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Opportunity Analysis for Enterprise Collaboration between Network of SMEs Presenter: M. Naeem Supervisor: Abdelaziz Bouras, Yacine Ouzrout, Néjib Moalla Laboratoire Décision et Information pour les Systèmes de Production (DISP),Université Lumière Lyon 2, France 27-May-2015 1

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Page 1: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Opportunity Analysis for Enterprise Collaboration between Network of SMEs

Presenter: M. Naeem Supervisor: Abdelaziz Bouras, Yacine Ouzrout, Néjib Moalla Laboratoire Décision et Information pour les Systèmes de Production (DISP),Université Lumière Lyon 2, France

27-May-2015 1

Page 2: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Agenda

Background

Context of Research

Challenge & Opportunities

Objective

Research Problem

Expected Results

Related Work

Proposed Framework

Results

Pig/Hive Results

Enterprise Collaboration Functional Flow

Enterprise Collaboration Big Data Capability Results

Ontological Modeling Results

Asset AS Service (SWRL)

2

Page 3: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Background

Context of Research

Network of SMEs

Diversified Data

Emergence of Big data technologies

Open data modeling

3

Page 4: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Background Challenge

• The diversity of data sources and the ontology modeling perspective

• The analysis of data repositories to create enterprise assets (services) for collaboration

• The composition of collaborative business processes from identified services

Martin Hilbert, Priscila Lopez, The world’s technological capacity to store, communicate, and compute information, Science 332 (6025) (2011) 60–65.

SME (Plastic Manufacturer)

DP ERP BA

SME (Metal Manufacturer)

DP ERP BA

Op

po

rtu

nit

y

4

Page 5: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Background

Challenge and Opportunities

Philip Chen, C. L., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Big Data Opportunities: above 50% of 560 enterprises think Big Data will help them in increasing operational efficiency, etc.

5

Page 6: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Background

Objectives

Integrate systems to capitalize and reuse enterprise capabilities and experiences when making decision.

Support concurrent/collaborative partners consortium in the definition of added value collaboration schema

Federated enterprises data repositories to create new collaboration capabilities.

6

DP + DMS

New Data

Asset Enabler

Service Orchestrator

Output (Collaborative Added Value)

Pro

vide o

nto

logies

Saving co

nfigu

ration

DP + DMS

New Data

Asset Enabler

Service Orchestrator

Bo

tto

m U

p A

pp

roac

h

Acquisition

Enterprises’ legacy systems

Digital preservation

system

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Background

Research Problem

How high degree of data integration in corporate data sources can be associated with perceived benefits of Added value during Inter-Enterprise collaboration?

How to define data and information assets in an enterprise

Find out the unique characteristics associated with this data

How to accelerate the creation of new business collaboration

7

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Background

Expected Results

Repository of assets published as services.

Assessment model for new collaboration opportunities.

Service matchmaker for collaborative business process composition.

8

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Literature Review Enterprise Collaboration

Ontology Engineering

Framework/Architecture

Year Methodology Comments

SnoBase 2006 Ontology

Large organizations are producing complex data, focus on acquisition and other aspects of valorizations of collaborations were missing

KAON 2004 Ontology

SymOntoX 2003 Ontology

pOWL 2005 Ontology

KACP 2008 Ontology Limited to only enterprise security access

Yuh-Jen et, al., 2009 ontology Covers PLM but ignores numerous complexities related to unstructured data

Daniel et al., 2010 ontology

ARIS 1998 Rstatic ontology Generalization not possible

CRE 2012 Fuzzy Logic Limited to risk analysis

NEGOSIS 2014 Ontology Limited to analysis phase only

9

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Chelmis et. al., (2013) Studied the exploitation of big data technologies for working collaboration with focus on interesting questions:

users' communication behavioral patterns

dynamics and characteristics, statistical properties and complex correlations between social and topical structures.

However limited to a single enterprise and did not address impact of big data for product improvement

10

Literature Review

Enterprise Collaboration Bigdata

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Bigdata bring new opportunities for:

Business analytic techniques and strategies. (Özcan et al., 2014)

Resources, capabilities, and skills needed to maximize business analytics impact. ( Shvachko et al., 2010 )

Challenge of globalized standard for inter-enterprise collaboration (Lin et al., 2007).

11

Literature Review

Enterprise Collaboration Bigdata

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Towards Solution

Enterprise Collaboration Framework

Consortium of SMEs

Big Data Technologies

Data Anonymizer

Fro

nt-

End

B

ack-

End

Make best use of collaboration capabilities in order to answer to new business requirements: • Co production of new product • Find best supplier of a specific raw material • Find a sub-contractor • Join capacity building • ....

SME-1

Inf. Tech.

Business Process

Dig. Res.

SME-2

Inf. Tech.

Business Process

Dig. Res.

Collaborative Model Added Value

Asset as Service (AaS) Service Orchestrator

Data Anonymizer

Data Anonymizer

Ontological Modeling

Digital Preservation Platform

SCM SRM ERP PLM CRM

Acquisition Organize Analyze Decide

Document Management System

(Un-structured docs)

Output

Added Value

Repository Assets

Assessment Model

Input

New Opportunities

(AaS) (AaS) (AaS) …..

12

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Big Data Technologies

Phase of Enterprise Collaboration:

Big Data Perspective

Proposed Architecture for Enterprise collaboration

Analysis in the phase of Acquisition (Case Studies)

Acquisition Organization Analysis Decide

Consortium of SMEs

Big Data Technologies

Data Anonymizer

Fro

nt-

End

B

ack-

End

Make best use of collaboration capabilities in order to answer to new business requirements: • Co production of new product • Find best supplier of a specific raw material • Find a sub-contractor • Join capacity building

SME-1

Inf. Tech.

Business Process

Dig. Res.

SME-2

Inf. Tech.

Business Process

Dig. Res.

Collaborative Model Added Value

Asset as Service (AaS) Service

Orchestrator

Data Anonymizer

Data Anonymizer

Ontological Modeling

Digital Preservation

Platform

SCM ERP

PLM

CPM

SRN

Acquisition

Organize

Analyze Decid

e

Document Management

System (Un-structured

docs)

Output

Added Value

Repository Assets

Assessment Model

Input

New Opportunities

(AaS) (AaS) (AaS) …..

13

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Results

Pig / Hive Results

Data Mining Results (MapReduce)

Big Data (Deep Learning)

14

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Query-1. Three types of clients. How to review it, given three

parameters ?

Query-2. Three types of clients. How to review it provided four

parameters ?

Query-3. Which specific business-deals pays us more ?

Results

Hive / Pig Results

15

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Query-4. List of customers with orders abandoned greater than

specific threshold ?

Query-5. Churn out analysis (leaving out customers). ?

Query-6. Identification of valuable customers who left away. ?(those

who paid n% more than customers who stayed)

Results

Hive / Pig Results

16

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Functional Layer

SRM ERP PLM CRM

Document Management System (SCM)

17

Business Assets

Business Ontology = small data

Big Data Processing

Re

du

ce

MA

P

Gro

up

ing

Sort

Shuffle Filter /

Transform

Aggregation

Summarize

v k

k

k

k

Intermediate

key-value pairs

v

v

v ……. k v

k v

k v v

v v k

k

k …

v

v

v

reduce

reduce

Group by Key

Output

key-value pairs Key-value groups

Da

ta S

ou

rce

s B

ig D

ata

Sto

rag

e

Visualization Results

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Functional Layer Contribution

18

1.1 For each feature in dataset Run Map without Reduce Run Sort/Shuffle Output mean and SD in individual file 1.21 Run Map without Reduce

Calculate MDL Run Sort 1.22 Run Map without Reduce

Calculate BIC Run Sort 1.23 Run Map without Reduce

Calculate AIC Run Sort

•Classification for continuos variables •Simple Naive Bayes is parallel in nature. No need for memory resident problem •Tradeoff . Poor Performance because of underfitting •Better solution is Graphical Bayesian Network

1.41 Run Map MDL-BestScore (HDFS) Run Reduce 1.42 Run Map BIC-BestScore (HDFS) Run Reduce 1.43 Run Map AIC-BestScore (HDFS) Run Reduce 2.1 Run Map and Reduce Output Optimized Model

AIC. Aikac Information Criteria BIC. Bayes Information Criteria MDL. Minimum Description Length

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rating event

model with validity time interval Train Model

Get unrated items Predict rating recommend

( , , )tc p r

( , )f c p r

( ) t , ts ef

( , , )tc p r

Customers

( )l R f

Select top k

( )tu

Customers

feedback feedback

2 2

, *r ,( , )

. , ,

c i c ji j ci j

i ji jc cc c

r x xx xsim x x

x x x xr r

Dimensions

Category of Company

Price

Product

Quantity Ordered

Date of

Order

Company

Product

Name Identified by

Identifi

er

Order Detail

Product History

Product Detail

Order Detail

Supplier Detail

Versioning Detail

Coefficient for Price Calculation

Client Quota Detail

Company Detail

Revenue Detail

Customer Grading

Revenue in off-peak

Customer Value

Massive Detail

Price

Identified by

Business Object Data Element Business Rule Capability

Symbol Legend

Information Asset

Business Assets

Enterprise Collaboration Functional Flow

Collaborative Recommendation Model

19

Why Big Data….no cold start

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Famille Format Mode Charge dimen

1 dimen

2

Couleur quantity last

production

Granule

ABS Coulé Chargé Bronze

0.5 1.5

4 7 8

Blanc Bleu

Rouge 5789 Jun - 2014

CHAUDRO Extrudé CONSO Coulé Polyester

Chargé Bronze

COULEE PU Pressé Pressé

Famille Format Mode Charge dimen

1 dimen

2 dimen

3 Couleur quantity

last production

Tube

GRAPHITAGE Pressé Anti UV

8 - 12 35

21 - 60

120 165 200 250

Rouge Incolore

Beige Fumé Bronze

99904 Nov - 2013

GRAVAGE Stabilisé

Anti Rayure

INJECT APR INJECT CLI INJECTION

Rectifié Régénéré

Grainé

JONC Régénéré

Grainé

20

Results

Data Mining Results

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Famille Format Mode Charge dimen

1 dimen

2 dimen

3 Couleur quantity last

production

Plaque

CONSO GRANULE

COULEE PU DECOUPE Grainé

Médical Moulé

Poreux OIL

Antistatique Diffusant

HI

12.7 14 16 45 70 80

110 140 180 300

55 - 70

260 300 310 325 330

Beige Fumé Bronze

Gris Gris Bleu

Ivoire Jaune

Incolore

9476 May - 2014

PETG Prismatique

FABRIQUES PETG

NEGOCE Lubrifiant

NEGOCE OIL

GRANULE Moulé Expansé

Antistatique

COULEE PU Diffusant

DECOUPE Moulé HI

21

Results

Data Mining Results

Page 22: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Famille Format Mode Charge dimen

1 dimen

2 dimen

3 Couleur quantity last production

Granule Jonc

INJECT CLI INJECTION

JONC MAINT LOC

MATIERE MONTAGE

NEGOCE PA PC PE

PEEK PETG

PETIT EQUI PF

PLAQUE PONCTUELS

TUBE USINAGE

Extrudé Pressé

Rectifié Grainé Moulé Lisse Plaxe

Chargé Bronze Chargé Carbone Chargé Calcium

Lubrifiant Antistatique

Diffusant Additif Anti UV AXPET

Confetti FROST

Polyester Prismatique

0-950 4-1200 120-25000

Blanc Bleu

Transparent Rouge

Incolore Fumé Bronze

Gris Bleu Ivoire Jaune

Orange Vert

75976 Sep - 2014

Famille Format Mode Charge dimen

1 dimen

2 dimen

3 Couleur quantity Last

production

Po

lyam

ide

PONCTUELS TUBE

USINAGE

Expansé Lisse Plaxe

Confetti Poreux

Prismatique 45 50 70 80 90

100 110 300

120-410

1230 1240 1250 1350

Blanc Bleu

Naturel Transparent

Noir Rouge

NON DEFINI Beige

Fumé Bronze Gris Bleu

Ivoire Jaune Vert

Aluminium

2967 Nov - 2013 TUBE

USINAGE Lisse

Poreux Prismatique

22

Results

Data Mining Results

Page 23: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Famille Format Mode Charge dimen

1

dimen

2

dimen 3

Couleur quantity Last

production

Poly

oxy

m

DIVERS Grainé Diffusant

70-164 - 550-1000

Blanc

1758 Dec - 2013

FABRIQUES Médical HI

Noir

BUR & INFO Moulé Additif

JONC Expansé Moulé

HI

23

Results

Data Mining Results

Page 24: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Quantity-Ordered Base Price Type of

Customer Abandoned Cart Price

Discount Recommendation

less than 100 300-400 A <10% 5%-6% B <10% 5%-6% C <7% 1%-3%

100-300 301-500 A <7% 6%-8% B <7% 6%-8% C <5% 3%-5%

more than 301 501-3000 A <5% 9%-12% B <6% 8%-12% C <4% 6%-9%

24

Results

Data Mining Results

Page 25: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Quantity-Ordered

Base Price Nomenclature Gamme Interne

Outillage Transport Devis lie Gamme sous-

traitance technique Globale

Discount Recommendation

less than 100 300-400

1 to 3

<4

>12% <7% >15% > 70%

>40

>3 5%-6%

3 to 7 >10% <7% >14% > 65% >3 5%-6%

8 to 10 >9% <5% >11% > 55% >2 1%-3%

100-300 301-500

2 to 3

<8 and >3

>12% <7% >16% > 65%

>50 >5

6%-8%

4 to 8 >10% <7% >14% > 60% 6%-8%

9 to 13 >9% <6% >12% > 58% 3%-5%

more than 301

501-3000

1 to 3

<8 and >3

>12% <7% >18% > 67%

>70

>2.4 9%-12%

4 to 9 >10% <7% >15% > 65% >2 8%-12%

10 to 14 >9% <5% >12% > 60% >1.5 6%-9%

25

Results

Data Mining Results

Page 26: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

Famille Production Hours

Minimum Maximum Average

Granule 57 hours 78 hours 70 hours

Tube 19 hours 23 hours 20 hours

Plaque 87 hours 101 hours 90 hours

Granule 123 hours 189 hours 169 hours

Jonc 68 hours 79 hours 73 hours

Polyamide 65 hours 74 hours 70 hours

26

Results

Data Mining Results

Page 27: Opportunity Analysis for Enterprise Collaboration between ...iwei2015.mines-ales.fr/sites/mines-ales.fr/files/u417/...Enterprise Collaboration Big Data Capability Results Ontological

27

Companies Items (Mode) x/10

NEC75 BUR & INFO (2) COULEE PU (6) MARCHES (9) METALISA (9) PONCTUELS (9)

LABEL74 DIVERS (9) FABRIQUES (7) INJECT APR (10) MAINT LOC (6) OUTILLAGE (3) PONCTUELS (1)

CEZUS44 CHAUDRO (1) GRAVAGE (7) PETIT EQUI (1) PONCTUELS (6)

HEULIE79 FABRIQUES (1) MAINT LOC (3) PONCTUELS (6)

GLYNWE34 DIVERS (2) INJECT APR (5) MAINT LOC (2) MARCHES (6)

AER69 FABRIQUES (7) GRAVAGE (4) METALISA (3) OUTILLAGE (4)

RHODIA93 CHAUDRO (4) MARCHES (3) PONCTUELS (8)

DINEL76 FABRIQUES (7) OUTILLAGE (3)

NEC75 LABEL74 CEZUS44 HEULIE79 GLYNWE34 AER69 RHODIA93 DINEL76

NEC75 1,0 11,8 11,4 9,3 11,4 10,0 22,0 0,0

LABEL74 11,8 1,0 4,5 12,6 21,9 14,8 5,8 10,5

CEZUS44 11,4 4,5 1,0 9,8 0,0 12,0 19,0 0,0

HEULIE79 9,3 12,6 9,8 1,0 6,1 10,7 17,1 8,0

GLYNWE34 11,4 21,9 0,0 6,1 1,0 0,0 9,0 0,0

AER69 10,0 14,8 12,0 10,7 0,0 1,0 0,0 15,7

RHODIA93 22,0 5,8 19,0 17,1 9,0 0,0 1,0 0,0

DINEL76 0,0 10,5 0,0 8,0 0,0 15,7 0,0 1,0

Enterprise Collaboration Big Data Capability Results

NEC75 LABEL74 CEZUS44 HEULIE79 GLYNWE34 AER69 RHODIA93 DINEL76

NEC75 1,0 11,8 11,4 9,3 11,4 10,0 22,0 0,0

LABEL74 11,8 1,0 4,5 12,6 21,9 14,8 5,8 10,5

CEZUS44 11,4 4,5 1,0 9,8 0,0 12,0 19,0 0,0

HEULIE79 9,3 12,6 9,8 1,0 6,1 10,7 17,1 8,0

GLYNWE34 11,4 21,9 0,0 6,1 1,0 0,0 9,0 0,0

AER69 10,0 14,8 12,0 10,7 0,0 1,0 0,0 15,7

RHODIA93 22,0 5,8 19,0 17,1 9,0 0,0 1,0 0,0

DINEL76 0,0 10,5 0,0 8,0 0,0 15,7 0,0 1,0

NEC75 CHAUDRO

LABEL74 MARCHES

CEZUS44 MARCHES

HEULIE79 CHAUDRO MARCHES

GLYNWE34 FABRIQUES OUTILLAGE PONCTUELS

AER69

RHODIA93 METALISA BUR & INFO COULEE PU

DINEL76 GRAVAGE METALISA

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Thing

Customer Product Recommendation Order

R.P N.R.P

Category

Detail

Quotation Detail

Product History

Order Date

Coefficient of Price

Creation Hours

Famille

Format

Mode

Charge

Color

dimension

Last-prod

Abandoned Cart Amount

Discount Recommended

rating event

Train Model

Predict rating

conta

ins

conta

ins

conta

ins

conta

ins

Base Price

demands

demanded by

dete

rmin

ed b

y

dete

rmin

ed b

y

uses

uses

Ontological Modelling Relationship among Information Assets,

Data Elements, and Business Objects

28

dete

rmin

ed b

y determined by

Business Object Information Asset Data Element Data Properties

are

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Asset As Service (SWRL)

APR(? ) . (? ,?y) (mode(?y,?m) . (divers, fabriques,bur info))

(charge(?y,?c) (diffusant,HI?Additif))

dim ((?y,?d) 1(?d,?d1) ((?d1,?r) (?r,70 164)) ((?y,?q)

x produce product x selection range

range

ension d range qty

(?q,1800)))

. ($ x,$ y) (($ y,$m) ($ y,$c) ($ y,$d) ($ y,$q))production capability conditions

Production Capability

APR(? ) . (? ,?y) . (?y,?z) min(?z,57) max(?z,78) average(?z,70) ($y)x produce product x production hours granule

APR(? ) . (? ,?y) . (?y,?z) min(?z,19) max(?z,23) average(?z,20) ($y)x produce product x production hours tube

APR(? ) . (? ,?y) . (?y,?z) min(?z,87) max(?z,101) average(?z,90) ($y)x produce product x production hours plaque

APR(? ) . (? ,?y) . (?y,?z) min(?z,68) max(?z,79) average(?z,73) ($y)x produce product x production hours jonc

APR(? ) . (? ,?y) . (?y,?z) min(?z,65) max(?z,74) average(?z,70) ($y)x produce product x production hours polyamide

Timing Capability

APR(? ) . (? ,?y) ( . (?y,?z) (300,400))

(quantity.ordered(?y,?q) (300,400)) . ((?y,?a) min(?a,10))

. (?c,a) ($c, range(5,6))

x produce product x base price range

range abandoned cartprice

customer type discount

Discount Recommendation (previous purchase history)

29

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30

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Chelmis C., "Complex modeling and analysis of workplace collaboration data", Collaboration Technologies and Systems (CTS), 2013 International Conference on. IEEE, 2013, pp. 576-579.

Chen T.-Y., "Knowledge sharing in virtual enterprises via an ontology-based access control approach", Computers in Industry", vol. 59 no. 5, 2008, p. 502-519.

Denicolai S., Zucchella A., Strange R., "Knowledge assets and firm international performance", International Business Review, vol. 23, no. 1, 2014, p. 55-62.

Ding Y., Foo S., "Ontology research and development, Part 2 - A review of ontology mapping and evolving", Journal of Information Science, vol. 28, no. 5, 2002, p. 375-388.

Gene Ontology Consortium, 2015, Gene Ontology Consortium: going forward, Nucleic Acids Research 43, no. D1, D1049-D1056.

Geerts G. L., McCarthy W. E.,. "An ontological analysis of the economic primitives of the extended-REA enterprise information architecture", International Journal of Accounting Information Systems, vol. 3, no 1, 2002, p. 1-16.

Lee J., Chae H., Kim C.-H., Kim K., "Design of product ontology architecture for collaborative enterprises", Expert Systems with Applications , vol. 36, no. 2, 2009, p. 2300-2309.

Lee J., Goodwin R., "Ontology management for large-scale enterprise systems", Electronic Commerce Research and Applications, vol. 5, no. 1, 2006, p. 2-15. 31

References

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Lin H. K., Harding J. A., "A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration", Computers in Industry, vol. 58, no. 5, 2007, p. 428-437.

Naeem M., Moalla N., Ouzrout Y., Bouaras A. "An ontology based digital preservation system for enterprise collaboration", Computer Systems and Applications (AICCSA), 2014 IEEE/ACS 11th International Conference on, November 2014, p. 691-698

O'Leary D. E., "Enterprise ontologies: Review and an activity theory approach", International Journal of Accounting Information Systems, vol. 11, no. 4, 2010, p. 336-352.

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