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Intelligent Data Warehousing from data preparation to data mining


To introduce the concepts, methods, and techniques of data warehousing and to ... DW/MIS: Kimball et al. 1998; Kimball and Merz, 2000 ... – PowerPoint PPT presentation

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Title: Intelligent Data Warehousing from data preparation to data mining

Intelligent Data Warehousing from data
preparation to data mining
  • Zhengxin Chen
  • CRC Express

  • To introduce the concepts, methods, and
    techniques of data warehousing and to give
    guidelines of planning, building, working,  and
    managing data warehouses.
  • Outline
  • Introduction
  • Enterprise intelligence and artificial
  • From DBMS to data warehousing
  • Data preparation and preprocessing
  • Building data warehouses
  • Basics of materialized views
  • Advanced in materialized views
  • Intelligent data analysis
  • Toward integrated OLAP and data mining

Chapter 1 Introduction
  • 1.1 Why this book is needed
  • 1.2 Features of the book
  • 1.3 Why intelligent data warehousing
  • 1.4 Organization of the book
  • 1.5 How to use this book

1.1 Why this book is needed
  • Decision support systems (DSSs) are rapidly
    becoming a key to gaining competitive advantage
    for business.
  • Many corporations have built or are building
    unified decision-support databases, referred to
    as data warehouses, on which intelligent data
    analysis can be carried out.
  • This book integrates theoretical and practical
    studies related to data warehousing from both
    computer science and MIS perspectives.
  • It tries to bridge the gap between theoretical
    research and business applications.

Data warehouse definition
  • By W.H. InmonA data warehouse is a
    subject-oriented, integrated, time-variant,
    nonvolatile collection of data in support of
    management decisions

Interesting issues in DW
  • Business-driven vs. database and AI technologies
  • DW stages how to prepare data, how to store data
    in DW, how to analyze data stored in DW using
    data mining techniques, and how to manage DWs.
  • Research topics integration of data (with
    knowledge), integration of various methods
    applied on the data for analysis, warehouse
    design optimization (e.g. parts and whole), and
    metadata issues.

1.2 Features of the book
  • State-of-the-art DW research and practice from an
    integrated business and computer science
  • Intelligent data analysis techniques (i.e., data
    mining) in the context of DW.
  • Existing literature and original research results
    with examples and case studies
  • Intelligent techniques are applied to the entire
    process of DW
  • A road map for studying intelligent data
  • Text for general information and references for
    more technical details

1.3 Why intelligent data warehousing
  • DW integrates data from multiple sources and
    provides different ways of looking at the data
    than do the databases being integrated Gupta
    and Mumick, 1998
  • DW extends traditional interests of DBMS. The
    success or failure of DW is closely related to
    the historical development of DBMSs. DW has
    raised many challenging issues for the database
  • There are various aspects of intelligence related
    to data warehousing, such as

AI/BI techniques
  • Issues on the manipulation of huge amounts of
    data, the meaning of such data, the implication
    of these data, and the methodology used to
    analyze them.
  • Building DW is an intelligent process, such as
    deciding what kind of data to be included
    (heuristic search, learning, and computational
    methods), Web-enabling techniques, and modeling
    user behavior for warehouse design.
  • OLAP for decision support queries.
  • Integration of OLAP and data mining.
  • Science and engineering data warehousing

1.4 Organization of the book
  • Part I overview CH1 emphasizes on integrated
    data and an integrated methodology rooted in AI.
    CH2 discusses AI/BI from a business-oriented
    perspective. CH3 covers DW and DBMS, OLAP, and
    materialized views.
  • Part II core materials on DW CH4 is on data
    preparation and preprocessing with a look at the
    overall DW lifecycle. CH5 examines DW building
    while CH67 are devoted to materialized views.
  • Part III data analysis and knowledge discovery
    CH8 is on data mining (rough set, clustering,
    classification and association) and CH9 covers
    influential association rule mining which
    combines association rule mining and OLAP.

1.5 How to use this book
  • This book assumes that readers have basic
    knowledge about DBMS.
  • Two other companion book on decision support and
    data mining.
  • References
  • DBMS Silberschatz et al., 2001 Elmasri and
    Navathe, 2000
  • DW/MIS Kimball et al. 1998 Kimball and Merz,
  • DW/CS
  • Data mining Han and Kamber 2000 Fayyad et al.,
    1996 Cios et al., 1998 Witten and Frank, 1999.
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