The integrated use of data mining and data warehousing techniques such as Online Analytical Processing (OLAP) has received considerable attention from researchers and practitioners alike, as they are key tools used in knowledge discovery from large data datasets. Although, a variety of integrated approaches have been proposed in the literature to mine large datasets for discovering knowledge. However, a number of issues remain unresolved in the previous work, especially on intelligent data analysis front.

This book aims to discuss and address the difficulties and challenges that of seamless integration of the two core disciplines of knowledge discovery. The editor will seek chapters that address different methods and techniques of integration for enhancing the overall goal of knowledge discovery. Additionally, the book will explore the impact of such techniques in a variety of application domains ranging from government, education, science, agriculture engineering etc.



Objective
The primary objective of this book is to provide insights concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. This is a front-line and important topic that is of interest in both industry and knowledge engineering research. The current approaches of knowledge discovery in industry are ad-hoc where data mining and warehousing is dealt separately. There is no standard rule of thumb in integrating these two disciplines. This book is an excellent opportunity to report on the existing gaps in this area and to propose novel approaches to bridge the existing gaps.

Target Audience
Decision makers, academicians, researchers, advanced-level students, technology developers, and Business Intelligence professionals will find this text useful in furthering their research exposure to relevant topics in knowledge discovery and assisting in furthering their own research efforts in this field.

Recommended Topics
Contributors are welcome to submit chapters on the following topics relating to data mining integration with data warehousing. Recommended topics include, but are not limited to, the following:

Data mining techniques: clustering, classification, association rules, decision trees, etc.
Data and knowledge representation
Knowledge discovery framework and process, including pre- and post-processing
Integration of data warehousing, OLAP and data mining
Exploring data analysis, inference of causes, prediction
Interactive data exploration/visualization and discovery
Data warehousing tools
OLAP and analytics tools
Data mining tools
Industry experiences
Data warehousing applications: corporate, scientific, government, healthcare, bioinformatics, etc.
Data mining applications: bioinformatics, E-commerce, Web, intrusion/fraud detection, finance, healthcare, marketing, telecommunications, etc
Data mining support for designing information systems


Submission Procedure
Researchers and practitioners are invited to submit on or before February 15, 2014, a 2-3 page chapter proposal clearly explaining the mission and concerns of his or her proposed chapter. Submissions should be made through the link at the bottom of this page. Authors of accepted proposals will be notified by March 30, 2014 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by June 15, 2014. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. For additional information regarding the publisher, please visit www.igi-global.com. This publication is anticipated to be released in 2015.