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  1. 1. DATA WAREHOUSING AND DATA MINING M.Mageshwari,Lecturer M.S.P.V.L Polytechnic College
  2. 2. Course Overview <ul><li>The course: what and how </li></ul><ul><li>0. Introduction </li></ul><ul><li>I. Data Warehousing </li></ul><ul><li>II. Decision Support and OLAP </li></ul><ul><li>III. Data Mining </li></ul><ul><li>IV. Looking Ahead </li></ul><ul><li>Demos and Labs </li></ul>
  3. 3. 0. Introduction <ul><li>Data Warehousing, OLAP and data mining: what and why (now)? </li></ul><ul><li>Relation to OLTP </li></ul><ul><li>A case study </li></ul><ul><li>demos, labs </li></ul>
  4. 4. A producer wants to know…. Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What product prom- -otions have the biggest impact on revenue? What is the most effective distribution channel?
  5. 5. Data, Data everywhere yet ... <ul><li>I can’t find the data I need </li></ul><ul><ul><li>data is scattered over the network </li></ul></ul><ul><ul><li>many versions, subtle differences </li></ul></ul><ul><li>I can’t get the data I need </li></ul><ul><ul><li>need an expert to get the data </li></ul></ul><ul><li>I can’t understand the data I found </li></ul><ul><ul><li>available data poorly documented </li></ul></ul><ul><li>I can’t use the data I found </li></ul><ul><ul><li>results are unexpected </li></ul></ul><ul><ul><li>data needs to be transformed from one form to other </li></ul></ul>
  6. 6. What is a Data Warehouse? <ul><li>A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context. </li></ul>
  7. 7. What are the users saying... <ul><li>Data should be integrated across the enterprise </li></ul><ul><li>Summary data has a real value to the organization </li></ul><ul><li>Historical data holds the key to understanding data over time </li></ul><ul><li>What-if capabilities are required </li></ul>
  8. 8. What is Data Warehousing? <ul><li>A process of transforming data into information and making it available to users in a timely enough manner to make a difference </li></ul>Data Information
  9. 9. Evolution <ul><li>60’s: Batch reports </li></ul><ul><ul><li>hard to find and analyze information </li></ul></ul><ul><ul><li>inflexible and expensive, reprogram every new request </li></ul></ul><ul><li>70’s: Terminal-based DSS(Decision Support System and EIS (executive information systems) </li></ul><ul><ul><li>still inflexible, not integrated with desktop tools </li></ul></ul>
  10. 10. Data Warehouse Structure <ul><ul><li>base customer (1985-87) </li></ul></ul><ul><ul><ul><li>custid, from date, to date, name, phone, dob </li></ul></ul></ul><ul><ul><li>base customer (1988-90) </li></ul></ul><ul><ul><ul><li>custid, from date, to date, name, credit rating, employer </li></ul></ul></ul><ul><ul><li>customer activity (1986-89) -- monthly summary </li></ul></ul><ul><ul><li>customer activity detail (1987-89) </li></ul></ul><ul><ul><ul><li>custid, activity date, amount, clerk id, order no </li></ul></ul></ul><ul><ul><li>customer activity detail (1990-91) </li></ul></ul><ul><ul><ul><li>custid, activity date, amount, line item no, order no </li></ul></ul></ul>Time is part of key of each table
  11. 11. Definition of DSS <ul><li>Decision support system is defined as a system that helps the decision makers in various levels to take decisions </li></ul><ul><li>This system uses data, analytical models and user friendly software for taking decision </li></ul>
  12. 12. Definition of EIS <ul><li>Executive information system(EIS) is defined as a system that helps the high level executives to take policy decisions. </li></ul><ul><li>This system user higher level data, analytical models and user friendly software for taking decisions. </li></ul>
  13. 13. Evolution <ul><li>80’s: Desktop data access and analysis tools </li></ul><ul><ul><li>query tools, spreadsheets, GUIs </li></ul></ul><ul><ul><li>easier to use, but only access operational databases </li></ul></ul><ul><li>90’s: Data warehousing with integrated OLAP(online analytical processing)engines and tools </li></ul>
  14. 14. Data Warehousing -- It is a process <ul><li>Technique for assembling and managing data from various sources for the purpose of answering business questions. Thus making decisions that were not previous possible </li></ul><ul><li>A decision support database maintained separately from the organization’s operational database </li></ul>
  15. 15. Characteristics of Data Warehouse <ul><li>A data warehouse is a </li></ul><ul><ul><li>subject-oriented </li></ul></ul><ul><ul><li>integrated </li></ul></ul><ul><ul><li>time-varying </li></ul></ul><ul><ul><li>non-volatile </li></ul></ul><ul><li>collection of data that is used primarily in organizational decision making. </li></ul>
  16. 16. subject-oriented <ul><li>A data warehouse is organized around the major subjects of the organization such as customer, supplier, product, sales, etc.., </li></ul><ul><li>Data warehouse provides a simple and concise view around a particular subject by excluding data that are not useful to the decision support process. </li></ul>
  17. 17. Integrated: <ul><li>A data warehouse is constructed by integrating multiple sources of data such as relational database, flat files and on-line transaction records. </li></ul><ul><li>Data cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attributes etc.., </li></ul>
  18. 18. Time Variant <ul><li>Data warehouse maintains records of both historical and current data. </li></ul><ul><li>So it can provide information in a historical perspective </li></ul>
  19. 19. Non Volatile <ul><li>Once data warehouse is loaded with data, it is not possible to perform any modifications in the stored data. </li></ul>
  20. 20. Explorers, Farmers and Tourists Explorers: Seek out the unknown and previously unsuspected rewards hiding in the detailed data Farmers: Harvest information from known access paths Tourists: Browse information about Tourists
  21. 21. Application-Orientation vs. Subject-Orientation Application-Orientation Operational Database Loans Credit Card Trust Savings Subject-Orientation Data Warehouse Customer Vendor Product Activity
  22. 22. Functioning of Data warehousing Data Source cleaning Transformation Data Warehouse New Update
  23. 23. Collection data <ul><li>Data warehousing collect data from various data sources such as relational data base, flat files and on-line records </li></ul><ul><li>The collection of data are stored in database inside the warehouse. </li></ul><ul><li>The type of data collection used depends on the architecture of the ware house. </li></ul>
  24. 24. Integration <ul><li>Each and every data source uses from different schema. </li></ul><ul><li>Data warehouse get data from different source with different schema and convert the data from various sources into a common integrated schema. </li></ul>
  25. 25. Star Schema <ul><li>A single fact table and for each dimension one dimension table </li></ul><ul><li>Does not capture hierarchies directly </li></ul>T i m e p r o d c u s t c i t y f a c t date, custno, prodno, cityname, ...
  26. 26. Snowflake schema <ul><li>Represent dimensional hierarchy directly by normalizing tables. </li></ul><ul><li>Easy to maintain and saves storage </li></ul>T i m e p r o d c u s t c i t y f a c t date, custno, prodno, cityname, ... r e g i o n
  27. 27. Data transformation and cleaning <ul><li>The task of correcting and preparing the data is called data cleaning. </li></ul><ul><li>Data source delivers data into the database of data warehouse it should be corrected. </li></ul>
  28. 28. Update of data <ul><li>Update on tables at the data sources must be sent to the data warehouse. </li></ul><ul><li>If the tables in data warehouse are same as sources, the updation is easy. </li></ul>
  29. 29. Summarizing data <ul><li>The raw data generated by a transaction may be too large to store online. </li></ul><ul><li>Therefore, we can use summary of transactions for easy querying. </li></ul>
  30. 30. Data Warehouse for Decision Support & OLAP <ul><li>Putting Information technology to help the knowledge worker make faster and better decisions </li></ul><ul><ul><li>Which of my customers are most likely to go to the competition? </li></ul></ul><ul><ul><li>What product promotions have the biggest impact on revenue? </li></ul></ul><ul><ul><li>How did the share price of software companies correlate with profits over last 10 years? </li></ul></ul>
  31. 31. Decision Support <ul><li>Used to manage and control business </li></ul><ul><li>Data is historical or point-in-time </li></ul><ul><li>Optimized for inquiry rather than update </li></ul><ul><li>Use of the system is loosely defined and can be ad-hoc </li></ul><ul><li>Used by managers and end-users to understand the business and make judgments </li></ul>
  32. 32. OLAP(Online analytical processing) <ul><li>A data warehouse stores data , but OLAP transform the data warehouse data into specific meaningful information. </li></ul><ul><li>Therefore OLAP provides a user friendly environment for interactive data analysis. </li></ul>
  34. 34. OLAP OPERATION on the multidimensional data <ul><li>Roll-up(GROUP) </li></ul><ul><li>Drill down(Less) </li></ul><ul><li>Slice and Dice(Pice) </li></ul><ul><li>Pivot(rotate) </li></ul>
  35. 35. TYPES OF OLAP <ul><li>MOLAP(MULTIDIMENSIONAL OLAP) </li></ul><ul><li>ROLAP(RELATIONAL ROLAP) </li></ul>
  36. 36. Multi-dimensional Data <ul><li>“ Hey…I sold $100M worth of goods” </li></ul>Dimensions: Product, Region, Time Hierarchical summarization paths Product Region Time Industry Country Year Category Region Quarter Product City Month Week Office Day Month 1 2 3 4 7 6 5 Product Toothpaste Juice Cola Milk Cream Soap Region W S N
  37. 37. Data Warehouse Architecture Data Warehouse Engine Optimized Loader Extraction Cleansing Analyze Query Metadata Repository Relational Databases Legacy Data Purchased Data ERP Systems
  38. 38. Architecture of data warehousing External data Data Acquisition Data Manager Warehouse data External data Data Dictionary Information Directiory Warehouse data Middleware Design Management Data Access
  39. 39. Architecture of
  40. 40. Design Component <ul><li>The data warehouse designer design the database of the data warehouse and the warehouse administrator manages the data warehouse. </li></ul><ul><li>The designer and administrator use the design component to design and store data </li></ul>
  41. 41. Types of design <ul><li>Bottom-up design </li></ul><ul><li>Business value can be returned as quickly as the first data marts can be created </li></ul><ul><li>Top-down design </li></ul><ul><li>Atomic data, that is, data at the lowest level of detail, are stored in the data warehouse. </li></ul><ul><li>Hybrid design </li></ul>
  42. 42. <ul><li>Hybrid design </li></ul><ul><li>. Hybrid methodologies have evolved to take advantage of the fast turn-around time of bottom-up design and the enterprise-wide data consistency of top-down design. </li></ul>
  43. 43. Data Manager Component <ul><li>The database in the data warehouse uses the data manager component for managing and accessing the data stored in the data warehouse. </li></ul><ul><li>Rdbms </li></ul><ul><li>Mdbms </li></ul>
  44. 44. Management Component <ul><li>Administering data acquisition operation </li></ul><ul><li>Managing backup copies of the data </li></ul><ul><li>Recovering the lost data </li></ul><ul><li>Providing security to the data stored in the data warehouse. </li></ul><ul><li>Authorizing access to the data stored in the data warehouse. </li></ul>
  45. 45. Data Acquisition Component <ul><li>This component acquires data from various sources by using the data acquisition applications </li></ul><ul><li>The data acquisition applications are based on rules that are defined by the data warehouse developers. </li></ul>
  46. 46. The operation performed during data clean up <ul><li>Restructuring the records and fields of the database tables. </li></ul><ul><li>Removing the irrelevant and redundant data </li></ul><ul><li>obtaining and adding missing data. </li></ul><ul><li>Verifying integrity and consistency of the data </li></ul>
  47. 47. The operation performed on the data for enhancement are <ul><li>Decoding and translating the values in fields. </li></ul><ul><li>Summarizing data </li></ul><ul><li>Calculating the derived values. </li></ul>
  48. 48. Information directory Component <ul><li>This component helps the end users to know the details of the data stored in the data warehouse. </li></ul><ul><li>This is done with the help of the data about the data named meta data. </li></ul><ul><li>Technical data </li></ul><ul><li>Business data </li></ul>
  49. 49. Middleware Component <ul><li>This components connect to the local databases. </li></ul><ul><li>Analytical server used to analyze multidimensional data. </li></ul><ul><li>Intelligent data warehousing middleware to control the access to the warehouse database. </li></ul>
  50. 50. Data mart <ul><li>Data mart is a database that contains data needed for a small group of users for their own department needs. </li></ul><ul><ul><ul><ul><ul><li>Dependent data mart </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Independent data mart </li></ul></ul></ul></ul></ul>
  51. 51. Different between data warehouse and data mart Data warehouse Data Mart Data mart is therefore useful for small organizations with very few departments data warehousing is suitable to support an entire corporate environment. If you listen to some vendors, you may be left thinking that building data warehouses is a waste of time. data mart vendor that tells you this are looking out for their own best interests. This supports the entire information requirement of an organization. This support the information requirement of a department in an organization This has large model, wider implementation, large data and more number of users. This has small data model, shorter implementation, less data and some users.
  52. 52. Advantages of data mart <ul><li>Since each department has its own data mart, the departments can summarize, sort , select structure etc their own department’s data. This will not confused with any other department. </li></ul><ul><li>The department can do whatever DSS processing they want. </li></ul><ul><li>The processing cost and storage are less that the data warehouse. </li></ul><ul><li>The department can select a software for their data mart. it is powerful to fit their needs. </li></ul>
  53. 53. Data warehousing life cycle Design Enhance prototype Operate deploy
  54. 54. Data Modeling(Multi-dimensional Database) <ul><li>“ Hey…I sold $100M worth of goods” </li></ul>Dimensions: Product, Region, periods Hierarchical summarization paths Product Region Period Industry Country Year Category Region Quarter Product City Month Week Office Day Month 1 2 3 4 7 6 5 Product Toothpaste Juice Cola Milk Cream Soap Region W S N
  55. 55. Building of data warehouse <ul><li>The builder must forecast the usage of the warehouse by the users. </li></ul><ul><li>The design should support accessing data with any meaningful values of the attributes. </li></ul><ul><li>To build a good data warehouse data acquisition process must follow the steps given flow </li></ul><ul><ul><ul><ul><ul><li>extract the data from multiple heterogeneous sources </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Format the data for consistency within the warehouse. </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>The data must be cleaned to ensure validity </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>The data must be converted from relational ,object oriented ,hierarchy model to a multidimensional model. </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>The data are loaded into the warehouse. Good monitoring tools are necessary to recover from incorrect load. </li></ul></ul></ul></ul></ul>
  56. 56. Data warehouse and views <ul><li>Data warehouse is a permanent storage of data in multidimensional tables. </li></ul><ul><li>View are temporarily created when needed using data warehouse. </li></ul><ul><li>This is used for decision support system. </li></ul>
  57. 57. Different between data warehouse and views Data warehouse Views Data warehouse is a permanent storage data. Views are created from warehouse data when needed and it is not permanent Data warehouse are multidimensional Views are relational Data warehouse can be indexed to maximize performance. Views cannot be indexed. Data warehouse provides specific support to a functionality Views cannot give specific support to a functionality. Data warehouse provide large amount of data. Views are created by extracting minimum data from data warehouse.
  58. 58. Data warehouse Future <ul><li>New techniques must be introduced in data cleaning ,indexing and partitioning. </li></ul><ul><li>The manual operation involved in data acquisition ,management data quality and performance maximization must be automated. </li></ul><ul><li>Proper business rules must be developed and incorporated in warehouse creation and maintenance process. </li></ul>
  59. 59. Data Mining <ul><li>Data mining is sorting through data to identify patterns and establish relationships. </li></ul>
  60. 60. Data Mining (cont.)
  61. 61. Data Mining works with Warehouse Data <ul><li>Data Warehousing provides the Enterprise with a memory </li></ul><ul><li>Data Mining provides the Enterprise with intelligence </li></ul>
  62. 62. <ul><li>“ The key in business is to know something that nobody else knows .” </li></ul><ul><li>— Aristotle Onassis </li></ul><ul><li>“ To understand is to perceive patterns.” </li></ul><ul><li>— Sir Isaiah Berlin </li></ul>Data Mining Motivation PHOTO: LUCINDA DOUGLAS-MENZIES PHOTO: HULTON-DEUTSCH COLL
  63. 63. Application Areas Industry Application Finance Credit Card Analysis Insurance Claims, Fraud Analysis Telecommunication Call record analysis Consumer goods promotion analysis Data Service providers Value added data Utilities Power usage analysis
  64. 64. Data Mining in Use <ul><li>The US Government uses Data Mining to track fraud </li></ul><ul><li>A Supermarket becomes an information broker </li></ul><ul><li>Basketball teams use it to track game strategy </li></ul><ul><li>Cross Selling </li></ul><ul><li>Warranty claims Routing </li></ul><ul><li>Holding on to Good Customers </li></ul><ul><li>Weeding out Bad Customers </li></ul>
  65. 65. What is data mining technology <ul><ul><ul><ul><li>The process of extracting or finding hidden knowledge from large database is called data mining. </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Ex: Age 21------  we can understand he is major </li></ul></ul></ul></ul>data information
  66. 66. <ul><li>Data Mining Technology </li></ul>Cleaning and Integration Databases Data Warehouse Flat Files Patterns Knowledge Selection and transformation Data Mining
  67. 67. The various step <ul><li>Data cleaning  To remove noise and inconsistent data </li></ul><ul><li>Data integration  Data from multiple sources are combined </li></ul><ul><li>Data selection  relevant data are retrieved from the database for analysis </li></ul>
  68. 68. <ul><li>Data transformation  The selected data are made for mining by performing aggregation operations </li></ul><ul><li>Data mining  Intelligent methods are applied to extract data patterns </li></ul><ul><li>Pattern evaluation  Identify the needed patterns </li></ul><ul><li>Knowledge presentation  present the mined knowledge to the user </li></ul>
  69. 69. Loading the Warehouse Cleaning the data before it is loaded
  70. 70. Data Integration Across Sources Trust Credit card Savings Loans Same data different name Different data Same name Data found here nowhere else Different keys same data
  71. 71. Data Transformation Example encoding unit field appl A - balance appl B - bal appl C - currbal appl D - balcurr appl A - pipeline - cm appl B - pipeline - in appl C - pipeline - feet appl D - pipeline - yds appl A - m,f appl B - 1,0 appl C - x,y appl D - male, female Data Warehouse
  72. 72. Structuring/Modeling Issues
  73. 73. Data Warehouse vs. Data Marts
  74. 74. From the Data Warehouse to Data Marts Departmentally Structured Individually Structured Data Warehouse Organizationally Structured Less More History Normalized Detailed Data Information
  75. 75. Data Warehouse and Data Marts OLAP Data Mart Lightly summarized Departmentally structured Organizationally structured Atomic Detailed Data Warehouse Data
  76. 76. Characteristics of the Departmental Data Mart <ul><li>OLAP </li></ul><ul><li>Small </li></ul><ul><li>Flexible </li></ul><ul><li>Customized by Department </li></ul><ul><li>Source is departmentally structured data warehouse </li></ul>
  77. 77. Techniques for Creating Departmental Data Mart <ul><li>OLAP </li></ul><ul><li>Subset </li></ul><ul><li>Summarized </li></ul><ul><li>Superset </li></ul><ul><li>Indexed </li></ul><ul><li>Arrayed </li></ul>Sales Mktg. Finance
  78. 78. Data Mart Centric Data Marts Data Sources Data Warehouse
  79. 79. True Warehouse Data Marts Data Sources Data Warehouse
  80. 80. II. On-Line Analytical Processing (OLAP) Making Decision Support Possible
  81. 81. What Is OLAP? <ul><li>Online Analytical Processing - coined by EF Codd in 1994 paper contracted by Arbor Software </li></ul><ul><li>Generally synonymous with earlier terms such as Decisions Support, Business Intelligence, Executive Information System </li></ul><ul><li>OLAP = Multidimensional Database </li></ul><ul><li>MOLAP: Multidimensional OLAP (Arbor Essbase, Oracle Express) </li></ul><ul><li>ROLAP: Relational OLAP (Informix MetaCube, Microstrategy DSS Agent) </li></ul>
  82. 82. The OLAP Market <ul><li>Rapid growth in the enterprise market </li></ul><ul><ul><li>1995: $700 Million </li></ul></ul><ul><ul><li>1997: $2.1 Billion </li></ul></ul><ul><li>Significant consolidation activity among major DBMS vendors </li></ul><ul><ul><li>10/94: Sybase acquires ExpressWay </li></ul></ul><ul><ul><li>7/95: Oracle acquires Express </li></ul></ul><ul><ul><li>11/95: Informix acquires Metacube </li></ul></ul><ul><ul><li>1/97: Arbor partners up with IBM </li></ul></ul><ul><ul><li>10/96: Microsoft acquires Panorama </li></ul></ul><ul><li>Result: OLAP shifted from small vertical niche to mainstream DBMS category </li></ul>
  83. 83. Strengths of OLAP <ul><li>It is a powerful visualization paradigm </li></ul><ul><li>It provides fast, interactive response times </li></ul><ul><li>It is good for analyzing time series </li></ul><ul><li>It can be useful to find some clusters and outliers </li></ul><ul><li>Many vendors offer OLAP tools </li></ul>
  84. 84. OLAP Is FASMI <ul><li>Fast </li></ul><ul><li>Analysis </li></ul><ul><li>Shared </li></ul><ul><li>Multidimensional </li></ul><ul><li>Information </li></ul>
  85. 85. Data Cube Lattice <ul><li>Cube lattice </li></ul><ul><ul><li>ABC AB AC BC A B C none </li></ul></ul><ul><li>Can materialize some groupbys, compute others on demand </li></ul><ul><li>Question: which groupbys to materialze? </li></ul><ul><li>Question: what indices to create </li></ul><ul><li>Question: how to organize data (chunks, etc) </li></ul>
  86. 86. Visualizing Neighbors is simpler
  87. 87. A Visual Operation: Pivot (Rotate) 10 47 30 12 Juice Cola Milk Cream NY LA SF 3/1 3/2 3/3 3/4 Date Month Region Product
  88. 88. “ Slicing and Dicing” Product Sales Channel Regions Retail Direct Special Household Telecomm Video Audio India Far East Europe The Telecomm Slice
  89. 89. Roll-up and Drill Down <ul><li>Sales Channel </li></ul><ul><li>Region </li></ul><ul><li>Country </li></ul><ul><li>State </li></ul><ul><li>Location Address </li></ul><ul><li>Sales Representative </li></ul>Roll Up Higher Level of Aggregation Low-level Details Drill-Down
  90. 90. Nature of OLAP Analysis <ul><li>Aggregation -- (total sales, percent-to-total) </li></ul><ul><li>Comparison -- Budget vs. Expenses </li></ul><ul><li>Ranking -- Top 10, quartile analysis </li></ul><ul><li>Access to detailed and aggregate data </li></ul><ul><li>Complex criteria specification </li></ul><ul><li>Visualization </li></ul>
  91. 91. Organizationally Structured Data <ul><li>Different Departments look at the same detailed data in different ways. Without the detailed, organizationally structured data as a foundation, there is no reconcilability of data </li></ul>marketing manufacturing sales finance
  92. 92. Multidimensional Spreadsheets <ul><li>Analysts need spreadsheets that support </li></ul><ul><ul><li>pivot tables (cross-tabs) </li></ul></ul><ul><ul><li>drill-down and roll-up </li></ul></ul><ul><ul><li>slice and dice </li></ul></ul><ul><ul><li>sort </li></ul></ul><ul><ul><li>selections </li></ul></ul><ul><ul><li>derived attributes </li></ul></ul><ul><li>Popular in retail domain </li></ul>
  93. 93. OLAP Operations © Prentice Hall Single Cell Multiple Cells Slice Dice Roll Up Drill Down
  94. 94. Relational OLAP: 3 Tier DSS Store atomic data in industry standard RDBMS. Generate SQL execution plans in the ROLAP engine to obtain OLAP functionality. Obtain multi-dimensional reports from the DSS Client. Data Warehouse ROLAP Engine Decision Support Client Database Layer Application Logic Layer Presentation Layer
  95. 95. MD-OLAP: 2 Tier DSS MDDB Engine MDDB Engine Decision Support Client Database Layer Application Logic Layer Presentation Layer Store atomic data in a proprietary data structure (MDDB), pre-calculate as many outcomes as possible, obtain OLAP functionality via proprietary algorithms running against this data. Obtain multi-dimensional reports from the DSS Client.
  96. 96. MSPVL Polytechnic College Pavoorchatram