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Chapter I Introduction to Data Warehousing. Presented by: Hongying lian Date: 11/07/2000 Course: CSSE 541. Overview. Describe the four levels of analytical processing in modern organizations that will drive the evolution of the data warehousing process

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chapter i introduction to data warehousing

Chapter I Introduction to Data Warehousing

Presented by: Hongying lian

Date: 11/07/2000

Course: CSSE 541

overview
Overview
  • Describe the four levels of analytical processing in modern organizations that will drive the evolution of the data warehousing process
  • Describe the overall architecture for the information data superstore (IDSS), which can also be called as super data warehouse
  • Define basic terminologies for the data warehouse technology
objectives of today s businesses
Objectives of Today’s Businesses
  • Access and combine data from a variety of data stores
  • Perform complex data analysis across these date stores
  • Create multidimensional views of data and its metadata
  • Easily summarize and roll up the information across subject areas and business dimensions
these objectives cannot be met easily
These objectives cannot be met easily
  • Data is scattered in many types of incompatible structures.
  • Lack of documentation has prevented from integration older legacy systems with newer systems
  • Internet software like searching engine needs to be improved
  • Accurate and accessible metadata across multiple organizations is hard to get
four levels of analytical processing
Four Levels of Analytical Processing
  • In modern organization, at least four levels of analytical processing should be supported by information systems
    • First level: Consists of simple queries and reports against current and historical data
    • Second level: Goes deeper and requires the ability to do “what if” processing across data store dimensions
four levels of analytical processing con t
Four Levels of Analytical Processing (Con’t)
  • Third level: Needs to step back and analyze what has previously occurred to bring about the current stat of the data
  • Fourth level: Analyzes what has happened in the past and what needs to be done in the future in order to bring some specific change
information data superstore idss
Information Data Superstore (IDSS)
  • Also named Super Data Warehouse
  • Introduced in a paper by Bischoff and Yevich
  • Define the architecture needed to support the four levels of analytical processing
information data superstore idss con t
Information Data Superstore (IDSS) (Con’t)

User’s perspective of the IDSS

information data superstore idss con t9
Information Data Superstore (IDSS) (Con’t)
  • Unfortunately, IDSS can’t be fully implemented by today’s technology
    • Lack of effective product, which can join data on fields with a common meaning
    • Lack of product with a dynamic, active and unstructured data directory that will support cross-organizational data access
    • Lack of administration tools that will ease the burden of both the data administration and database administration staffs in metadata maintenance
data warehouse technology
Data Warehouse Technology
  • A strategy to build the basic constructs of the IDSS with today’s technologies
  • Definition given by W.H.Inmon
    • The data warehouse is a collection of integrated, subject-oriented databases designed to support the DSS (decision support) function, where each unit of data is relevant to some moment in time
data warehouse technology con t
Data Warehouse Technology (Con’t)
  • The data should be well-defined, consistent, and nonvolatile in nature.
  • The quantity of data should be large enough to support data analysis, querying, reporting, and comparisons of historical data over a longer period of time.
  • The data warehouse must be user driven.