SLIDE 1IS Fall 2002 Data Warehousing University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management

SLIDE 2IS Fall 2002 Lecture Outline 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)

SLIDE 3IS Fall 2002 Lecture Outline 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)

SLIDE 4IS Fall 2002 Originators Repositories Users Index Services Network A Digital Library Infrastructure Model

SLIDE 5IS Fall 2002 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)

SLIDE 6IS Fall 2002 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.

SLIDE 7IS Fall 2002 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.

SLIDE 8IS Fall 2002 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.

SLIDE 9IS Fall 2002 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.

SLIDE 10IS Fall 2002 Multivalent Documents Cheshire Layer OCR Layer OCR Mapping Layer History of The Classical World 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 Modernjsfj sjjhfjs jsjj jsjhfsjf sslfjksh sshf jsfksfjk sjs jsjfs kj sjfkjsfhskjf sjfhjksh skjfhkjshfjksh jsfhkjshfjkskjfhsfh skjfksjflksjflksjflksf sjfksjfkjskfjskfjklsslk slfjlskfjklsfklkkkdsj GIS Layer taksksh kdjjdkd kdjkdjkd kj sksksk kdkdk kdkd dkk skksksk jdjjdj clclc ldldl taksksh kdjjdkd kdjkdjkd kj sksksk kdkdk kdkd dkk skksksk jdjjdj clclc ldldl Table 1. Table Layer kdk dkd kdk Scanned Page Image Valence: 2: The relative capacity to unite, react, or interact (as with antigens or a biological substrate). Webster’s 7th Collegiate Dictionary Network Protocols & Resources

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SLIDE 14IS Fall 2002 GIS Viewer Example

SLIDE 18IS Fall 2002 Blobworld: use regions for retrieval We want to find general objects  Represent images based on coherent regions

SLIDE 21IS Fall 2002 Lecture Outline 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)

SLIDE 22IS Fall 2002 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

SLIDE 23IS Fall 2002 Problem: Heterogeneous Information Sources “Heterogeneities are everywhere” p Different interfaces p Different data representations p Duplicate and inconsistent information Personal Databases Digital Libraries Scientific Databases World Wide Web Slide credit: J. Hammer

SLIDE 24IS Fall 2002 Problem: Data Management in Large Enterprises Vertical fragmentation of informational systems (vertical stove pipes) Result of application (user)-driven development of operational systems Sales AdministrationFinanceManufacturing... Sales Planning Stock Mngmt... Suppliers... Debt Mngmt Num. Control... Inventory Slide credit: J. Hammer

SLIDE 25IS Fall 2002 Goal: Unified Access to Data Integration System Collects and combines information Provides integrated view, uniform user interface Supports sharing World Wide Web Digital LibrariesScientific Databases Personal Databases Slide credit: J. Hammer

SLIDE 26IS Fall 2002 The Traditional Research Approach Source... Integration System... Metadata Clients Wrapper Query-driven (lazy, on-demand) Slide credit: J. Hammer

SLIDE 27IS Fall 2002 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

SLIDE 28IS Fall 2002 The Warehousing ApproachDataWarehouse Clients Source... Extractor/ Monitor Integration System... Metadata Extractor/ Monitor Extractor/ Monitor Information integrated in advance Stored in WH for direct querying and analysis Slide credit: J. Hammer

SLIDE 29IS Fall 2002 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

SLIDE 30IS Fall 2002 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

SLIDE 31IS Fall 2002 Data Warehouse Evolution TIME Information- Based Management Data Revolution “Middle Ages” “Prehistoric Times” Relational Databases PC’s and Spreadsheets End-user Interfaces 1st DW Article DW Confs. Vendor DW Frameworks Company DWs “Building the DW” Inmon (1992) Data Replication Tools Slide credit: J. Hammer

SLIDE 32IS Fall 2002 What is a Data Warehouse? “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

SLIDE 33IS Fall 2002 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.

SLIDE 34IS Fall 2002 DW Definition… Integrated –The data housed in the data warehouse are defined using consistent Naming conventions Formats Encoding Structures Related Characteristics

SLIDE 35IS Fall 2002 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

SLIDE 36IS Fall 2002 DW Definition… Non-volatile –Data in the data warehouse are loaded and refreshed from operational systems, but cannot be updated by end-users

SLIDE 37IS Fall 2002 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

SLIDE 38IS Fall 2002 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

SLIDE 39IS Fall 2002 … 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

SLIDE 40IS Fall 2002 Warehouse is a Specialized DB 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) Slide credit: J. Hammer

SLIDE 41IS Fall 2002 Summary Operational Systems Enterprise Modeling Business Information Guide Data Warehouse Catalog Data Warehouse Population Data Warehouse Business Information Interface Slide credit: J. Hammer

SLIDE 42IS Fall 2002 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

SLIDE 43IS Fall 2002 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

SLIDE 44IS Fall 2002 Data Warehousing Architecture

SLIDE 45IS Fall 2002 “Ingest”DataWarehouse Clients Source/ FileSource / ExternalSource / DB... Extractor/ Monitor Integration System... Metadata Extractor/ Monitor Extractor/ Monitor

SLIDE 46IS Fall 2002 Data Warehouse Architectures: Conceptual View Single-layer –Every data element is stored once only –Virtual warehouse Two-layer –Real-time + derived data –Most commonly used approach in – industry today “Real-time data” Operational systems Informational systems Derived Data Real-time data Operational systems Informational systems Slide credit: J. Hammer

SLIDE 47IS Fall 2002 Three-layer Architecture: Conceptual View Transformation of real-time data to derived data really requires two steps Derived Data Real-time data Operational systems Informational systems Reconciled Data Physical Implementation of the Data Warehouse View level “Particular informational needs” Slide credit: J. Hammer

SLIDE 48IS Fall 2002 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

SLIDE 49IS Fall 2002 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

SLIDE 50IS Fall 2002 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

SLIDE 51IS Fall 2002 Wrapper pConverts data and queries from one data model to another pExtends query capabilities for sources with limited capabilities Data Model B Data Model A Queries Data Queries Source Wrapper Slide credit: J. Hammer

SLIDE 52IS Fall 2002 Wrapper Generation Solution 1: Hard code for each source Solution 2: Automatic wrapper generation Wrapper Generator Definition Slide credit: J. Hammer

SLIDE 53IS Fall 2002 Data Transformations Convert data to uniform format –Byte ordering, string termination –Internal layout Remove, add & reorder attributes –Add key –Add data to get history Sort tuples Slide credit: J. Hammer

SLIDE 54IS Fall 2002 Monitors Goal: Detect changes of interest and propagate to integrator How? –Triggers –Replication server –Log sniffer –Compare query results –Compare snapshots/dumps Slide credit: J. Hammer

SLIDE 55IS Fall 2002 Data Integration Receive data (changes) from multiple wrappers/monitors and integrate into warehouse Rule-based Actions –Resolve inconsistencies –Eliminate duplicates –Integrate into warehouse (may not be empty) –Summarize data –Fetch more data from sources (wh updates) –etc. Slide credit: J. Hammer

SLIDE 56IS Fall 2002 Data Cleansing Find (& remove) duplicate tuples –e.g., Jane Doe vs. Jane Q. Doe Detect inconsistent, wrong data –Attribute values that don’t match Patch missing, unreadable data Notify sources of errors found Slide credit: J. Hammer

SLIDE 57IS Fall 2002 Warehouse Maintenance Warehouse data  materialized view –Initial loading –View maintenance View maintenance Slide credit: J. Hammer

SLIDE 58IS Fall 2002 Differs from Conventional View Maintenance... Warehouses may be highly aggregated and summarized Warehouse views may be over history of base data Process large batch updates Schema may evolve Slide credit: J. Hammer

SLIDE 59IS Fall 2002 Differs from Conventional View Maintenance... Base data doesn’t participate in view maintenance –Simply reports changes –Loosely coupled –Absence of locking, global transactions –May not be queriable Slide credit: J. Hammer

SLIDE 60IS Fall 2002 SalesComp. Integrator Data Warehouse Sale(item,clerk)Emp(clerk,age) Sold (item,clerk,age) Sold = Sale Emp Slide credit: J. Hammer Warehouse Maintenance Anomalies Materialized view maintenance in loosely coupled, non-transactional environment Simple example

SLIDE 61IS Fall Insert into Emp(Mary,25), notify integrator 2. Insert into Sale (Computer,Mary), notify integrator 3. (1)  integrator adds Sale (Mary,25) 4. (2)  integrator adds (Computer,Mary) Emp 5. View incorrect (duplicate tuple) SalesComp. Integrator Data Warehouse Sale(item,clerk)Emp(clerk,age) Sold (item,clerk,age) Slide credit: J. Hammer Warehouse Maintenance Anomalies

SLIDE 62IS Fall 2002 Slide credit: J. Hammer Maintenance Anomaly - Solutions Incremental update algorithms (ECA, Strobe, etc.) Research issues: Self-maintainable views –What views are self-maintainable –Store auxiliary views so original + auxiliary views are self-maintainable

SLIDE 63IS Fall 2002 Sold(item,clerk,age) = Sale(item,clerk) Emp(clerk,age) Inserts into Emp –If Emp.clerk is key and Sale.clerk is foreign key (with ref. int.) then no effect Inserts into Sale –Maintain auxiliary view: –Emp-  clerk,age (Sold) Deletes from Emp –Delete from Sold based on clerk Self-Maintainability: Examples Slide credit: J. Hammer

SLIDE 64IS Fall 2002 Self-Maintainability: Examples Deletes from Sale Delete from Sold based on {item,clerk} Unless age at time of sale is relevant Auxiliary views for self-maintainability –Must themselves be self-maintainable –One solution: all source data –But want minimal set Slide credit: J. Hammer

SLIDE 65IS Fall 2002 Partial Self-Maintainability Avoid (but don’t prohibit) going to sources Sold=Sale(item,clerk) Emp(clerk,age) Inserts into Sale –Check if clerk already in Sold, go to source if not –Or replicate all clerks over age 30 –Or... Slide credit: J. Hammer

SLIDE 66IS Fall 2002 Warehouse Specification (ideally) Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor Integrator Warehouse... Metadata Warehouse Configuration Module View Definitions Integration rules Change Detection Requirements Slide credit: J. Hammer

SLIDE 67IS Fall 2002 Optimization Update filtering at extractor –Similar to irrelevant updates in constraint and view maintenance Multiple view maintenance –If warehouse contains several views –Exploit shared sub-views Slide credit: J. Hammer

SLIDE 68IS Fall 2002 Additional Research Issues Historical views of non-historical data Expiring outdated information Crash recovery Addition and removal of information sources –Schema evolution Slide credit: J. Hammer

SLIDE 69IS Fall 2002 More Information on DW Agosta, Lou, The Essential Guide to Data Warehousing. Prentise Hall PTR, Devlin, Barry, Data Warehouse, from Architecture to Implementation. Addison-Wesley, Inmon, W.H., Building the Data Warehouse. John Wiley, Widom, J., “Research Problems in Data Warehousing.” Proc. of the 4th Intl. CIKM Conf., Chaudhuri, S., Dayal, U., “An Overview of Data Warehousing and OLAP Technology.” ACM SIGMOD Record, March 1997.