Data Warehousing

1. Data Warehousing University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management

2. Review Database Applications: Berkeley’s Environmental Digital Library Data Warehouses Introduction to Data Warehouses Data Warehousing (Based on lecture notes from Joachim Hammer, University of Florida, and Joe Hellerstein and Mike Stonebraker of UCB) Lecture Outline

3. Review Database Applications: Berkeley’s Environmental Digital Library Data Warehouses Introduction to Data Warehouses Data Warehousing (Based on lecture notes from Joachim Hammer, University of Florida, and Joe Hellerstein and Mike Stonebraker of UCB) Lecture Outline

4. A Digital Library Infrastructure Model Originators Index Services Repositories Network Users

5. UC Berkeley Digital Library Project • Focus: Work-centered digital information services • Testbed: Digital Library for the California Environment • Research: Technical agenda supporting user-oriented access to large distributed collections of diverse data types. • Part of the NSF/NASA/DARPA Digital Library Initiative (Phases 1 and 2)

6. The Environmental Library - Contents • As of late 2002, the collection represents over one terabyte of data, including over 183,000 digital images, about 300,000 pages of environmental documents, and over 2 million records in geographical and botanical databases.

7. Botanical Data: • The CalFlora Database contains taxonomical and distribution information for more than 8000 native California plants. The Occurrence Database includes over 600,000 records of California plant sightings from many federal, state, and private sources. The botanical databases are linked to the CalPhotos collection of California plants, and are also linked to external collections of data, maps, and photos.

8. Geographical Data: • Much of the geographical data in the collection has been used to develop our web-based GIS Viewer. The Street Finder uses 500,000 Tiger records of S.F. Bay Area streets along with the 70,000-records from the USGS GNIS database. California Dams is a database of information about the 1395 dams under state jurisdiction. An additional 11 GB of geographical data represents maps and imagery that have been processed for inclusion as layers in our GIS Viewer. This includes Digital Ortho Quads and DRG maps for the S.F. Bay Area.

9. Documents: • Most of the 300,000 pages of digital documents are environmental reports and plans that were provided by California state agencies. This collection includes documents, maps, articles, and reports on the California environment including Environmental Impact Reports (EIRs), educational pamphlets, water usage bulletins, and county plans. Documents in this collection come from the California Department of Water Resources (DWR), California Department of Fish and Game (DFG), San Diego Association of Governments (SANDAG), and many other agencies. Among the most frequently accessed documents are County General Plans for every California county and a survey of 125 Sacramento Delta fish species.

10. Multivalent Documents Network Protocols & Resources Cheshire Layer GIS Layer Table Layer OCR Layer OCR Mapping Layer Valence: 2: The relative capacity to unite, react, or interact (as with antigens or a biological substrate). Webster’s 7th Collegiate Dictionary History of The Classical World kdk dkd kdk Modernjsfj sjjhfjs jsjj jsjhfsjf sslfjksh sshf jsfksfjk sjs jsjfs kj sjfkjsfhskjf sjfhjksh skjfhkjshfjksh jsfhkjshfjkskjfhsfh skjfksjflksjflksjflksf sjfksjfkjskfjskfjklsslk slfjlskfjklsfklkkkdsj The jsfj sjjhfjs jsjj jsjhfsjf sjhfjksh sshf jsfksfjk sjs jsjfs kj sjfkjsfhskjf sjfhjksh skjfhkjshfjksh jsfhkjshfjkskjfhsfh skjfksjflksjflksjflksf sjfksjfkjskfjskfjklsslk slfjlskfjklsfklkkkdsj ksfksjfkskflk sjfjksf kjsfkjsfkjshf sjfsjfjks ksfjksfjksjfkthsjir\\ ks ksfjksjfkksjkls’ks klsjfkskfksjjjhsjhuu sfsjfkjs Scanned Page Image taksksh kdjjdkd kdjkdjkd kj sksksk kdkdk kdkd dkk skksksk jdjjdj clclc ldldl Table 1.

14. GIS Viewer Example http://elib.cs.berkeley.edu/annotations/gis/buildings.html

18. Blobworld: use regions for retrieval • We want to find general objects Represent images based on coherent regions

21. Review Database Applications: Berkeley’s Environmental Digital Library Data Warehouses Introduction to Data Warehouses Data Warehousing (Based on lecture notes from Joachim Hammer, University of Florida, and Joe Hellerstein and Mike Stonebraker of UCB) Lecture Outline

22. Overview • Data Warehouses and Merging Information Resources • What is a Data Warehouse? • History of Data Warehousing • Types of Data and Their Uses • Data Warehouse Architectures • Data Warehousing Problems and Issues

23. Problem: Heterogeneous Information Sources “Heterogeneities are everywhere” Personal Databases World Wide Web Scientific Databases Digital Libraries • Different interfaces • Different data representations • Duplicate and inconsistent information Slide credit: J. Hammer

24. Problem: Data Management in Large Enterprises • Vertical fragmentation of informational systems (vertical stove pipes) • Result of application (user)-driven development of operational systems Sales Planning Suppliers Num. Control Stock Mngmt Debt Mngmt Inventory ... ... ... Sales Administration Finance Manufacturing ... Slide credit: J. Hammer

25. Goal: Unified Access to Data Integration System World Wide Web Personal Databases Digital Libraries Scientific Databases • Collects and combines information • Provides integrated view, uniform user interface • Supports sharing Slide credit: J. Hammer

26. The Traditional Research Approach • Query-driven (lazy, on-demand) Clients Metadata Integration System . . . Wrapper Wrapper Wrapper . . . Source Source Source Slide credit: J. Hammer

27. Disadvantages of Query-Driven Approach • Delay in query processing • Slow or unavailable information sources • Complex filtering and integration • Inefficient and potentially expensive for frequent queries • Competes with local processing at sources • Hasn’t caught on in industry Slide credit: J. Hammer

28. The Warehousing Approach Clients Data Warehouse Metadata Integration System . . . Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor . . . Source Source Source • Information integrated in advance • Stored in WH for direct querying and analysis Slide credit: J. Hammer

29. Advantages of Warehousing Approach • High query performance • But not necessarily most current information • Doesn’t interfere with local processing at sources • Complex queries at warehouse • OLTP at information sources • Information copied at warehouse • Can modify, annotate, summarize, restructure, etc. • Can store historical information • Security, no auditing • Has caught on in industry Slide credit: J. Hammer

30. Not Either-Or Decision • Query-driven approach still better for • Rapidly changing information • Rapidly changing information sources • Truly vast amounts of data from large numbers of sources • Clients with unpredictable needs Slide credit: J. Hammer

31. Data Warehouse Evolution “Building the DW” Inmon (1992) Data Replication Tools Relational Databases Company DWs 1960 1975 1980 1985 1990 1995 2000 Information- Based Management Data Revolution “Prehistoric Times” “Middle Ages” TIME PC’s and Spreadsheets End-user Interfaces 1st DW Article DW Confs. Vendor DW Frameworks Slide credit: J. Hammer

32. “A Data Warehouse is a subject-oriented, integrated, time-variant, non-volatile collection of data used in support of management decision making processes.” -- Inmon & Hackathorn, 1994: viz. Hoffer, Chap 11 What is a Data Warehouse?

33. DW Definition… • Subject-Oriented: • The data warehouse is organized around the key subjects (or high-level entities) of the enterprise. Major subjects include • Customers • Patients • Students • Products • Etc.

34. DW Definition… • Integrated • The data housed in the data warehouse are defined using consistent • Naming conventions • Formats • Encoding Structures • Related Characteristics

35. DW Definition… • Time-variant • The data in the warehouse contain a time dimension so that they may be used as a historical record of the business

36. DW Definition… • Non-volatile • Data in the data warehouse are loaded and refreshed from operational systems, but cannot be updated by end-users

37. What is a Data Warehouse?A Practitioners Viewpoint • “A data warehouse is simply a single, complete, and consistent store of data obtained from a variety of sources and made available to end users in a way they can understand and use it in a business context.” • -- Barry Devlin, IBM Consultant Slide credit: J. Hammer

38. A Data Warehouse is... • Stored collection of diverse data • A solution to data integration problem • Single repository of information • Subject-oriented • Organized by subject, not by application • Used for analysis, data mining, etc. • Optimized differently from transaction-oriented db • User interface aimed at executive decision makers and analysts

39. … Cont’d • Large volume of data (Gb, Tb) • Non-volatile • Historical • Time attributes are important • Updates infrequent • May be append-only • Examples • All transactions ever at WalMart • Complete client histories at insurance firm • Stockbroker financial information and portfolios Slide credit: J. Hammer

40. Standard DB Mostly updates Many small transactions Mb - Gb of data Current snapshot Index/hash on p.k. Raw data Thousands of users (e.g., clerical users) Warehouse Mostly reads Queries are long and complex Gb - Tb of data History Lots of scans Summarized, reconciled data Hundreds of users (e.g., decision-makers, analysts) Warehouse is a Specialized DB Slide credit: J. Hammer

41. Summary Business Information Guide Business Information Interface Data Warehouse Data Warehouse Catalog Data Warehouse Population Enterprise Modeling Operational Systems Slide credit: J. Hammer

42. Warehousing and Industry • Warehousing is big business • $2 billion in 1995 • $3.5 billion in early 1997 • Predicted: $8 billion in 1998 [Metagroup] • WalMart has largest warehouse • 900-CPU, 2,700 disk, 23 TB Teradata system • ~7TB in warehouse • 40-50GB per day Slide credit: J. Hammer

43. Types of Data • Business Data - represents meaning • Real-time data (ultimate source of all business data) • Reconciled data • Derived data • Metadata - describes meaning • Build-time metadata • Control metadata • Usage metadata • Data as a product* - intrinsic meaning • Produced and stored for its own intrinsic value • e.g., the contents of a text-book Slide credit: J. Hammer

44. Data Warehousing Architecture

45. “Ingest” Clients Data Warehouse Metadata Integration System . . . Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor . . . Source/ File Source / DB Source / External

46. Data Warehouse Architectures: Conceptual View Operational systems Informational systems “Real-time data” Operational systems Informational systems Derived Data Real-time data • Single-layer • Every data element is stored once only • Virtual warehouse • Two-layer • Real-time + derived data • Most commonly used approach in • industry today Slide credit: J. Hammer

47. Three-layer Architecture: Conceptual View Operational systems Informational systems Derived Data Reconciled Data Real-time data • Transformation of real-time data to derived data really requires two steps View level “Particular informational needs” Physical Implementation of the Data Warehouse Slide credit: J. Hammer

48. Issues in Data Warehousing • Warehouse Design • Extraction • Wrappers, monitors (change detectors) • Integration • Cleansing & merging • Warehousing specification & Maintenance • Optimizations • Miscellaneous (e.g., evolution) Slide credit: J. Hammer

49. Data Warehousing: Two Distinct Issues • (1) How to get information into warehouse • “Data warehousing” • (2) What to do with data once it’s in warehouse • “Warehouse DBMS” • Both rich research areas • Industry has focused on (2) Slide credit: J. Hammer

50. Data Extraction • Source types • Relational, flat file, WWW, etc. • How to get data out? • Replication tool • Dump file • Create report • ODBC or third-party “wrappers” Slide credit: J. Hammer