Data Warehousing and Knowledge Discovery de Yahiko Kambayashi

Data Warehousing and Knowledge Discovery

Yahiko Kambayashi

Werner Winiwarter

Masatoshi Arikawa

en Limba Engleză Paperback – 27 aug 2001

Data Warehousing and Knowledge Discovery technology is emerging as a key technology for enterprises that wish to improve their data analysis, decision support activities, and the automatic extraction of knowledge from data. The objective of the Third International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2001) was 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 focused on the logical and physical design of data warehousing and knowledge discovery systems. The scope of the papers covered the most recent and relevant topics in the areas of association rules, mining temporal patterns, data mining techniques, collaborative filtering, Web mining, visualization, matchmaking, evelopment and maintenance of data warehouses, OLAP, and distributed data warehouses. These proceedings contain the technical papers selected for presentation at the conference. We received more than 90 papers from over 20 countries, and the program committee finally selected 34 papers. The conference program included one invited talk: “Knowledge Management in Heterogeneous Data Warehouse Environments” by Professor Larry Kerschberg, George Mason University, USA.

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Cuprins

Invited Talk.- Knowledge Management in Heterogeneous Data Warehouse Environments.- Association Rules (1).- Mining Generalized Association Rules with Multiple Minimum Supports.- A Theoretical Framework for Association Mining based on the Boolean Retrieval Model.- Mining Inter-Transactional Association Rules: Generalization and Empirical Evaluation.- On the Equivalence of Top-down and Bottom-up Data Mining in Relational Databases.- Association Rules (2).- Monitoring Change in Mining Results.- Discovering Knowledge from Meteorological Databases: A Meteorological Aviation Forecast Study.- Enhancing the Apriori Algorithm for Frequent Set Counting.- Mining Temporal Patterns.- Mining Cyclically Repeated Patterns.- Development of Automated Data Mining System for Quality Control in Manufacturing.- Data Mining Techniques.- FastFDs: A Heuristic-Driven, Depth-First Algorithm for Mining Functional Dependencies from Relation Instances Extended Abstract.- Ensemble Feature Selection Based on the Contextual Merit.- Interactive Clustering for Transaction Data.- Collaborative Filtering and Web Mining.- A New Approach For Item Choice Recommendations.- RecTree: An Efficient Collaborative Filtering Method.- Discovering Web Document Associations for Web Site Summarization.- Visualization and Matchmaking.- Data Visualization and Analysis with Self-Organizing Maps in Learning Metrics.- Towards a Novel OLAP Interface for Distributed Data Warehouses.- Matchmaking for Structured Objects.- Development of Data Warehouses.- Prototyping Data Warehouse Systems.- Information Warehouse for Medical Research.- Risk-Management for Data Warehouse Systems.- Maintenance of Data Warehouses.- PVM: Parallel View Maintenance under Concurrent Data Updates of Distributed Sources?.- An Experimental PerformanceEvaluation of Incremental Materialized View Maintenance in Object Databases.- Managing Time Consistency for Active Data Warehouse Environments.- OLAP (1).- Optimization Algorithms for Simultaneous Multidimensional Queries in OLAP Environments.- Improving the Performance of OLAP Queries Using Families of Statistics Trees.- Changes of Dimension Data in Temporal Data Warehouses.- OLAP (2).- Determining the Convex Hull in Large Multidimensional Databases.- Shared Index Scans for Data Warehouses.- Adaptable Similarity Search Using Vector Quantization.- Distributed Data Warehouses.- A Framework for Supporting Interoperability of Data Warehouse Islands Using XML.- Fragtique: Applying an OO Database Distribution Strategy to Data Warehouse.- Approximate Query Answering Using Data Warehouse Striping.