Data Warehousing and Knowledge Discovery : 11th International Conference, DaWaK 2009 Linz, Austria, August 31--September 2, 2009 Proceedings. This item is unavailable.Data Warehousing and Knowledge Discovery : 11th International Conference, DaWaK 2009 Linz, Austria, August 31--September 2, 2009 Proceedings

Data warehousing and knowledge discovery are increasingly becoming mission-critical technologies for most organizations, both commercial and public, as it becomes incre- ingly important to derive important knowledge from both internal and external data sources. With the ever growing amount and complexity of the data and information available for decision making, the process of data integration, analysis, and knowledge discovery continues to meet new challenges, leading to a wealth of new and exciting research challenges within the area. Over the last decade, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has established itself as one of the most important international scientific events within data warehousing and knowledge discovery. DaWaK brings together a wide range of researchers and practitioners working on these topics. The DaWaK conference series thus serves as a leading forum for discu- ing novel research results and experiences within data warehousing and knowledge th discovery. This year’s conference, the 11 International Conference on Data Wa- housing and Knowledge Discovery (DaWaK 2009), continued the tradition by d- seminating and discussing innovative models, methods, algorithms, and solutions to the challenges faced by data warehousing and knowledge discovery technologies.

This book constitutes the refereed proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2009 held in Linz, Austria in August/September 2009.

The 36 revised full papers presented were carefully reviewed and selected from 124 submissions. The papers are organized in topical sections on data warehouse modeling, data streams, physical design, pattern mining, data cubes, data mining applications, analytics, data mining, clustering, spatio-temporal mining, rule mining, and OLAP recommendation.