1. Data Warehousing Xintao Wu

2. Evolution of Database Technology (See Fig. 1.1) 1960s: Data collection, database creation, IMS and network DBMS 1970s: Relational data model, relational DBMS implementation 1980s: RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s—2000s: Data mining and data warehousing, multimedia databases, and Web databases

3. Can You Easily Answer These Questions? What are Personnel Services costs across all departments for all funding sources? What are the effects of outsourcing specific services? What is the correlation between expenditures and collection of delinquent taxes? What is the impact on revenues and expenditures of changing the operating hours of the Dept. of Motor Vehicles? What is the economic impact of the small business initiative in our district?

4. Overview: Data Warehousing and OLAP Technology for Data Mining What is a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining

5. What is a Warehouse? Collection of diverse data subject oriented aimed at executive, decision maker often a copy of operational data with value-added data (e.g., summaries, history) integrated time-varying non-volatile more

6. What is a Warehouse? Collection of tools gathering data cleansing, integrating,... querying, reporting, analysis data mining monitoring, administering warehouse

7. Data Warehouse vs. Operational DBMS OLTP (on-line transaction processing) Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. OLAP (on-line analytical processing) Major task of data warehouse system Data analysis and decision making Distinct features (OLTP vs. OLAP): User and system orientation: customer vs. market Data contents: current, detailed vs. historical, consolidated Database design: ER + application vs. star + subject View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries

8. OLTP vs. OLAP

9. Overview: Data Warehousing and OLAP Technology for Data Mining What a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining

10. Why Separate Data Warehouse? High performance for both systems DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation. Different functions and different data: missing data: Decision support requires historical data which operational DBs do not typically maintain data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled

11. Warehouse Architecture Client Warehouse Source Query & Analysis Integration Metadata

12. Advantages of Warehousing High query performance Queries not visible outside warehouse Local processing at sources unaffected Can operate when sources unavailable Can query data not stored in a DBMS Extra information at warehouse Modify, summarize (store aggregates) Add historical information

13. Overview: Data Warehousing and OLAP Technology for Data Mining What a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining

14. Modeling OLTP Systems Goal -- Update as many transactions as possible in the shortest period of time Approach Model to 3rd Normal Form (3NF) Minimize redundancy to optimize update Result Create many (hundreds) of tables Difficult for business users to understand and use Retrieval requires many JOINs = lousy performance

15. Modeling the Data Warehouse Tuning the relational model Denormalize – Reduces the number of tables – Improves usability – Improves performance Add aggregate data (typically separate tables) – Improves performance – Degrades usability

16. From Tables and Spreadsheets to Data Cubes A data warehouse is based on a multidimensional data model which views data in the form of a data cube A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year) Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables In data warehousing literature, an n-D base cube is called a base cuboid. The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid. The lattice of cuboids forms a data cube.

17. Cube: A Lattice of Cuboids all timeitemlocationsupplier time,itemtime,location time,supplier item,location item,supplier location,supplier time,item,location time,item,supplier time,location,supplier item,location,supplier time, item, location, supplier 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D cuboids 4-D(base) cuboid

18. Conceptual Modeling of Data Warehouses Modeling data warehouses: dimensions & measures Star schema: A fact table in the middle connected to a set of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation

19. Example of Star Schema time_key day day_of_the_week month quarter year time location_key street city province_or_street country location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type item branch_key branch_name branch_type branch

20. Example of Snowflake Schema time_key day day_of_the_week month quarter year time location_key street city_key location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_key item branch_key branch_name branch_type branch supplier_key supplier_type supplier city_key city province_or_street country city

21. Example of Fact Constellation time_key day day_of_the_week month quarter year time location_key street city province_or_street country location Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type item branch_key branch_name branch_type branch Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped shipper_key shipper_name location_key shipper_type shipper

22. Typical OLAP Operations Roll up (drill-up): summarize data by climbing up hierarchy or by dimension reduction Drill down (roll down): reverse of roll-up from higher level summary to lower level summary or detailed data, or introducing new dimensions Slice and dice: project and select Pivot (rotate): reorient the cube, visualization, 3D to series of 2D planes. Other operations drill across: involving (across) more than one fact table drill through: through the bottom level of the cube to its back- end relational tables (using SQL)

23. Relational Operators Select Project Join

24. Overview: Data Warehousing and OLAP Technology for Data Mining What a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining

25. Multi-Tiered Architecture Data Warehouse Extract Transform Load Refresh OLAP Engine Analysis Query Reports Data mining Monitor & Integrator Metadata Data Sources Front-End Tools Serve Data Marts Operational DBs other sources Data Storage OLAP Server

26. OLAP Server Architectures Relational OLAP (ROLAP) ROLAP - provides a Multi-dimensional view of a relational DB (e.g. MicroStrategy ) Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware to support missing pieces Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services greater scalability Multidimensional OLAP (MOLAP) Array-based multidimensional storage engine (sparse matrix techniques) fast indexing to pre-computed summarized data Hybrid OLAP (HOLAP) User flexibility, e.g., low level: relational, high-level: array Specialized SQL servers specialized support for SQL queries over star/snowflake schemas

27. MOLAP Databases n Data is stored using a proprietary format(MOLAP) n Accessible only through the DB vendor’s tools n Suitable only for summarized data n Data may be summarized in advance or real-time n Examples: s PowerPlay s Holos s Essbase

28. MOLAP versus ROLAP MOLAP Multidimensional OLAP Data stored in multi- dimensional cube Transformation required Data retrieved directly from cube for analysis Faster analytical processing Cube size limitations ROLAP Relational OLAP Data stored in relational database as virtual cube No transformation needed Data retrieved via SQL from database for analysis Slower analytical processing No size limitations

29. Data Warehouse Back-End Tools and Utilities Data extraction: get data from multiple, heterogeneous, and external sources Data cleaning: detect errors in the data and rectify them when possible Data transformation: convert data from legacy or host format to warehouse format Load: sort, summarize, consolidate, compute views, check integrity, and build indices and partitions Refresh propagate the updates from the data sources to the warehouse

30. Overview: Data Warehousing and OLAP Technology for Data Mining What a data warehouse? Why a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehouse to data mining

31. Data Mining: A KDD Process Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation

32. Steps of a KDD Process Learning the application domain: relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation: Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge

33. Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP

34. Summary Data warehouse A subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process A multi-dimensional model of a data warehouse Star schema, snowflake schema, fact constellations A data cube consists of dimensions & measures OLAP operations: drilling, rolling, slicing, dicing and pivoting OLAP servers: ROLAP, MOLAP, HOLAP From OLAP to OLAM