Encyclopedia of Data Warehousing and Mining (2 Volumes)

Encyclopedia of Data Warehousing and Mining (2 Volumes)

John Wang (Montclair State University, USA)
Release Date: June, 2005|Copyright: © 2005 |Pages: 1382
DOI: 10.4018/978-1-59140-557-3
ISBN13: 9781591405573|ISBN10: 1591405572|EISBN13: 9781591405597
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Description & Coverage
Description:

Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead.

The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • A-Z overview of DWM
  • Data pre-processing and post-processing
  • Data privacy and security
  • DWM applications in the real world
  • DWM system architectures & functionalities
  • From Bayesian networks to Dempster-Shafer theory
  • From fractal DM to survival DM
  • From microarray DM to multimedia DM
  • From semantic-based DM to symbiotic DM
  • From vertical DM to evolutionary DM
  • Future and emerging trends in DWM
  • Intrusion and damage detection
Reviews & Statements

"I recommend this reference work to all academic libraries with a computing or information systems courses. The work will also be relevant to academics and practitioners alike."

– Philip Calvert, Victoria University of Wellington, New Zealand

It is highly recommended for libraries with strong computer and information science collections.

– American Reference Books Annual (ARBA) 2006

As the first encyclopedia devoted to data warehousing and mining, it provides a thorough introduction and a systematic description of this rapidly expanding field of data warehousing and mining. The book will be a valuable and lasting source of reference for researchers and developers interested in working in areas such as e-commerce, business intelligence, knowledge services.

– Prof. Ning Zhong, Maebashi Institute of Technology, Japan

The Encyclopedia of Data Warehousing and Mining is an excellent reference for students, researchers, and practitioners alike. Its coverage of a broad range of topics from both theoretical and application-oriented perspectives makes it the place to start when learning any topic within data mining and warehousing.

– Nikunj Oza, NASA Ames Research Center, USA

The Encyclopedia of Data Warehousing and Mining is an accessible yet comprehensive resource for students, researchers and practitioners. The range of topics covered by the encyclopedia is very broad indeed, and it should prove extremely useful as a desktop reference for those active in data mining and warehousing, as well as in related fields.

– Dr. Sach Mukherjee, University of Oxford, UK

I think the Encyclopedia of Data Warehousing and Mining is the first publication giving a comprehensive, analytical and high quality description of what the scientific community has been producing in the important and emergent field of data warehousing and data mining.

– Prof. Francesco Buccafurri, Universita’ di Reggio Calabria, Italy

"This is an interesting encyclopedia about data mining, information technology, and related issues. Many students, professionals, professors, and researchers could use it to learn the key concepts about data management. It is a valuable reference for graduate students or professionals dealing with huge amounts of data in big companies. I found it rewarding to expand my knowldege about data mining by browsing through this encyclopedia."

– Javier Faulin, University of Navarra, Spain
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Editor/Author Biographies
John Wang is a professor in the Department of Information Management and Business Analytics at Montclair State University, USA. Having received a scholarship award, he came to the USA and completed his PhD in operations research from Temple University. Due to his extraordinary contributions beyond a tenured full professor, Dr. Wang has been honored with two special range adjustments in 2006 and 2009, respectively. He has published over 100 refereed papers and seventeen books. He has also developed several computer software programs based on his research findings.

He serves as Editor-in-Chief for ten Scopus-indexed journals, such as Int. J. of Business Analytics, Int. J. of Information Systems and Supply Chain Management, Int. J. of Information Systems in the Service Sector, Int. J. of Applied Management, Int. J. of Information and Decision Sciences, Int. J. of Data Mining, Modelling and Management, etc. He is the Editor of Encyclopedia of Business Analytics and Optimization (five volumes), Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (six-volume) and the Editor of the Encyclopedia of Data Warehousing and Mining, 1st (two-volume) and 2nd (four-volume). His long-term research goal is on the cooperation of operations research, data mining and cybernetics.

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Editorial Advisory Board
Floriana Esposito

Universita’di Bari, Italy

Chandrika Kamath

Lawrence Livermore National Laboratory, USA

Huan Liu

Arizona State University, USA

Sach Mukherjee

University of Oxford, England

Alan Oppenheim

Montclair State University, USA

Marco Ramoni

Harvard University, USA

Mehran Sahami

Stanford University, USA

Michele Sebag

Universite Paris-Sud, France

Alexander Tuzhilin

New York University, USA

Ning Zhong

Maebashi Institute of Technology, Japan