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The chapters go into tremendous details, discussing tool requirements and offering a look at tool-by-tool vendor-based solutions. Parallel processing architectures, selecting database schemas for decision support, the process of extracting, cleaning, and transforming data, and describes meta data as a key component of This section of the book details mapping the warehouse to the The second section, Data Warehousing, begins by detailing data warehousing components and the processes of building a data warehouse. This section builds a tremendous foundation of warehousing technology by detailing hardware architectures, multiprocessing architectures, and RDBMS features and The section continuesīy discussing distributed database management systems, and by individually offering an overview of major client/server RDBMS database environments such as Oracle, Informix, Sybase, IBM’s DB2,Īnd Microsoft MS-SQL Server.
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The Foundation section begins by introducing the data warehouse, presenting an overview of client/server architectures and presenting parallel processors and cluster systems. Starting general and moving to the specific, detailing at least five long chapters within each section. Each part goes into a tremendous amount of detail The book is broken into five parts, Foundation, Data Warehousing, Business Analysis, Data Mining, and Data Visualization and Overall Perspective. Single source, 2) supporting online analytical processing (OLAP), and 3) the newest data delivery engine, Data Mining. TheĪuthors use the forward to specify the three areas of data warehousing to be covered in the book as 1) bringing data necessary for enhancing traditional information presentation technologies into a Smith (Computing McGraw-Hill 1997), focuses on data delivery as a top priority in business computing today. Publisher: McGRAW-HILL (ISBN 0-07-006272-2)ĭata Warehousing, Data Mining, & OLAP, written by Alex Berson and Stephen J. Tuning and testing of Data Warehouse of Data Warehouse.Authors: Alex Berson and Stephen J. This unit includes following topics: Computation of Data Cubes, modeling, Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP,
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This unit includes following topics: On-line analytical processing models and operations (drill down, drill up, slice, dice, pivot), Star schema, Snowflake schema and Fact constellation schema This unit includes following topics: Differences between operational database systems and data warehouses,ĭata marts, Metadata, Multidimensional data model,įrom Tables and Spread Sheets to Data Cubes, Issues in Data Warehouse and Data Mining,Īpplication of Data Warehouse and Data Mining Stages of Knowledge discovery in database(KDD), This unit includes following topics: Types of databases (Relational database, Data Warehouses, Transactional Database),įunctionalities of data mining – What kinds of Pattern can be mined?,Īssociation Analysis, Cluster Analysis, Outlier Analysis, Evolution Analysis, This course introduces advanced aspects of data warehousing and data mining, encompassing the principles, research results and commercial application of the current technologies. Computation of Data Cubes, OLAP Dataand OLAP QueriesĬluster analysis, Classification and Predication.