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

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.

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. 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.