ITCS 6163 Data Warehousing

1. ITCS 6163 Data Warehousing Xintao Wu

2. History 60s C. Bachman GE network data model Late 60s IBM IMS hierarchical data model 70 E.Codd relational model 80s SQL IBM R transaction J. Gray Late 80s-90s DB2, Oracle, informix, sybase 90s- DW, internet Turing award and Turing test?

3. 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

4. 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?

5. 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

6. 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

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

8. What is a Warehouse? • Defined in many different ways, but not rigorously. • A decision support database that is maintained separately from the organization’s operational database • Support information processing by providing a solid platform of consolidated, historical data for analysis. • “A data warehouse is asubject-oriented, integrated, time-variant, and nonvolatilecollection of data in support of management’s decision-making process.”—W. H. Inmon • Data warehousing: • The process of constructing and using data warehouses

9. Data Warehouse—Subject-Oriented • Organized around major subjects, such as customer, product, sales. • Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing. • Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

10. Data Warehouse—Integrated • Constructed by integrating multiple, heterogeneous data sources • relational databases, flat files, on-line transaction records • Data cleaning and data integration techniques are applied. • Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources • E.g., Hotel price: currency, tax, breakfast covered, etc. • When data is moved to the warehouse, it is converted.

11. Data Warehouse—Time Variant • The time horizon for the data warehouse is significantly longer than that of operational systems. • Operational database: current value data. • Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) • Every key structure in the data warehouse • Contains an element of time, explicitly or implicitly • But the key of operational data may or may not contain “time element”.

12. Data Warehouse—Non-Volatile • A physically separate store of data transformed from the operational environment. • Operational update of data does not occur in the data warehouse environment. • Does not require transaction processing, recovery, and concurrency control mechanisms • Requires only two operations in data accessing: • initial loading of data and access of data.

13. Mostly updates Many small transactions Mb-Tb of data Current snapshot Raw data Clerical users Mostly reads Queries are long, complex Gb-Tb of data History Summarized, consolidated data Decision-makers, analysts as users Warehouse is specialized DB Standard DB Warehouse

14. Data Warehouse vs. Heterogeneous DBMS • Traditional heterogeneous DB integration: • Build wrappers/mediators on top of heterogeneous databases • Query driven approach • When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set • Complex information filtering, compete for resources • Data warehouse: update-driven, high performance • Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis

15. 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

16. OLTP vs. OLAP

17. 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

18. 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

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

20. ? Source Source Why a Warehouse? • Two Approaches: • Query-Driven (Eager) • Warehouse (Lazy)

21. Client Client Mediator Wrapper Wrapper Wrapper Source Source Source Query-Driven Approach

22. 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

23. Advantages of Query-Driven • No need to copy data • less storage • no need to purchase data • More up-to-date data • Query needs can be unknown • Only query interface needed at sources

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. 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

26. 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

27. Modeling the Data Warehouse “Entity relation data models are a disaster for querying because they cannot be understood by users and they cannot be navigated usefully by DBMS software. Entity relation models cannot be used as the basis for enterprise data warehouses.” Ralph Kimball, The Data Warehouse Toolkit, 1996, John Wiley & Sons, Inc., ISBN 0-471-15337-0

28. 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.

29. 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

30. 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

31. 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

32. 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

33. 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

34. Multidimensional Data • Sales volume as a function of product, month, and region Dimensions: Product, Location, Time Hierarchical summarization paths Region Industry Region Year Category Country Quarter Product City Month Week Office Day Product Month

35. Date 2Qtr 1Qtr sum 3Qtr 4Qtr TV Product U.S.A PC VCR sum Canada Country Mexico sum All, All, All A Sample Data Cube Total annual sales of TV in U.S.A.

36. Cuboids Corresponding to the Cube all 0-D(apex) cuboid country product date 1-D cuboids product,date product,country date, country 2-D cuboids 3-D(base) cuboid product, date, country

37. 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)

38. Relational Operators • Select • Project • Join

39. Aggregates • Add up amounts by day • In SQL: SELECT date, sum(amt) FROM SALE • GROUP BY date

40. Another Example • Add up amounts by day, product • In SQL: SELECT date, sum(amt) FROM SALE • GROUP BY date, prodId rollup drill-down

41. Aggregates • Operators: sum, count, max, min, median, avg • Type • Distributive • Algebraic • holistic • “Having” clause • Using dimension hierarchy • average by region (within store) • maximum by month (within date)

42. rollup drill-down Cube Aggregation Example: computing sums day 2 . . . day 1 129

43. day 2 day 1 Aggregation Using Hierarchies customer region country (customer c1 in Region A; customers c2, c3 in Region B)

44. day 2 day 1 Pivoting Fact table view: Multi-dimensional cube:

45. 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

46. Design of a Data Warehouse: A Business Analysis Framework • Four views regarding the design of a data warehouse • Top-down view • allows selection of the relevant information necessary for the data warehouse • Data source view • exposes the information being captured, stored, and managed by operational systems • Data warehouse view • consists of fact tables and dimension tables • Business query view • sees the perspectives of data in the warehouse from the view of end-user

47. Data Warehouse Design Process • Top-down, bottom-up approaches or a combination of both • Top-down: Starts with overall design and planning (mature) • Bottom-up: Starts with experiments and prototypes (rapid) • From software engineering point of view • Waterfall: structured and systematic analysis at each step before proceeding to the next • Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around • Typical data warehouse design process • Choose a business process to model, e.g., orders, invoices, etc. • Choose the grain (atomic level of data) of the business process • Choose the dimensions that will apply to each fact table record • Choose the measure that will populate each fact table record

48. 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

49. Three Data Warehouse Models • Enterprise warehouse • collects all of the information about subjects spanning the entire organization • Data Mart • a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart • Independent vs. dependent (directly from warehouse) data mart • Virtual warehouse • A set of views over operational databases • Only some of the possible summary views may be materialized

50. Finance HR Budget Info. Tech. Asset Mgmt. Purch- asing What is a Data Mart ? • A data mart is a small-scale data warehouse that is focused on a single department or single subject area to provide a subset of data warehouse data to address specific reporting and analysis requirements. • Smaller warehouses • Spans part of organization • Do not require enterprise-wide consensus • but long term integration problems?