Data Warehousing

1. Data Warehousing

2. Evolution of Database Technology • 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 is the correlation between expenditures and collection of delinquent taxes? What are the effects of outsourcing specific services? 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. more 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

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. Client Client Query & Analysis Warehouse Integration Source Source Source Warehouse Architecture 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 0-D(apex) cuboid time item location supplier 1-D cuboids time,item time,location item,location location,supplier 2-D cuboids time,supplier item,supplier time,location,supplier time,item,location 3-D cuboids item,location,supplier time,item,supplier 4-D(base) cuboid time, item, location, supplier

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

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

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

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

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

28. 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 MOLAP versus ROLAP

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 Knowledge Pattern Evaluation • Data mining: the core of knowledge discovery process. Data Mining Task-relevant Data Selection Data Warehouse Data Cleaning Data Integration Databases

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 Making Decisions Business Analyst Data Presentation Visualization Techniques Data Mining Data Analyst Information Discovery Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA DBA 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