. (2015). Big Data Can Help Construction Companies Deliver Projects On Time. Retrieved from https://datafloq.com/read/big-data-construction-companies-deliver-projects-t/143

Rubin, H. J., & Rubin, I. S. (2011). Qualitative interviewing: The art of hearing data: Sage.

Sadhu, R. (2016). Big Data For Construction: How It Can Help You Deliver Your Project On Time. Retrieved from https://www.planacademy.com/big-data-construction/

Santiago Castagnino, Christoph Rothballer, & Gerbert, P. (2016). What's the future of the construction industry? Retrieved from https://www.weforum.org/agenda/2016/04/building-in-the-fourth-industrial-revolution/

Shamsulhadi, B., & Fadhlin, A. (2016). Documenting its application in Quantity Surveying research: A Review. International Journal of Built Environment and Sustainability, 3(1), 10-17. doi:10.11113/ijbes.v3.n1.105

Shamsulhadi, B., Fadhlin, A., & Hamimah, A. (2015). Research into the Bills of Quantities: Where can it be focused. The Malaysian Surveyor Journal, 50(3), 15-24.

Shrestha, J. (2013). Big Data, Predictive Analytics, and Data Visualization in The Construction Engineering. CCEE Graduate Student Research Showcase and Poster Competition.

Skibniewski, M., & Golparvar-Fard, M. (2016). Toward a Science of Autonomy for Physical Systems: Construction. arXiv preprint arXiv:1604.03563.

Stankovic, J. A. (2014). Research directions for the internet of things. IEEE Internet of Things Journal, 1(1), 3-9.

Teizer, J., Cheng, T., & Fang, Y. (2013). Location tracking and data visualization technology to advance construction ironworkers' education and training in safety and productivity. Automation in Construction, 35, 53-68.

Travis, D. (2016). Desk research: the what, why and how. Dostupno na http://www. userfocus. co. uk/articles/desk-research-the-what-why-and-how. html, pristupljeno, 10, 2017.

Viktor, M.-S., & Kenneth, C. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.

Waal-Montgomery, M. d. (2015). World's data volume to grow 40% per year & 50 times by 2020: Aureus. Retrieved Syyskuu, 28, 2016.

Webster, J., & Watson, R. T. (2002). Analysing the past to prepare for the future: writing a literature review. MIS Quarterly, 26(2), 1-11.

Wei, C., & Li, Y. (2011). Design of energy consumption monitoring and energy-saving management system of intelligent building based on the Internet of things. Paper presented at the Electronics, Communications and Control (ICECC), 2011 International Conference on.

Wood, C. (2016). Betting on big data: How construction firms are leveraging digitized job sites. Retrieved from https://www.constructiondive.com/news/betting-on-big-data-how-construction-firms-are-leveraging-digitized-job-si/431047/

Yang, J., Park, M.-W., Vela, P. A., & Golparvar-Fard, M. (2015). Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future. ADVANCED ENGINEERING INFORMATICS, 29(2), 211-224.






















http://www

http://www




154

Zafira, N. M., Shamsulhadi, B., & Roslan, A. (2018). Potential opportunities and future directions of big data in the construction industry. Paper presented at the The 12th SEATUC Symposium 2018, Universitas Gadjah Mada, Yogyakarta.

Zhang, S., Teizer, J., Lee, J.-K., Eastman, C. M., & Venugopal, M. (2013). Building information modeling (BIM) and safety: Automatic safety checking of construction models and schedules. Automation in Construction, 29, 183-195.

Zhang, Y., Luo, H., & He, Y. (2015). A system for tender price evaluation of construction project based on big data. Procedia Engineering, 123, 606-614.

Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2012). Harness the power of big data The IBM big data platform: McGraw Hill Professional.

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