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CSE 5331/7331CSE 5331/7331Fall 2007Fall 2007

Dimensional ModelingDimensional Modeling

Margaret H. DunhamMargaret H. DunhamDepartment of Computer Science and EngineeringDepartment of Computer Science and Engineering

Southern Methodist UniversitySouthern Methodist University

Some slides extracted from Some slides extracted from Data Mining, Introductory and Advanced TopicsData Mining, Introductory and Advanced Topics, Prentice Hall, 2002., Prentice Hall, 2002.

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Dimensional ModelingDimensional Modeling View data in a hierarchical manner more as View data in a hierarchical manner more as

business executives mightbusiness executives might Useful in decision support systems and miningUseful in decision support systems and mining Dimension:Dimension: collection of logically related collection of logically related

attributes; axis for modeling data.attributes; axis for modeling data. Facts:Facts: data stored data stored Ex: Dimensions – products, locations, dateEx: Dimensions – products, locations, date

Facts – quantity, unit priceFacts – quantity, unit price

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Multidimensional Model ExampleMultidimensional Model Example

Fig 2 [1]

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Cube view of DataCube view of Data

Fig 4 [1]

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Aggregation HierarchiesAggregation Hierarchies

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Multidimensional SchemasMultidimensional Schemas Star Schema shows facts and dimensionsStar Schema shows facts and dimensions

– Center of the star has facts shown in fact tablesCenter of the star has facts shown in fact tables– Outside of the facts, each diemnsion is shown Outside of the facts, each diemnsion is shown

separately in dimension tablesseparately in dimension tables– Access to fact table from dimension table via joinAccess to fact table from dimension table via join

SELECT Quantity, PriceSELECT Quantity, PriceFROM Facts, LocationFROM Facts, LocationWhere (Facts.LocationID = Location.LocationID) andWhere (Facts.LocationID = Location.LocationID) and(Location.City = ‘Dallas’)(Location.City = ‘Dallas’)

– View as relations, problem volume of data and View as relations, problem volume of data and indexingindexing

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Star SchemaStar Schema

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Flattened StarFlattened Star

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Normalized StarNormalized Star

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Snowflake SchemaSnowflake Schema

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OLAP IntroductionOLAP Introduction

OLAP by ExampleOLAP by Examplehttp://perso.orange.fr/bernard.lupin/englishttp://perso.orange.fr/bernard.lupin/english/index.htmh/index.htm What is OLAP?What is OLAP?http://www.olapreport.com/fasmi.htmhttp://www.olapreport.com/fasmi.htm

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OLAPOLAP Online Analytic Processing (OLAP):Online Analytic Processing (OLAP): provides more provides more

complex queries than OLTP.complex queries than OLTP. OnLine Transaction Processing (OLTP):OnLine Transaction Processing (OLTP): traditional traditional

database/transaction processing.database/transaction processing. Dimensional data; cube view Dimensional data; cube view Support ad hoc queryingSupport ad hoc querying Require analysis of dataRequire analysis of data Can be thought of as an extension of some of the basic Can be thought of as an extension of some of the basic

aggregation functions available in SQLaggregation functions available in SQL OLAP tools may be used in DSS systemsOLAP tools may be used in DSS systems Mutlidimentional view is fundamentalMutlidimentional view is fundamental

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OLAP ImplementationsOLAP Implementations MOLAP (Multidimensional OLAP)MOLAP (Multidimensional OLAP)

– Multidimential Database (MDD)Multidimential Database (MDD)– Specialized DBMS and software system capable of supporting the Specialized DBMS and software system capable of supporting the

multidimensional data directlymultidimensional data directly– Data stored as an n-dimensional array (cube)Data stored as an n-dimensional array (cube)– Indexes used to speed up processingIndexes used to speed up processing

ROLAP (Relational OLAP)ROLAP (Relational OLAP)– Data stored in a relational databaseData stored in a relational database– ROLAP server (middleware) creates the multidimensional view for ROLAP server (middleware) creates the multidimensional view for

the userthe user– Less Complex; Less efficientLess Complex; Less efficient

HOLAP (Hybrid OLAP)HOLAP (Hybrid OLAP)– Not updated frequently – MDDNot updated frequently – MDD– Updated frequently - RDBUpdated frequently - RDB

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OLAP OperationsOLAP Operations

Single Cell Multiple Cells Slice Dice

Roll Up

Drill Down

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OLAP OperationsOLAP Operations Simple query – single cell in the cubeSimple query – single cell in the cube SliceSlice – Look at a subcube to get more – Look at a subcube to get more

specific informationspecific information Dice Dice – Rotate cube to look at another – Rotate cube to look at another

dimensiondimension Roll UpRoll Up – Dimension Reduction; Aggregation – Dimension Reduction; Aggregation Drill DownDrill Down Visualization: These operations allow the Visualization: These operations allow the

OLAP users to actually “see” results of an OLAP users to actually “see” results of an operation.operation.

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Relationship Between TopcsRelationship Between Topcs

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Decision Support SystemsDecision Support Systems Tools and computer systems that assist Tools and computer systems that assist

management in decision makingmanagement in decision making What if types of questionsWhat if types of questions High level decisionsHigh level decisions Data warehouse – data which supports Data warehouse – data which supports

DSSDSS

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StarflakeStarflake

Fig 2 [4]

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Hierarchy of Data CubesHierarchy of Data Cubes

Fig 4 [4]

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Unified Dimensional ModelUnified Dimensional Model

Microsoft Cube ViewMicrosoft Cube View SQL Server 2005SQL Server 2005http://msdn2.microsoft.com/en-us/library/ms345http://msdn2.microsoft.com/en-us/library/ms345143.aspx143.aspxhttp://cwebbbi.spaces.live.com/Blog/cns!1pi7EThttp://cwebbbi.spaces.live.com/Blog/cns!1pi7ETChsJ1un_2s41jm9Iyg!325.entryChsJ1un_2s41jm9Iyg!325.entry MDX AS2005MDX AS2005http://msdn2.microsoft.com/en-us/library/aa216http://msdn2.microsoft.com/en-us/library/aa216767(SQL.80).aspx767(SQL.80).aspx

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BibliographyBibliography[1] [1] Anne-Muriel Arigon, Anne Tchounikine, and Maryvonne Miquel, “Handling Anne-Muriel Arigon, Anne Tchounikine, and Maryvonne Miquel, “Handling

Multiple Points of View in a Multimedia Data Warehouse,” Multiple Points of View in a Multimedia Data Warehouse,” ACM Transactions on ACM Transactions on Multimedia Computing, Communications and ApplicationsMultimedia Computing, Communications and Applications , Vol. 2, No. 3, August , Vol. 2, No. 3, August 2006, Pages 199–218.2006, Pages 199–218.

[2] S. Nicholson, “The Bibliomining Process: Data Warehousing and Data Mining [2] S. Nicholson, “The Bibliomining Process: Data Warehousing and Data Mining for Library Decision-Making,” for Library Decision-Making,” Information Technology and Libraries,Information Technology and Libraries, 22(4), 22(4), 2003.2003.

[3] S. Nicholson, “The Basis for Biliomining: Frameworks for Bringing Together [3] S. Nicholson, “The Basis for Biliomining: Frameworks for Bringing Together Usage-Based Data Mining and Bibliometrics through Data Warehousing in Usage-Based Data Mining and Bibliometrics through Data Warehousing in Digital Library Services,” Digital Library Services,” Information Processing & Management,Information Processing & Management, 42(3), May 42(3), May 2006, pp 785-804.2006, pp 785-804.

[4] Jane You, Tharam Dillon, James Liu, Edwige Pissaloux, “On Hierarchical [4] Jane You, Tharam Dillon, James Liu, Edwige Pissaloux, “On Hierarchical Multimedia Information Retrieval,” You, J.; Multimedia Information Retrieval,” You, J.; Proceedings of the 2001 Proceedings of the 2001 International Conference on Image ProcessingInternational Conference on Image Processing, 7-10 Oct 2001, pp 729 – 732., 7-10 Oct 2001, pp 729 – 732.

[5] Torsten Priebe and Gunther Pernul, “Ontology-based Integration of OLAP and [5] Torsten Priebe and Gunther Pernul, “Ontology-based Integration of OLAP and Information Retrieval,” Information Retrieval,” Proceedings of the 14Proceedings of the 14thth International Workshop on International Workshop on Database and expert Systems Applications, 2003.Database and expert Systems Applications, 2003.