Within thelastfewyears,datawarehousingandknowledgediscoverytechnology has established itself as a key technology for enterprises that wish to improve the quality of the results obtained from data analysis, decision support, and the automatic extraction of knowledge from data. The 6th InternationalConference on Data Warehousing and KnowledgeD- covery (DaWaK 2004) continued a series of successful conferences dedicated to this topic. Its main objective was to bring together researchers and practiti- ers to discuss research issues and experience in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. The conference focused on the logical and physical design of data wareho- ing and knowledge discovery systems. The scope of the papers covers the most recent and relevant topics in the areas of data cubes and queries, multidim- sionaldatamodels,XMLdatamining,datasemanticsandclustering,association rules, data mining techniques, data analysis and discovery, query optimization, datacleansing,datawarehousedesignandmaintenance,andapplications.These proceedings contain the technical papers selected for presentation at the conf- ence. We received more than 100 papers, including 12 industrial papers, from over 33 countries, and the program committee ?nally selected 40 papers. The conf- ence program included an invited talk by Kazuo Iwano, IBM Tokyo Research Lab, Japan. We would like to thank the DEXA 2004 Workshop General Chairs (Prof.

This book constitutes the refereed proceedings of the 5th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2003, held in Prague, Czech Republic in September 2003.

The 41 revised full papers presented were carefully reviewed and selected from more than 130 submissions. The papers are organized in topical sections on data cubes and queries, multidimensional data models, Web warehousing, change detection, Web mining and association rules, association rules and decision trees, clustering, association rule mining, data analysis and discovery, ontologies and improving data quality, queries and data patterns, improving database query engines, and sampling and vector classification.