isncc 2017

16th May 2017 ISNCC'[email protected] of Morocco 1

Multi-Dimensional Database Modeling:

Literature Survey & Research Agenda in the

Big Data

Alfredo Cuzzocrea University of Trieste & ICAR Rim Moussa LaTICE Lab. & University of Carthage

The 4th Intl. Symposium on Networks, Computers and

Communications @ Marrakesh, Kingdom of Morocco 16th of May, 2017


Business Intelligence

How to translate data into Insights & Opportunities! »BI encompasses a variety of tools, applications and methodologies that

enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.

»Integrations tools (ETL tools), Workflow design, »Data warehousing tools »OLAP servers technologies, OLAP clients, »mining structures, reporting tools, dashboards’ design tools »Multi-dimensional design methods, …


Research agenda?

DOLAP Workshop 2006

IBM White paper 2015


Outline

Part I: Data Warehouse Systems DWS Architectures OLAP cube DSS Benchmarks

Part II: Multi-dimensional Database Modeling

Part III: Challenging Problems

Conclusion


Lazy Data Integration Query-driven Architecture

Relational Data

Source

WRAPPER WRAPPER

MEDIATOR

WRAPPER WRAPPER


Eager Data Integration Warehouse System Architecture

Data Warehouse Relational Data

Source

Integration workflows of the

Integration System

16th May 2017 ISNCC'[email protected] of Morocco

Facts: are the objects that represent the subject of the desired analyses. »Examples: sales records, weather records, cabs trips, … »The fact table contained 3 types of attributes: measured attributes, foreign keys

to dimension tables, degenerate dimensions Dimension(s): »Levels are individual values that make up dimensions »Examples

»Date dimension (Trimester, month, day) »Time dimension (hour, min, sec) »Geography dimension (Country, city, postal code)

Measure(s): »Examples: revenue, lost revenue, sold quantities, expenses, … »Use aggregate functions: min, max, count, distinct-count, sum, average, …

7

OLAP Cube


OLAP Operations On-Line Analytical Processing (Q10 of TPC-H benchmark)


Structured Query Language (SQL)

»Relational and static schema »Data Definition, data Manipulation, and Data Control Language »Analytic Functions (window functions over partition by …) »Cube, roll-up and grouping sets operators

MultiDimensional eXpressions (MDX)

»Invented by Microsoft in 1997 »For querying and manipulating the multidimensional data stored in OLAP cubes »Static schema

Data Flow programming language

»Google Sawzall, Apache Pig Latin, IBM Infosphere Streams »Dynamic schema »After data is loaded, multiple operators are applied on that data before the final

output is stored.

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Query Languages

Load Data Apply

Schema Apply Filter Group Data

Apply Aggregate Function

Sort Data Store

Output


Query Languages SQL – Q16 of TPC-H benchmark


Query Languages MDX –Q16 of TPC-H Benchmark

WITH SET [Brands] AS 'Except({[Part Brand].Members}, {[Part

Brand].[Brand#45 ]})'

SET [Types] AS 'Filter({[Part Type].Members}, (NOT ([Part

Type].CurrentMember.Name MATCHES "(?i)MEDIUM POLISHED.*")))'

SET [Sizes] AS 'Filter({[Part Size].Members}, ([Part Size].CurrentMember IN

{[Part Size].[3], [Part Size].[9], [Part Size].[14], [Part Size].[19], [Part Size].[23],

[Part Size].[36], [Part Size].[45], [Part Size].[49]}))'

SELECT [Measures].[Supplier Count] ON COLUMNS,

nonemptyCrossjoin(nonemptyCrossjoin([Brands], [Types]), [Sizes]) ON ROWS

FROM [Cube16]


Query Languages Data Flow –Pig Latin script for Q16 of TPC-H benchmark


Decision Support Systems Benchmarks

Non-TPC Benchmarks Real datasets

»Open data or proprietary data »fixed size »Devise a workload or trace the proprietary workload

APB-1: no scale factor TPC Benchmarks

The Transaction Processing Council founded in 1988 to define benchmarks

In 2009, TPC-TC is set up as an International Technology Conference Series on Performance Evaluation and Benchmarking

Examples of benchmarks relevant for benchmarking decision support systems: TPC-H, TPC-DS and TPC-DI Common characteristics of TPC benchmarks

»Synthetic data »Scale factor allowing generation of different volumes 1GB to 1PB


Decision Support Systems Benchmarks TPC-H Benchmark Schema (1/2) TPC-H Benchmark: complex schema 22 ad-hoc SQL statements (star queries, nested queries, …) + refresh functions


Decision Support Systems Benchmarks TPC-H Benchmark (2/2)

TPC-H Benchmark 2 Metrics »QphH@Size is the number of queries processed per hour, that the system

under test can handle for a fixed load »$/QphH@Size represents the ratio of cost to performance, where the cost is

the cost of ownership of the SUT (hardware,software, maintenance). Variants of TPC-H Benchmarks

TPC-H*d Benchmark [Cuzzocrea and Moussa, 2013] »Turning TPC-H benchmark into a Multi-dimensional benchmark »Few schema changes »No update to workload requirement »MDX workload for OLAP cubes and OLAP queries

SSB: Star Schema Benchmark [O’Neil et al., 2012] »Turning TPC-H benchmark into star-schema »Workload composed of 12 queries

TPC-H translated into Pig Latin (Apache Hadoop Ecosystem) [Moussa,2012] »22 pig latin scripts which load and process TPC-H raw data files (.tbl files)


Decision Support Systems Benchmarks TPC-DS Benchmark (1/2)

TPC-DS Benchmark: 7 data marts


Decision Support Systems Benchmarks TPC-DS Benchmark (2/2) TPC-DS Benchmark Workload

Hundred of queries (99 query templates) OLAP, windowing functions, mining, and reporting queries ACID and Concurrent data maintenance (not ACID in TPC-DS 2.x)

TPC-DS Benchmark Metrics 3 Metrics

»QphDS@Size is the number of queries processed per hour, that the system under test can handle for a fixed load.

»Data Maintenance and Load Time are calculated »$/QphDS@Size represents the ratio of cost to performance, where the

cost is a 3 year cost of ownership of the SUT (hardware,software, maintenance)

»System Availability date: the date when the system is available to customers.

TPC-DS implementations TPC-DS v2.0

»Extension for non-relational systems such as Hadoop/Spark big data systems


Decision Support Systems Benchmarks TPC-DI Benchmark (1/3) [Poess et al. 2014] For benchmarking Data Integration technologies Synthetic Data of a Factious Retail Brokerage Firm

»Internal Trading system data, Internal Human resources data, Internal CRM System and External data

»Different data scales »Data extracted from different sources:

»Structured (csv) »Semi-structured data (xml) »Multi record (nested data) »Change Data Capture (CDS)

Complex Data Integration Tasks Load large volumes of historical data Load incremental updates Execute complex transformations Check and ensure consistency of data


TPC-DI Benchmark Complex Transformations (2/3) TPC-DI implements 18 Complex Transformations which feature the

following characteristics, Transfer XML to relational data Detect changes in dimension data, and applying appropriate tracking

mechanisms for history keeping dimensions Filter input data according to pre-defined conditions Identify new, deleted and updated records in input data Merge multiple input files of the same structure Join data of one input file to data from another input file with different

structure Standardize entries of the input files Join data from input file to dimension table Join data from multiple input files with separate structures Consolidate multiple change records per day and identify most current Perform extensive arithmetic calculations Read data from files with variable type records Check data for errors or for adherence to business rules Detect changes in fact data, and journaling updates to reflect current state


TPC-DI Benchmark Metrics (3/3)

Metrics

Performance Metric TPC_DI_RPS = Trunc(GeoMean(TH, Min(TI1 , TI2)))

TH: Throughput of Historical Load

TI1: Throughput of Incremental Update 1

TI2: Throughput of Incremental Update 2

Price/Performance Metric Price-per-TPC_DI_RPS = $ / TPC_DI_RPS

$ is the total 3-year pricing


Outline

Introduction

Part I: Data Warehouses

Part II: Experiences

TPC-H*d Experience AutoMDB TPC-DS*d


Conclusion


Given,

A relational Warehouse schema

A Workload -a set of SQL Statements,

W = {Q1, Q2, …, Qn}

where Qi is a parameterized query

How to design the Multi-dimensional DB Schema?

How to define cubes?

Will there be a single cube or multiple cubes?

Are there any rules for merging of cubes?

Are there any rules for definition of virtual cubes?

Which Optimizations are suitable for performance tuning ?

Derived data calculus & refresh?

Data partitioning & parallel cube building?

# 22

MDB Design Problem

16th May 2017 ISNCC'[email protected] of Morocco # 23

Idea

Map each business question to an OLAP cube

>> Obtain a multi-dimensional DB schema

Recommend & Test Optimizations

>> Derived Data

>> Data partitioning

>> Cube Merging


TPC-H*d

Truly OLAP variant of TPC-H benchmark

TPC-H SQL workload translated into MDX (MultiDimensional

eXpressions)

The workload is composed of 23 MDX statements for OLAP

cubes and 23 MDX statements for OLAP business queries. Each business question of TPC-H benchmark is mapped to an OLAP

cube


TPC-H*d Q8: From SQL statement to OLAP cube


TPC-H*d TPC-H*d OLAP Cube C8

Market Share for each supplier nation within a region of customers, for each year and each part type


TPC-H*d TPC-H*d OLAP Query Q8

Market Share for each RUSSIAN Suppliers within AMERICA region, Over the years 1995 and 1996 and for part type ECO. ANODIZED STEEL


Open source software implemented in java

Parses MDB schemas (.xml) files using SAX Library

Performs comparisons of OLAP cubes' characteristics.

»For each pair of OLAP cubes, »show whether they have same fact table or not »compute the nbr of shared | different | coalescable dimensions

»Dimensions are coalescable if they are extracted from the dimension table and their hierarchies are coalescable

»compute the number of shared | different measures »Run merge of OLAP cubes using different similarity functions

»Simple distance function have or not same fact table »K-means clustering

»Distance function is computed with weights according dimensions

»Propose Virtual Cubes »Auto-generate a new MDB Schema (.xml)

»Create MDB Schema from SQL Workload »On-going, tests include TPC-DS benchmark

# 28

AutoMDB


AutoMDB Load OLAP Cubes defined in xml file


AutoMDB Compare OLAP Cubes –have or not same fact table


AutoMDB Compare Cubes –Group cubes which have same fact table


AutoMDB Compare Cubes –Auto-generate a new MDB schema


Outline

Introduction

Part I: Data warehousing


Part III: Challenges Big Data Integration Flexible Schema Model Curse of dimensionality Systems which scale-out Intelligent Recommenders Real-time OLAP Advanced Visualization

Conclusion


Big Data

Volume

»Volume refers to the amount of data, henceforth the challenge is integration at scale,

Velocity »Velocity refers to the speed at which new data is generated, henceforth

the challenge is to integrate and analyze data while it is being generated, Variety

»Variety refers to different types of data; e.g. structured (relational data), semi-structured (XML, JSON, BSON), unstructured (text); henceforth the challenge is integration of different types of data,

Veracity »Veracity refers to the messiness or trustworthiness of the data,

henceforth the challenge is to integrate uncertain data quality in data sources,

Value »Value refers to our ability to turn data into value.


Advanced and highly performance Technologies

»Allow to load and extract different data formats and allow to perform complex transformations

»Solve heterogeneity »Syncing Across Data Sources

»data copies migrated from a wide range of sources on different rates and schedules can rapidly get out of the synchronization

»Perform data integration at scale Solution Cost

TPC-DI is a good start for benchmarking integration solutions TPC-DI implementation is on-going

Challenge #1: Big Data Integration


Challenge #2: Data Schema Model

Schema-based data »E.g. relational model, multi-dimensional model »They define what columns appear, their names, and their datatypes. Schemaless Data and Dynamic Schema »Non-uniform data: custom fields and non-uniform data types »Parse data and query on-the-fly »NoSQL systems (e.g. Apache pig latin, SQL-on-hadoop) Data Lake »All data is loaded from source systems i.e., no data is turned away. »Data is stored at the leaf level in an untransformed or nearly

untransformed state. »Data is transformed and schema is applied to fulfill the needs of analysis.

»Load in the raw data, as-is. At processing time give data a structure. schema-on-read

»High agility: configuration and reconfiguration of models, queries and applications as needed on-the-fly


Challenge #3: Curse of Dimensionality

Datasets in applications like bioinformatics and text processing are characterized by,

Large column data set a.k.a. highly dimensional data »100 columns of data 100 dimension hyperspace »A data cube of 100 dimensions such each dimension cardinality=10 11100 aggregate cells

Moderate size »One million of tuples

Proposed Solutions Minimal Cubing Approach [Li et al., 2004 ] Dimension Reduction & Feature Selection

»Principal Component Analysis (PCA) »Linear Discriminant Analysis (LDA) »Canonical Correlation Analysis (CCA) »Latent Semantic Indexing (LSI) for text data

»Complex and not scalable


Challenge #4: Scale-out

Introduction

Part I: Methods & State-of-the-Art



Conclusion

Data Fragmentation Parallel IO Parallel Processing

Technologies Parallel Cube Calculus Distributed Relational Data Warehouses + Mid-tier for parallel

cube calculus, e.g. OLAP* framework

SQL-on-Hadoop Systems e.g. Hive, Spark SQL, Drill, Impala, BigInsights

Scalable and Distributed Data Structures K-RP*s [Litwin et al.,1996], SkipTree [Messeguer, 1997], SkipWebs

[Arge, 2005], PN-Tree [Ali, 2005]


Challenge #5: Intelligent Recommenders Self Manageability Selection of Indexes and Materialized Views are physical

structures that can significantly accelerate performance. Recommenders for performance tuning

»AutoAdmin research project at Microsoft, which explores novel techniques to make databases self-tuning [Agrawal et al., 2000]

»MS Database Tuning Advisor: offline techniques, DBA is out of the picture

Alerter Approach [Hose et al., 2008] »support the aggregate configuration of an OLAP server by (1)

continuously monitoring information about the workload and the benefit of aggregation tables and (2) alerting the DBA if changes to the current configuration would be beneficial

Semi-Automatic Index Tuning: keeping DBAs in the loop [Schnaiter and Polyzotis, 2012] »Online workload analysis with decisions delegated to the DBA »Solution takes into index interactions


Challenge #6: Real-time Processing Retrospective Analysis

Traditional Architectures »DWS are deployed as part of an OLAP System separated out from the

OLTP system, »Data propagates down to the OLAP system, but typically after some “time”,

»This is a sufficient system for retrospective analytics, but does not suffice

in situations that require real-time analytics . New Data Processing Architectures Architecture for Big Data processing at Scale »Lambda Architecture by Nathan Marz (2014)

»Batch processing system + Speed processing system »Kappa Architecture

»No batch processing systems

»New solutions for architecting DW systems!


Challenge #7: Visualization

Introduction




Conclusion

Goal: Visualize multidimensional data sets by capturing the dimensionality of data Data cube

Is a representation of a multidimensional data set OLAP clients technologies Pivot table or cross-tab OLAP ops: drill-down, roll-up, pivot, slice, dice bar-charts, pie-charts, and time series e.g. Apache jPivot, Saiku

Sophisticated visualization techniques challenges Interactive Reflect current data Support of large dimensional datasets Ergonomic


Challenge #7: Visualization Multidimensional Networks

Networks’ application domains: social networks (linkedIn, facebook, …) , the web, communication networks, … A multidimensional network

Graph stucture and Multidimensional attributes Example: Graph Cube [Zhao et al. 2011]

-1- and -2- are friends


Challenge #7: Visualization Nanocubes by AT&T Real-time exploratory visualization on large spatiotemporal and

multidimensional datasets Real-time: extremely fast queries Spatial: spatial region that can be either a rectangle covering most of

the world, or a heatmap of activity Multidimensional: besides latitude, longitude, and time, there are

other attributes such as tweet device: android or iphone, tweet language: eng, fr, it, sp, ru…

Nanocube [Lins et al., 2013 ] Algorithm outline for every object oi find the finest address of the schema S hit by this object, update the time series associated with this address update in a deepest first fashion, all coarser addresses hit by oi

Cons: memory usage when indexing all six dimensions (latitude, longitude, time, language, device,

application), the 210 million points from Twitter take around 45GB of memory


Conclusion

There is a lot to do.


References (1/3) M. Fricke, The Knowledge Pyramid: A Critique of the DIKW Hierarchy. Journal of Information

Science. 2009. E.F. Codd, S.B. Codd and C.T. Salley, Providing OLAP to User Analysts: an IT mandate, 1993. J. Widom, Integrating Heterogeneous databases: eager or lazy? ACM Computing Surveys (CSUR)

Vol.4, 1996 Y.R. Cho, Data Warehouse and OLAP Operations www.ecs.baylor.edu/faculty/cho/4352 TPC homepage http://www.tpc.org/ M. Poess, T. Rabl and B. Caufield: TPC-DI: The First Industry Benchmark for Data

Integration. PVLDB 7(13): 1367-1378 (2014) http://www.vldb.org/pvldb/vol7/p1367-poess.pdf X. Li, J. Han, H. Gonzalez: High-Dimensional OLAP: A Minimal Cubing Approach. VLDB 2004. C. Imhoff, N. Galemmo, J. G. Geiger. Mastering Data Warehouse Design: Relational and

Dimensional Techniques. 2003.

R. Kimball, M. Ross, W. Thornthwaite, J. Mundy, B. Becker. The Data Warehouse

Lifecycle Toolkit. 2nd Edition.

R. Kimball, M. Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional

Modeling. 2nd Edition. H. G. Molia. Data Warehousing Overview: Issues, Terminology, Products.

www.cs.uh.edu/~ceick/6340/dw-olap.ppt (slides)

http://www.minet.uni-jena.de/dbis/lehre/ss2005/sem_dwh/lit/Cod93.pdf
















http://www.ecs.baylor.edu/faculty/cho/4352








http://www.tpc.org/

http://www.tpc.org/

http://www.tpc.org/

http://www.tpc.org/

http://www.tpc.org/

http://www.tpc.org/

http://www.tpc.org/

http://www.vldb.org/pvldb/vol7/p1367-poess.pdf














http://www.cs.uh.edu/~ceick/6340/dw-olap.ppt















References (2/3) Modeling Multidimensional Databases (non exhaustive list)

M. Gyssens and L. V.S. Lakshmanan. A Foundation for Multi-Dimensional Databases. VLDB’1997. R. Agrawal, A. Gupta and S. Sarawagi. Modeling Multidimensional Databases. ICDE’1997. J. Gray, A. Bosworth, A. Layman and H. Priahesh. Data Cube: A Relational Aggregation

Operator Generalizing Group-By, Cross-Tab, and Sub-Totals. ICDE’2008. P. Vassiliadis. Modeling Multidimensional Databases, Cubes and Cube Operations. SSDBM’1998. L. Cabibbo and R. Torlone. A Logical Approach to Multidimensional Databases. EDBT’1998. D. Cheung, B. Zhou, B. Kao, H. Lu, T. Lam and H. Ting. Requirement-based data cube

schema design. CIKM’1999. T. Niemi, J. Nummenmaa and P. Thanisch. Constructing OLAP cubes based on Queries. DOLAP’2001. O. Teste. Towards Conceptual Multidimensional Design in Decision Support Systems. DEXA’2010. A. Cuzzocrea and R. Moussa. Multidimensional Database Design

via Schema Transformation: Turning TPC-H into the TPC-H*d Multidimensional Benchmark. COMAD’2013.


References (3/3)

Introduction




Conclusion

M. Fowler, Schemaless data structures. 2013 http://martinfowler.com/articles/schemaless/ N. Marz and J. Warren, Big Data: Principles and best practices of scalable realtime data

systems, 1st Edition S. Agrawal, S. Chaudhuri and V. Narasayya Automated Selection of Materialized Views and

Indexes for SQL Databases. VLDB’2000 http://www.research.microsoft.com/dmx/AutoAdmin K. Hose, D. Klan, M. Marx and K. Sattler. When is it Time to Rethink the Aggregate

Configuration of Your OLAP Server?. VLDB’2008 Karl Schnaitter and Neoklis Polyzotis. Semi-Automatic Index Tuning: Keeping DBAs in the

Loop. VLDB’2012 P. Zhao, X. Li, D. Xin and J. Han.

Graph cube: on warehousing and OLAP multidimensional networks. SIGMOD’2011 L. D. Lins, J. T. Klosowski and C. E. Scheidegger:

Nanocubes for Real-Time Exploration of Spatiotemporal Datasets. IEEE Trans. Vis. Comput. Graph. 2013 https://github.com/laurolins/nanocube

http://www.research.microsoft.com/dmx/AutoAdmin









https://github.com/laurolins/nanocube






Thank you for your Attention

Q & A

Multi-Dimensional Database Modeling and Querying: Literature

Survey and Research Agenda in the Big Data Era

Alfredo Cuzzocrea and Rim Moussa

16th of May, 2017

isncc 2017

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