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  • Avondale College Data Warehouse Project
    Computing by David Heise 23 December 1996 Macquarie University Table of Contents List of Figures List of Tables Acknowledgments Executive Summary 1 Introduction 2 Motivation For This Project 3 Data Warehousing Principles 4 Data Warehousing At Avondale 5 Pilot Review

    Original URL path: http://dheise.andrews.edu/dw/Avondale/ACDWTOC.html (2013-06-13)
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  • Script of Data Warehousing Presentation
    to stages 2 and 3 Users will be able to perform data enquiry using Impromptu without having to wait for special programs to be written by IT As further questions are raised in response to this users will be able to refine their questioning with further enquiries and will be better able to interpret the data Decision makers will be able to perform strategic analysis on multi dimensional data models using PowerPlay Notice that as an organization moves through the stages of decision support and achieves higher levels of sophistication in their use of data the lower levels are not made redundant There will always be a place for standard operational reports Knowledge workers will always benefit from having easy to use reporting tools and so on N DW7 Data Warehousing The Data View 1 15 This is a simplified view of how data from the various sources is taken into the data warehouse and is then accessible to end users for reporting and analysis and corresponds to a model we had proposed early in our project We later learned that a database to support multi dimensional analysis in PowerPlay was quite different from one supporting knowledge workers writing reports in Impromptu A data warehouse suitable for multi dimensional analysis is denormalized in several ways coded data is replaced with values or meanings the complex joins are done ahead of time and stored as tables this requires a good understanding of requirements including anticipated queries as well as the ability to adapt and evolve as the requirements develop and thirdly significant aggregation and summarization occurs As a result the data warehouse we are building consists of two parts there is an integrated relational view of all operational data with history for Impromptu and there are summarized and pre joined data to support multi dimensional analysis for PowerPlay N DW8 Data Warehousing The Process View 2 16 This diagram gives a more detailed view of the processes involved in managing and maintaining a data warehouse The processes run from left to right with a feedback loop from the users One of the very clear lessons of data warehousing is that you don t build one in the way you build a house Iteration and refinement is vital The clue is to start small and then evolve the data warehouse as the needs develop Flexibility and the ability to adapt to changing business needs are essential Some vendors are beginning to talk about tools for automating maintenance For this to happen the management of the metadata needs to become more tightly integrated into the data warehousing process However one of the fundamental assumptions of a data warehouse is that it is scaleable All of the advice I have seen suggests starting small with a pilot project and then letting it grow In fact I read where one consultant predicts that any data warehouse that takes longer than 4 months to get its first model working has a high likelihood of failure The proprietary multi dimensional databases work well for smaller data warehouses or departmental data marts up to 4 or 5 giga bytes Using the more open Relational OLAP database products data warehouses into the tera bytes in size have been built running on multi CPU platforms The actual design process for developing a data warehouse runs from right to left in this diagram You start with talking to the users then you determine their needs in terms that can be measured design a database to support those needs document the data descriptions and other attributes this will ultimately include data sources time stamps data meanings that change over time etc then you design the logic for translating data from various sources into an integrated data store write the code for extracting data from the various sources and transforming it into the data warehouse with updates to the metadata and finally package the procedures to handle scheduling management and maintenance N DW9 Some Terms 2 55 These terms relate to storage and processing technologies OLTP OnLine Transaction Processing OLTP is the traditional data processing area now dominated by relational databases which have matured into products optimized for transaction throughput OLAP OnLine Analytical Processing The case for OLAP is very well put in a white paper by E F Codd Associates OLAP requires the ability to consolidate view pivot and rotate and analyze data according to its multiple dimensions This requirement is called multi dimensional analysis or MDA MDD Multi Dimensional Database MDD An analysts view of the enterprises universe is typically multi dimensional in nature For instance they will want to analyze a given student population with regard to course department school year in course gender age etc These could be some of the dimensions for analyzing a retention rate model The effect the dimensions have on retention rate is observed interactively by an operation know as slice and dice Consolidation paths can be followed up or down using roll up or drill down There are a number of proprietary multi dimensional database formats one of which was developed by Cognos and is used in their PowerPlay product The multi dimensional attributes of this data model also known as a Hyper cube are designed into the storage technology of the database and the desktop tool that sits on top of the database All of the various levels of summarization and cross tabulation are pre computed and stored using sparse matrix technology ROLAP Relational OLAP ROLAP or Relational OLAP is the answer to MDD being proposed by vendors of traditional RDBMS They argue that the multi dimensionality of data is merely an attribute of the way the data is viewed and made available to user applications The actual storage technology used to store the views can be treated separately However multi dimensional analysis MDA based on OLTP databases suffers in performance for two reasons Current optimizing algorithms are inappropriate for the resulting complex joins often spanning large history tables Also aggregate queries are

    Original URL path: http://dheise.andrews.edu/dw/Avondale/DWPresSc.htm (2013-06-13)
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  • Data Warehousing - Bibliography
    Accessed December 9 1996 IBM Multi Dimensional Analysis Extending the Information Warehouse Framework http www software ibm com data dbtools mdaandiw ps Last updated May 16 1996 Accessed December 9 1996 IBM The IBM Information Warehouse Solution A Date Warehouse Plus http www software ibm com data dbtools iwsolu ps Last updated December 6 1995 Accessed December 9 1996 Information Advantage Multi Level Security http www infoadvan com 1b 5ts 4 html Last updated November 26 1996 Accessed December 9 1996 Informix Data Warehousing with Informix http www informix com informix solution warehous intro htm Last updated Accessed December 9 1996 Kimball R DBMS Magazine Online Book Excerpt The Data Warehouse Toolkit Practical Techniques for Building Dimensional Data Warehouses http www dbmsmag com dwtlkit html Last updated November 9 1996 Accessed December 9 1996 Labio W and Garcia Molina H Stanford University Efficient Snapshot Differential Algorithms for Data Warehousing http www db stanford edu pub papers window short ps Last updated June 26 1996 Accessed December 9 1996 Mattison R CIO Magazine State of the Art Warehousing Wherewithall http www cio com CIO 040196 soa html Last updated Accessed December 9 1996 McGuff F Frank McGuff Task Index http members aol com fmcguff spiral FULLNDX HTM Last updated October 29 1996 Accessed December 9 1996 McGuff F Frank McGuff Translating user requirements to product requirements http members aol com fmcguff userreqs userreqs htm Last updated October 18 1996 Accessed December 9 1996 MicroStrategy Inc Relational OLAP An Enterprise Wide Data Delivery Architecture http www strategy com wp a i1 htm Last updated November 19 1996 Accessed December 9 1996 MicroStrategy Inc The Case For Relational OLAP http www strategy com dwf wp b a1 htm Last updated November 19 1996 Accessed December 9 1996 Oracle Oracle OLAP Products Adding Value to the Data Warehouse An Oracle White Paper http www oracle com products olap collatrl olapwp pdf Last updated September 1 1995 Accessed December 9 1996 Paller A The Data Warehousing Institute TDWI Issues Online Top Six Trends for 1996 http www tekptnr com tpi tdwi issues top6trnd htm Last updated June 14 1996 Accessed December 9 1996 Perkins A Information Engineering Systems Corporation Developing a Data Warehouse The IES Approach http www ozemail com au ieinfo dw htm Last updated October 20 1996 Accessed December 9 1996 PLATINUM technology inc Data Warehousing PLATINUM technology Approach http www platinum com products gleason htm Last updated Accessed December 9 1996 Quass D Gupta A Mumick I and Widom J Stanford University Making Views Self Maintainable for Data Warehousing http www db stanford edu pub papers self maint ps Last updated September 27 1996 Accessed December 9 1996 Quinion M B Citing online sources http clever net quinion words citation htm Last updated February 20 1996 Accessed December 9 1996 Raden N Archer Decision Sciences Inc Data Data Everywhere http www hiredbrains com artic2 html Last updated 1999 Accessed March 11 2003 Raden N Archer Decision Sciences Inc Modeling the Data Warehouse

    Original URL path: http://dheise.andrews.edu/dw/Avondale/ACDWBibl.html (2013-06-13)
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  • Index to PowerPoint Slides
    Issues in Information Systems 5 Mar 28 Ch 5 IT Infrastructure and Emerging Technologies Ch 6 Foundations of Business Intelligence Database and Information Management Asg 1 due 6 Apr 4 Ch 7 Telecommunications the Internet and Wireless Technology Ch 8 Securing Information Systems 7 Apr 11 Ch 9 Achieving Operational Excellence and Customer Intimacy Enterprise Apps Ch 10 E Commerce Digital Markets Digital Goods FOF 8 Apr 18 Classes end

    Original URL path: http://dheise.andrews.edu/courses/2013INFS21600/Schedule.htm (2013-06-13)
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  • INFS21600 Management Information Systems
    How to Analyze a Case Study General Guidelines for writing business reports Assignment 1 Does IT Matter Assignment Specifications Nicholas G Carr Article Assignment 2 Reebok Adidas Assignment Specifications Case Description Assignment 3 Project Prioritizing Assignment Specifications Case Description Course

    Original URL path: http://dheise.andrews.edu/courses/2013INFS21600/asg/index.htm (2013-06-13)
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  • Group Listings include file
    Group B Hardy Candice Group B Jeremy David Group D Lever Tristan Group C Lewis Andrew Group A Malo Herod Group D Takkula Tuomas Group C Verhoeven Amy Group A Walker Bryan Group A Zuisang Thang Group D LastName FirstName Group Lewis Andrew Group A Verhoeven Amy Group A Walker Bryan Group A Anderson Sally Group B Hamilton Tracy Group B Hardy Candice Group B Chaplin Joshua Group C Cowan

    Original URL path: http://dheise.andrews.edu/courses/2013INFS21600/ClassList.shtm (2013-06-13)
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  • David Heise: Leadership, Data Warehousing, courses on MIS, IT Management
    my GuestBook or send me Subscribe to changes in Directory of Data Warehousing in Higher Education View My Stats Visitors since 23 Jun 1997 The views and opinions expressed in these pages are strictly my own Andrews University in no

    Original URL path: http://dheise.andrews.edu/Default.aspx (2013-06-13)
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  • Index to PowerPoint Slides
    Issues in Information Systems 5 Mar 29 Ch 5 IT Infrastructure and Emerging Technologies Ch 6 Foundations of Business Intelligence Database and Information Management Asg 1 due 6 Apr 5 Classes end at 5 00 PM Apr 12 Mid Semester Break April 6 15 7 Apr 19 No MIS Class FOF 8 Apr 26 Ch 7 Telecommunications the Internet and Wireless Technology Ch 8 Securing Information Systems 9 May 3

    Original URL path: http://dheise.andrews.edu/courses/2012INFS21600/Schedule.htm (2013-06-13)
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