CSE 5331/7331 F'071 CSE 5331/7331 Fall 2007 Dimensional Modeling Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist University Some slides extracted from Data Mining, Introductory and Advanced Topics, Prentice Hall, 2002.

2CSE 5331/7331 F'07 Dimensional Modeling View data in a hierarchical manner more as business executives might View data in a hierarchical manner more as business executives might Useful in decision support systems and mining Useful in decision support systems and mining Dimension: collection of logically related attributes; axis for modeling data. Dimension: collection of logically related attributes; axis for modeling data. Facts: data stored Facts: data stored Ex: Dimensions – products, locations, date Ex: Dimensions – products, locations, date Facts – quantity, unit price Facts – quantity, unit price

3CSE 5331/7331 F'07 Multidimensional Model Example Fig 2 [1]

4CSE 5331/7331 F'07 Cube view of Data Fig 4 [1]

5CSE 5331/7331 F'07 Aggregation Hierarchies

6CSE 5331/7331 F'07 Multidimensional Schemas Star Schema shows facts and dimensions Star Schema shows facts and dimensions –Center of the star has facts shown in fact tables –Outside of the facts, each diemnsion is shown separately in dimension tables –Access to fact table from dimension table via join SELECT Quantity, Price FROM Facts, Location Where (Facts.LocationID = Location.LocationID) and (Location.City = ‘Dallas’) –View as relations, problem volume of data and indexing

7CSE 5331/7331 F'07 Star Schema

8CSE 5331/7331 F'07 Flattened Star

9CSE 5331/7331 F'07 Normalized Star

10CSE 5331/7331 F'07 Snowflake Schema

11CSE 5331/7331 F'07 OLAP Introduction OLAP by Example OLAP by Example h/index.htm h/index.htm What is OLAP? What is OLAP?

12CSE 5331/7331 F'07 OLAP Online Analytic Processing (OLAP): provides more complex queries than OLTP. Online Analytic Processing (OLAP): provides more complex queries than OLTP. OnLine Transaction Processing (OLTP): traditional database/transaction processing. OnLine Transaction Processing (OLTP): traditional database/transaction processing. Dimensional data; cube view Dimensional data; cube view Support ad hoc querying Support ad hoc querying Require analysis of data Require analysis of data Can be thought of as an extension of some of the basic aggregation functions available in SQL Can be thought of as an extension of some of the basic aggregation functions available in SQL OLAP tools may be used in DSS systems OLAP tools may be used in DSS systems Mutlidimentional view is fundamental Mutlidimentional view is fundamental

13CSE 5331/7331 F'07 OLAP Implementations MOLAP (Multidimensional OLAP) MOLAP (Multidimensional OLAP) –Multidimential Database (MDD) –Specialized DBMS and software system capable of supporting the multidimensional data directly –Data stored as an n-dimensional array (cube) –Indexes used to speed up processing ROLAP (Relational OLAP) ROLAP (Relational OLAP) –Data stored in a relational database –ROLAP server (middleware) creates the multidimensional view for the user –Less Complex; Less efficient HOLAP (Hybrid OLAP) HOLAP (Hybrid OLAP) –Not updated frequently – MDD –Updated frequently - RDB

14CSE 5331/7331 F'07 OLAP Operations Single CellMultiple CellsSliceDice Roll Up Drill Down

15CSE 5331/7331 F'07 OLAP Operations Simple query – single cell in the cube Simple query – single cell in the cube Slice – Look at a subcube to get more specific information Slice – Look at a subcube to get more specific information Dice – Rotate cube to look at another dimension Dice – Rotate cube to look at another dimension Roll Up – Dimension Reduction; Aggregation Roll Up – Dimension Reduction; Aggregation Drill Down Drill Down Visualization: These operations allow the OLAP users to actually “see” results of an operation. Visualization: These operations allow the OLAP users to actually “see” results of an operation.

16CSE 5331/7331 F'07 Relationship Between Topcs

17CSE 5331/7331 F'07 Decision Support Systems Tools and computer systems that assist management in decision making Tools and computer systems that assist management in decision making What if types of questions What if types of questions High level decisions High level decisions Data warehouse – data which supports DSS Data warehouse – data which supports DSS

18CSE 5331/7331 F'07 Starflake Fig 2 [4]

19CSE 5331/7331 F'07 Hierarchy of Data Cubes Fig 4 [4]

20CSE 5331/7331 F'07 Unified Dimensional Model Microsoft Cube View Microsoft Cube View SQL Server 2005 SQL Server us/library/ms aspx us/library/ms aspx ChsJ1un_2s41jm9Iyg!325.entry ChsJ1un_2s41jm9Iyg!325.entry MDX AS2005 MDX AS us/library/aa216767(SQL.80).aspx us/library/aa216767(SQL.80).aspx

21CSE 5331/7331 F'07 Bibliography [1] Anne-Muriel Arigon, Anne Tchounikine, and Maryvonne Miquel, “Handling Multiple Points of View in a Multimedia Data Warehouse,” ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 2, No. 3, August 2006, Pages 199–218. [2] S. Nicholson, “The Bibliomining Process: Data Warehousing and Data Mining for Library Decision-Making,” Information Technology and Libraries, 22(4), [3] S. Nicholson, “The Basis for Biliomining: Frameworks for Bringing Together Usage-Based Data Mining and Bibliometrics through Data Warehousing in Digital Library Services,” Information Processing & Management, 42(3), May 2006, pp [4] Jane You, Tharam Dillon, James Liu, Edwige Pissaloux, “On Hierarchical Multimedia Information Retrieval,” You, J.; Proceedings of the 2001 International Conference on Image Processing, 7-10 Oct 2001, pp 729 – 732. [5] Torsten Priebe and Gunther Pernul, “Ontology-based Integration of OLAP and Information Retrieval,” Proceedings of the 14 th International Workshop on Database and expert Systems Applications, 2003.