Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches

Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches

Release Date: April, 2011|Copyright: © 2011 |Pages: 410
DOI: 10.4018/978-1-60960-537-7
ISBN13: 9781609605377|ISBN10: 1609605373|EISBN13: 9781609605384
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Description & Coverage
Description:

Over the years, advances in the business world as well as the changing of diverse application contexts, have caused Data Warehousing and Data Mining to become more paramount in our society. The two share many common issues and are commonly interrelated.

Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches provides a comprehensive compilation of knowledge covering state-of-the-art developments and research, as well as current innovative activities in data warehousing and mining. This book focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real world problems and provides a broad perspective on the future of these two cohesive topic areas.

Coverage:

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

  • Algorithms
  • Applications issues
  • Data mining methods
  • Data models
  • Data structures
  • Data warehousing process
  • Knowledge discovery process
  • Mining databases
  • Online analytical process
  • Practical issues
  • Tools and Languages
Reviews & Statements

A data warehouse stores a massive amount of data integrated from data sources, which can reflect the reality of the real world for reporting and analysis purposes, and its tools can be used to discover from the data, the trend or potential direction of developments. Therefore, in application areas such as commerce, health care and monitoring of global changes in the environment and biodiversity, data warehouses are used extensively for the purposes of inquiries, decision making and data mining. The purpose of this book is to present and disseminate the latest developments in data warehousing. The focus is on the most recent research and discoveries, in particular several interesting issues and trends that have emerged in the last few years.

– David Taniar, Monash University, Australia; and Li Chen, LaTrobe University, Australia

Computer scientists from around the world report recent developments in data warehousing, focusing not on the static storage of data, but on how the data can be used to devise or refine business strategies and scientific models. They consider such aspects as a parametrized framework for clustering streams, a hybrid method for mining high-utility item sets in large high-dimensional data, handling the evolution of external data sources in a data warehouse architecture, open source tools for business intelligence, a dynamic and semantically-aware technique for document clustering in biomedical literature, and performance analysis of reliability estimates for regression predictions.

– Book News, Reference - Research Book News - August 2011
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Editor/Author Biographies
David Taniar holds Bachelor, Master, and PhD degrees - all in Computer Science, with a particular specialty in Databases. His current research is applying data management techniques to various domains, including mobile and geography information systems, parallel and grid computing, web engineering, and data mining. Every year he publishes extensively, including his recent co-authored book: High Performance Parallel Database Processing and Grid Databases (John Wiley & Sons, 2008). His list of publications can be viewed at the DBLP server (http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/t/Taniar:David.html). He is a founding editor-in-chief of three SCI-E journals: Intl. J. of Data Warehousing and Mining, Mobile Information Systems, and Intl. J. of Web and Grid Services. He is currently an Associate Professor at the Faculty of Information Technology, Monash University, Australia.
Li Chen obtained a Masters degree in Computer Science from La Trobe University, Australia, in 2008. Her thesis was in the area of near real-time data warehousing. Currently, she is pursuing a PhD degree at the Department of Computer Science, La Trobe University. Her research interests include near-real time data warehousing, temporal data warehousing and spatial temporal data warehousing.
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