ch29-Overview of Data Warehousing and OLAP

ch29-Overview of Data Warehousing and OLAP - Copyright 2007...

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Slide 29- 1 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe
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Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Chapter 29 Overview of Data Warehousing and OLAP
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Slide 29- 3 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Chapter 29 Outline Purpose of Data Warehousing Introduction, Definitions, and Terminology Comparison with Traditional Databases Characteristics of Data Warehouses Classification of Data Warehouses Multi-dimensional Schemas Building a Data Warehouse Functionality of a Data Warehouse Warehouse vs. Data Views Implementation difficulties and open issues
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Slide 29- 4 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Purpose of Data Warehousing Traditional databases are not optimized for data access only they have to balance the requirement of data access with the need to ensure integrity of data. Most of the times the data warehouse users need only read access but, need the access to be fast over a large volume of data. Most of the data required for data warehouse analysis comes from multiple databases and these analysis are recurrent and predictable to be able to design specific software to meet the requirements. There is a great need for tools that provide decision makers with information to make decisions quickly and reliably based on historical data. The above functionality is achieved by Data Warehousing and Online analytical processing (OLAP)
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Slide 29- 5 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Introduction, Definitions, and Terminology W. H Inmon characterized a data warehouse as: “A subject-oriented, integrated, nonvolatile, time-variant collection of data in support of management’s decisions.”
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Slide 29- 6 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Introduction, Definitions, and Terminology Data warehouses have the distinguishing characteristic that they are mainly intended for decision support applications. Traditional databases are transactional. Applications that data warehouse supports are: OLAP (Online Analytical Processing) is a term used to describe the analysis of complex data from the data warehouse. DSS (Decision Support Systems) also known as EIS (Executive Information Systems) supports organization’s leading decision makers for making complex and important decisions. Data Mining is used for knowledge discovery, the process of searching data for unanticipated new knowledge.
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Slide 29- 7 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Conceptual Structure of Data Warehouse Data Warehouse processing involves Cleaning and reformatting of data OLAP Data Mining Databases Data Warehouse Cleaning Reformatting Updates/New Data Back Flushing Other Data Inputs OLAP Data Mining Data Metadata DSSI EIS
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Slide 29- 8 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Comparison with Traditional Databases Data Warehouses are mainly optimized for appropriate
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This note was uploaded on 02/23/2012 for the course CS 348 taught by Professor Staff during the Fall '08 term at Purdue University-West Lafayette.

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ch29-Overview of Data Warehousing and OLAP - Copyright 2007...

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