Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY

1. Data Warehousing and Decision Supportcourtesy of Jiawei Han, Larry Kerschberg, and etc. for some slides. Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY

2. Outline • Data Warehouse: Basic Concepts • Data Warehouse Modeling: Data Cube and OLAP • Summary Data Mining: Concepts and Techniques

3. What is a Data 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 Data Mining: Concepts and Techniques

4. 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 Data Mining: Concepts and Techniques

5. 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. Data Mining: Concepts and Techniques

6. 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” Data Mining: Concepts and Techniques

7. Data Warehouse—Nonvolatile • 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 Data Mining: Concepts and Techniques

8. OLTP vs. OLAP Data Mining: Concepts and Techniques

9. Why a 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 • Note: There are more and more systems which perform OLAP analysis directly on relational databases Data Mining: Concepts and Techniques

10. Other sources Extract Transform Load Refresh Operational DBs Data Warehouse: A 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 Data Mining: Concepts and Techniques

11. Extraction, Transformation, and Loading (ETL) • 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 indicies and partitions • Refresh • propagate the updates from the data sources to the warehouse Data Mining: Concepts and Techniques

12. Outline • Data Warehouse: Basic Concepts • Data Warehouse Modeling: Data Cube and OLAP • Data Warehouse Design and Usage • Data Warehouse Implementation • Summary Data Mining: Concepts and Techniques

13. Data Cube Concept • Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab and Sub-Totals • By Jim Gray et al, in IEEE ICDE’1996

14. Aggregate Group By Sum (with total) By Color RED Cross Tab WHITE BLUE By Color Chevy Ford RED The Data Cube and WHITE Sum The Sub-Space Aggregates BLUE By Make 1990 FORD 1991 CHEVY 1992 Sum 1993 By Year By Make By Make & Year RED WHITE BLUE By Color & Year By Make & Color Sum By Color CUBE: A Relational Aggregate Operator Generalizing Group By (from Gray, et al)

15. Data Analysis Tasks • Formulate a query to extract relevant information • Extract the aggregated data from the database into a file. • Visualize the result to look for patterns. • Analyze the result and perhaps formulate new queries.

16. Represent N-dimensional data cubes using 2-D relations Compute the aggregate information within the database, Create language extensions to SQL that will support the required operations. Goal of the Cube

17. Relational Representation of N-Dimensional Data • Weather information consists of four dimensions: Time, Latitude, Longitude and Altitude • Measured variables are Temperature and Pressure. • SQL 1992 Standard supports aggregation functions: • Count (), Sum(), Min(), Max() and AVE(); Some SQL • systems have Median, Standard Deviation, variance, etc. • SELECT AVE(Temp) FROM Weather • SELECT COUNT(DISTINCT Time) FROM Weather

18. Group-By Operation and Aggregates • Group-By operation defines an equivalence relation on a table, partitioning the tuples in classes having the same attribute values. • Each group can then be aggregated. • TCP-D has one 6D GROUP BY and three 3D GROUP Bys. SELECT Time, Altitude, AVE(Temp) FROM Weather GROUP BY Time, Altitude

19. Problems with GROUP-BY • Group-By cannot directly construct: • Histograms, • Roll-up Totals, Sub-totals for Drill-downs, and • Cross Tabulations.

20. Roll-Ups and Sub-Totals for Drill-Down • Tables 3a, 3b, and 4 show Sales Roll Up by Model by Year by Color and an Excel Pivot Table with this information

21. Roll-Ups and Sub-Totals for Drill-Down • Excel Pivot Table • Sales Summary Roll Up in Table 5a and 5b for Chevy over all color and years (Note the ALL value)

22. SQL Query for Chevy Roll Up

23. N-dimensional Aggregate (sum(), max(),...) Fits relational model exactly: a1, a2, ...., aN, f() Super-aggregate over N-1 Dimensional sub-cubes ALL, a2, ...., aN , f() a1 , ALL, a3, ...., aN , f() ... a1, a2, ...., ALL, f() This is the N-1 Dimensional cross-tab. Super-aggregate over N-2 Dimensional sub-cubes ALL, ALL, a3, ...., aN , f() ... a1, a2 ,...., ALL, ALL, f() Cube: Each Attribute is a Dimension

24. Dimension collapse, * denotes ALL Sub-cube Derivation <M,Y,C> <M,Y,*> <*,Y,C> <M,*,C> <*,*,C> <M,*,*> <*,Y,*> <*,*,*>

25. Cube Operator Example CUBE

26. Interesting Aggregate Functions • From RedBrick systems • Rank (in sorted order) • N-Tile (histograms) • Running average (cumulative functions) • Windowed running average • Percent of total • Users want to define their own aggregate functions • statistics • domain specific

27. 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 Data Mining: Concepts and Techniques

28. 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 state_or_province 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 Data Mining: Concepts and Techniques

29. 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 location_key street city_key city_key city state_or_province country 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 Data Mining: Concepts and Techniques

30. 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_state 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 Data Mining: Concepts and Techniques

31. A Concept Hierarchy: Dimension (location) all all Europe ... North_America region Germany ... Spain Canada ... Mexico country Vancouver ... city Frankfurt ... Toronto L. Chan ... M. Wind office Data Mining: Concepts and Techniques

32. Data Cube Measures: Three Categories • Distributive: if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning • E.g., count(), sum(), min(), max() • Algebraic:if it can be computed by an algebraic function with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate function • E.g.,avg(), min_N(), standard_deviation() • Holistic: if there is no constant bound on the storage size needed to describe a subaggregate. • E.g., median(), mode(), rank() Data Mining: Concepts and Techniques

33. Typical OLAP Operations over Data Cube • 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) Data Mining: Concepts and Techniques

34. () (city) (item) (year) (city, item) (city, year) (item, year) (city, item, year) Definition of Cube Operation • A new operator cube by, introduced by Gray et al.’96 SELECT item, city, year, SUM (amount) FROM SALES CUBE BY item, city, year • Need compute the following Group-Bys (date, product, customer), (date,product),(date, customer), (product, customer), (date), (product), (customer) () Data Mining: Concepts and Techniques

35. all 0-D (apex) cuboid time item location supplier 1-D cuboids time,location item,location location,supplier 2-D cuboids time,supplier item,supplier time,location,supplier 3-D cuboids item,location,supplier time,item,supplier 4-D (base) cuboid Cube: A Lattice of Cuboids time,item time,item,location time, item, location, supplier Data Mining: Concepts and Techniques

36. Iceberg Cube • Computing only the cuboid cells whose count or other aggregates satisfy a condition with minimum support, e.g. HAVING COUNT(*) >= minsup Data Mining: Concepts and Techniques

37. Indexing OLAP Data: Bitmap Index • Index on a particular column • Each value in the column has a bit vector: bit-op is fast • The length of the bit vector: # of records in the base table • The i-th bit is set if the i-th row of the base table has the value for the indexed column • A recent bit compression technique, Word-Aligned Hybrid (WAH), makes it work for high cardinality domain as well [Wu, et al. TODS’06] Base table Index on Region Index on Type Data Mining: Concepts and Techniques

38. Indexing OLAP Data: Join Indices • Join index: • JI(R-id, S-id) where R (R-id, …)  S (S-id, …) • In data warehouses, join index relates the values of the dimensions of a star schema to rows in the fact table. • E.g. fact table: Sales and two dimensions city and product • A join index on city maintains for each distinct city a list of R-IDs of the tuples recording the Sales in the city • Join indices can span multiple dimensions Data Mining: Concepts and Techniques

39. Efficient Processing OLAP Queries • Determine which operations should be performed on the available cuboids • Transform drill, roll, etc. into corresponding SQL and/or OLAP operations, e.g., dice = selection + projection • Determine which materialized cuboid(s) should be selected for OLAP op. • Let the query to be processed be on {brand, province_or_state} with the condition “year = 2004”, and there are 4 materialized cuboids available: 1) {year, item_name, city} 2) {year, brand, country} 3) {year, brand, province_or_state} 4) {item_name, province_or_state} where year = 2004 Which should be selected to process the query? • Explore indexing structures and compressed vs. dense array structs in MOLAP Data Mining: Concepts and Techniques

40. OLAP Server Architectures • Relational OLAP (ROLAP) • Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware • Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services • Greater scalability • Multidimensional OLAP (MOLAP) • Sparse array-based multidimensional storage engine • Fast indexing to pre-computed summarized data • Hybrid OLAP (HOLAP)(e.g., Microsoft SQLServer) • Flexibility, e.g., low level: relational, high-level: array Data Mining: Concepts and Techniques

41. Summary • Decision support is an emerging, rapidly growing subarea of databases. • Involves the creation of large, consolidated data repositories called data warehouses. • Warehouses exploited using sophisticated analysis techniques: complex SQL queries and OLAP “multidimensional”queries (influenced by both SQL and spreadsheets). • New techniques for database design, indexing, view maintenance, and interactive querying need to be supported.