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Data Warehousing and Data Mining

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Data Warehousing and Data Mining

  1. 1. Data Warehousing and Data Mining May 2006
  2. 2. Contents <ul><li>Data Warehousing </li></ul><ul><li>OLAP </li></ul><ul><li>Data Mining </li></ul><ul><li>Further Reading </li></ul>
  3. 3. Data Warehousing <ul><li>OLTP (online transaction processing) systems </li></ul><ul><ul><li>range in size from megabytes to terabytes </li></ul></ul><ul><ul><li>high transaction throughput </li></ul></ul><ul><li>Decision makers require access to all data </li></ul><ul><ul><li>Historical and current </li></ul></ul><ul><ul><li>'A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision-making process' (Inmon 1993) </li></ul></ul>
  4. 4. Benefits <ul><li>Potential high returns on investment </li></ul><ul><ul><li>90% of companies in 1996 reported return of investment (over 3 years) of > 40% </li></ul></ul><ul><li>Competitive advantage </li></ul><ul><ul><li>Data can reveal previously unknown, unavailable and untapped information </li></ul></ul><ul><li>Increased productivity of corporate decision-makers </li></ul><ul><ul><li>Integration allows more substantive, accurate and consistent analysis </li></ul></ul>
  5. 5. Comparison Source: Connolly and Begg p1153
  6. 6. Typical Architecture Warehouse mgr Load mgr Warehouse mgr Query manager DBMS Meta-data Highly summarized data Lightly summarized data Detailed data Mainframe operational n/w,h/w data Departmental RDBMS data Private data External data Archive/backup Reporting query, app development,EIS tools OLAP tools Data-mining tools Source: Connolly and Begg p1157
  7. 7. Data Warehouses <ul><li>Types of Data </li></ul><ul><ul><li>Detailed </li></ul></ul><ul><ul><li>Summarised </li></ul></ul><ul><ul><li>Meta-data </li></ul></ul><ul><ul><li>Archive/Back-up </li></ul></ul>
  8. 8. Information Flows Warehouse Mgr Load mgr Warehouse mgr Query manager DBMS Meta- data Highly summ. data Lightly summ. Detailed data Operational data source 1 Operational data source n Archive/backup Reporting query, app development,EIS tools OLAP tools Data-mining tools Meta-flow Inflow Downflow Upflow Outflow Source Connolly and Begg p1162
  9. 9. Information Flow Processes <ul><li>Five primary information flows </li></ul><ul><ul><li>Inflow - extraction, cleansing and loading of data from source systems into warehouse </li></ul></ul><ul><ul><li>Upflow - adding value to data in warehouse through summarizing, packaging and distributing data </li></ul></ul><ul><ul><li>Downflow - archiving and backing up data in warehouse </li></ul></ul><ul><ul><li>Outflow - making data available to end users </li></ul></ul><ul><ul><li>Metaflow - managing the metadata </li></ul></ul>
  10. 10. Problems of Data Warehousing <ul><li>Underestimation of resources for data loading </li></ul><ul><li>Hidden problems with source systems </li></ul><ul><li>Required data not captured </li></ul><ul><li>Increased end-user demands </li></ul><ul><li>Data homogenization </li></ul><ul><li>High demand for resources </li></ul><ul><li>Data ownership </li></ul><ul><li>High maintenance </li></ul><ul><li>Long duration projects </li></ul><ul><li>Complexity of integration </li></ul>
  11. 11. Data Warehouse Design <ul><li>Data must be designed to allow ad-hoc queries to be answered with acceptable performance constraints </li></ul><ul><li>Queries usually require access to factual data generated by business transactions </li></ul><ul><ul><li>e.g. find the average number of properties rented out with a monthly rent greater than £700 at each branch office over the last six months </li></ul></ul><ul><li>Uses Dimensionality Modelling </li></ul>
  12. 12. Dimensionality Modelling <ul><li>Similar to E-R modelling but with constraints </li></ul><ul><ul><li>composed of one fact table with a composite primary key </li></ul></ul><ul><ul><li>dimension tables have a simple primary key which corresponds exactly to one foreign key in the fact table </li></ul></ul><ul><ul><li>uses surrogate keys based on integer values </li></ul></ul><ul><ul><li>Can efficiently and easily support ad-hoc end-user queries </li></ul></ul>
  13. 13. Star Schemas <ul><li>The most common dimensional model </li></ul><ul><li>A fact table surrounded by dimension tables </li></ul><ul><li>Fact tables </li></ul><ul><ul><li>contains FK for each dimension table </li></ul></ul><ul><ul><li>large relative to dimension tables </li></ul></ul><ul><ul><li>read-only </li></ul></ul><ul><li>Dimension tables </li></ul><ul><ul><li>reference data </li></ul></ul><ul><ul><li>query performance speeded up by denormalising into a single dimension table </li></ul></ul>
  14. 14. E-R Model Example Source: Connolly and Begg
  15. 15. Star Schema Example Source: Connolly and Begg
  16. 16. Other Schemas <ul><li>Snowflake schemas </li></ul><ul><ul><li>variant of star schema </li></ul></ul><ul><ul><li>each dimension can have its own dimensions </li></ul></ul><ul><li>Starflake schemas </li></ul><ul><ul><li>hybrid structure </li></ul></ul><ul><ul><li>contains mixture of (denormalised) star and (normalised) snowflake schemas </li></ul></ul>
  17. 17. OLAP <ul><li>Online Analytical Processing </li></ul><ul><ul><li>dynamic synthesis, analysis and consolidation of large volumes of multi-dimensional data </li></ul></ul><ul><ul><li>normally implemented using specialized multi-dimensional DBMS </li></ul></ul><ul><ul><ul><li>a method of visualising and manipulating data with many inter-relationships </li></ul></ul></ul><ul><ul><li>Support common analytical operations such as </li></ul></ul><ul><ul><ul><li>consolidation </li></ul></ul></ul><ul><ul><ul><li>drill-down </li></ul></ul></ul><ul><ul><ul><li>slicing and dicing </li></ul></ul></ul>
  18. 18. Codd’s OLAP Rules <ul><li>1. Multi-dimensional conceptual view </li></ul><ul><li>2. Transparency </li></ul><ul><li>3. Accessibility </li></ul><ul><li>4. Consistent reporting performance </li></ul><ul><li>5. Client-server architecture </li></ul><ul><li>6. Generic dimensionality </li></ul><ul><li>7. Dynamic sparse matrix handling </li></ul><ul><li>8. Multi-user support </li></ul><ul><li>9. Unrestricted cross-dimensional operations </li></ul><ul><li>10. Intuitive data manipulation </li></ul><ul><li>11. Flexible reporting </li></ul><ul><li>12. Unlimited dimensions and aggregation levels </li></ul>
  19. 19. OLAP Tools <ul><li>Categorised according to architecture of underlying database </li></ul><ul><ul><li>Multi-dimensional OLAP </li></ul></ul><ul><ul><ul><li>data typically aggregated and stored according to predicted usage </li></ul></ul></ul><ul><ul><ul><li>use array technology </li></ul></ul></ul><ul><ul><li>Relational OLAP </li></ul></ul><ul><ul><ul><li>use of relational meta-data layer with enhanced SQL </li></ul></ul></ul><ul><ul><li>Managed Query Environment </li></ul></ul><ul><ul><ul><li>deliver data direct from DBMS or MOLAP server to desktop in form of a datacube </li></ul></ul></ul>
  20. 20. MOLAP RDB Server Load MOLAP server Request Result Presentation Layer Database/Application Logic Layer
  21. 21. ROLAP RDB Server ROLAP server Request Result Presentation Layer Application Logic Layer SQL Result Database Layer
  22. 22. MQE RDB Server Load MOLAP server Request Result SQL Result End-user tools
  23. 23. Data Mining <ul><li>‘ The process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions’ (Simoudis, 1996) </li></ul><ul><ul><li>focus is to reveal information which is hidden or unexpected </li></ul></ul><ul><ul><li>patterns and relationships are identified by examining the underlying rules and features of the data </li></ul></ul><ul><ul><li>work from data up </li></ul></ul><ul><ul><li>require large volumes of data </li></ul></ul>
  24. 24. Example Data Mining Applications <ul><li>Retail/Marketing </li></ul><ul><ul><li>Identifying buying patterns of customers </li></ul></ul><ul><ul><li>Finding associations among customer demographic characteristics </li></ul></ul><ul><ul><li>Predicting response to mailing campaigns </li></ul></ul><ul><ul><li>Market basket analysis </li></ul></ul>
  25. 25. Example Data Mining Applications <ul><li>Banking </li></ul><ul><ul><li>Detecting patterns of fraudulent credit card use </li></ul></ul><ul><ul><li>Identifying loyal customers </li></ul></ul><ul><ul><li>Predicting customers likely to change their credit card affiliation </li></ul></ul><ul><ul><li>Determining credit card spending by customer groups </li></ul></ul>
  26. 26. Data Mining Techniques <ul><li>Four main techniques </li></ul><ul><ul><li>Predictive Modelling </li></ul></ul><ul><ul><li>Database Segmentation </li></ul></ul><ul><ul><li>Link Analysis </li></ul></ul><ul><ul><li>Deviation Direction </li></ul></ul>
  27. 27. Data Mining Techniques <ul><li>Predictive Modelling </li></ul><ul><ul><li>using observations to form a model of the important characteristics of some phenomenon </li></ul></ul><ul><li>Techniques: </li></ul><ul><ul><li>Classification </li></ul></ul><ul><ul><li>Value Prediction </li></ul></ul>
  28. 28. Classification Example- Tree Induction Customer renting property > 2 years Rent property Rent property Buy property Customer age > 25 years? No Yes No Yes Source: Connolly and Begg
  29. 29. Data Mining Techniques <ul><li>Database Segmentation: </li></ul><ul><ul><li>to partition a database into an unknown number of segments (or clusters) of records which share a number of properties </li></ul></ul><ul><li>Techniques: </li></ul><ul><ul><li>Demographic clustering </li></ul></ul><ul><ul><li>Neural clustering </li></ul></ul>
  30. 30. Segmentation: Scatterplot Example Source: Connolly and Begg
  31. 31. Data Mining Techniques <ul><li>Link Analysis </li></ul><ul><ul><li>establish associations between individual records (or sets of records) in a database </li></ul></ul><ul><ul><ul><li>e.g. ‘when a customer rents property for more than two years and is more than 25 years old, then in 40% of cases, the customer will buy the property’ </li></ul></ul></ul><ul><ul><li>Techniques </li></ul></ul><ul><ul><ul><li>Association discovery </li></ul></ul></ul><ul><ul><ul><li>Sequential pattern discovery </li></ul></ul></ul><ul><ul><ul><li>Similar time sequence discovery </li></ul></ul></ul>
  32. 32. Data Mining Techniques <ul><li>Deviation Detection </li></ul><ul><ul><li>identify ‘outliers’, something which deviates from some known expectation or norm </li></ul></ul><ul><ul><li>Statistics </li></ul></ul><ul><ul><li>Visualisation </li></ul></ul>
  33. 33. Deviation Detection: Visualisation Example Source: Connolly and Begg
  34. 34. Mining and Warehousing <ul><li>Data mining needs single, separate, clean, integrated, self-consistent data source </li></ul><ul><li>Data warehouse well equipped: </li></ul><ul><ul><li>populated with clean, consistent data </li></ul></ul><ul><ul><li>contains multiple sources </li></ul></ul><ul><ul><li>utilises query capabilities </li></ul></ul><ul><ul><li>capability to go back to data source </li></ul></ul>
  35. 35. Further Reading <ul><li>Connolly and Begg, chapters 31 to 34. </li></ul><ul><li>W H Inmon, Building the Data Warehouse , New York, Wiley and Sons, 1993. </li></ul><ul><li>Benyon-Davies P, Database Systems (2 nd ed), Macmillan Press, 2000, ch 34, 35 & 36. </li></ul>

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