Data Warehousing and Knowledge Discovery: 4th International Conference, DaWaK 2002, Aix-en-Provence, France, September 4-6, 2002. Proceedings (Lecture Notes in Computer Science, 2454). Questo articolo non è disponibile.Data Warehousing and Knowledge Discovery: 4th International Conference, DaWaK 2002, Aix-en-Provence, France, September 4-6, 2002. Proceedings (Lecture Notes in Computer Science, 2454)

Within the last few years Data Warehousing and Knowledge Discovery technology 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 Fourth International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2002) continues a series of successful conferences dedicated to this topic. Its main objective is to bring together researchers and practitioners to discuss research issues and experience in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. The conference focuses on the logical and physical design of data warehousing and knowledge discovery systems. The scope of the papers covers the most recent and relevant topics in the areas of association rules, clustering, Web mining, security, data mining techniques, data cleansing, applications, data warehouse design and maintenance, and OLAP. These proceedings contain the technical papers selected for presentation at the conference. We received more than 100 papers from over 20 countries, and the program committee finally selected 32 papers. The conference program included one invited talk: “Text Mining Applications of a Shallow Parser” by Walter Daelemans, Univer- ty of Antwerp, Belgium. We would like to thank the DEXA 2002 Workshop General Chair (Roland Wagner) th and the organizing committee of the 13 International Conference on Database and Expert Systems Applications (DEXA 2002) for their support and their cooperation.

Association Rules.- A Comparison between Query Languages for the Extraction of Association Rules.- Learning from Dissociations*.- Mining Association Rules from XML Data.- Estimating Joint Probabilities from Marginal Ones*.- Clustering.- Self-Tuning Clustering: An Adaptive Clustering Method for Transaction Data.- CoFD: An Algorithm for Non-distance Based Clustering in High Dimensional Spaces*.- An Efficient K-Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search.- Web Mining and Security.- A Hybrid Approach to Web Usage Mining.- Building and Exploiting Ad Hoc Concept Hierarchies for Web Log Analysis.- Authorization Based on Evidence and Trust*.- An Algorithm for Building User-Role Profiles in a Trust Environment1.- Data Mining Techniques.- Neural-Based Approaches for Improving the Accuracy of Decision Trees.- Approximate k-Closest-Pairs with Space Filling Curves.- Optimal Dimension Order: A Generic Technique for the Similarity Join.- Fast Discovery of Sequential Patterns by Memory Indexing.- Data Cleansing.- Dynamic Similarity for Fields with NULL Values.- Outlier Detection Using Replicator Neural Networks.- The Closed Keys Base of Frequent Itemsets.- Applications.- New Representation and Algorithm for Drawing RNA Structure with Pseudoknots*.- Boosting Naive Bayes for Claim Fraud Diagnosis.- Optimization of Association Word Knowledge Base through Genetic Algorithm.- Mining Temporal Patterns from Health Care Data*.- Data Warehouse Design.- Adding a Performance-Oriented Perspective to Data Warehouse Design.- Cost Modeling and Estimation for OLAP-XML Federations.- Constraint-Free Join Processing on Hyperlinked Web Data.- Focusing on Data Distribution in the WebD2W System.- OLAP.- A Decathlon in Multidimensional Modeling: Open Issues and Some Solutions.- Modeling and Imputation of Large Incomplete Multidimensional Datasets.- PartJoin:An Efficient Storage and Query Execution for Data Warehouses.- Data Warehouse Maintenance.- A Transactional Approach to Parallel Data Warehouse Maintenance.- Striving towards Near Real-Time Data Integration for Data Warehouses.- Time-Interval Sampling for Improved Estimations in Data Warehouses.