Yahiko Kambayashi / Mukesh Mohania / Wolfgang Wöß (eds.)

Data Warehousing and Knowledge Discovery

6th International Conference, DaWaK 2004, Zaragoza, Spain, September 1-3, 2004, Proceedings
Herausgegeben:Kambayashi, Yahiko; Mohania, Mukesh; Wöß, Wolfram

• Broschiertes Buch

Produktbeschreibung

This book constitutes the refereed proceedings of the 6th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2004, held in Zaragoza, Spain, in September 2004.

The 40 revised full papers presented were carefully reviewed and selected from over 100 submissions. The papers are organized in topical sections on data warehouse design; knowledge discovery framework and XML data mining, data cubes and queries; multidimensional schema and data aggregation; inductive databases and temporal rules; industrial applications; data clustering; data visualization and exploration; data classification, extraction, and interpretation; data semantics, association rule mining; event sequence mining; and pattern mining.

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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.

Produktdetails

• Lecture Notes in Computer Science 3181
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 11312215, 978-3-540-22937-7
• 2004
• Seitenzahl: 432
• Erscheinungstermin: 18. August 2004
• Englisch
• Abmessung: 235mm x 155mm x 24mm
• Gewicht: 700g
• ISBN-13: 9783540229377
• ISBN-10: 354022937X
• Artikelnr.: 14754822

Autorenporträt

Yahiko Kambayashi, University of Kyoto, Japan / Mukesh Mohania, I.B.M. India Research Lab., New Delhi, India / Wolfgang Wöß, University of Linz, Austria

Inhaltsangabe

Data Warehousing Design.- Conceptual Design of XML Document Warehouses.- Bringing Together Partitioning, Materialized Views and Indexes to Optimize Performance of Relational Data Warehouses.- GeoDWFrame: A Framework for Guiding the Design of Geographical Dimensional Schemas.- Workload-Based Placement and Join Processing in Node-Partitioned Data Warehouses.- Knowledge Discovery Framework and XML Data Minig.- Novelty Framework for Knowledge Discovery in Databases.- Revisiting Generic Bases of Association Rules.- Mining Maximal Frequently Changing Subtree Patterns from XML Documents.- Discovering Pattern-Based Dynamic Structures from Versions of Unordered XML Documents.- Data Cubes and Queries.- Space-Efficient Range-Sum Queries in OLAP.- Answering Approximate Range Aggregate Queries on OLAP Data Cubes with Probabilistic Guarantees.- Computing Complex Iceberg Cubes by Multiway Aggregation and Bounding.- Multidimensional Schema and Data Aggregation.- An Aggregate-Aware Retargeting Algorithm for Multiple Fact Data Warehouses.- A Partial Pre-aggregation Scheme for HOLAP Engines.- Discovering Multidimensional Structure in Relational Data.- Inductive Databases and Temporal Rules.- Inductive Databases as Ranking.- Inductive Databases of Polynomial Equations.- From Temporal Rules to Temporal Meta-rules.- Industrial Track.- How Is BI Used in Industry?: Report from a Knowledge Exchange Network.- Towards an Adaptive Approach for Mining Data Streams in Resource Constrained Environments.- Data Clustering.- Exploring Possible Adverse Drug Reactions by Clustering Event Sequences.- SCLOPE: An Algorithm for Clustering Data Streams of Categorical Attributes.- Novel Clustering Approach that Employs Genetic Algorithm with New Representation Scheme and Multiple Objectives.- Data Visualizationand Exploration.- Categorical Data Visualization and Clustering Using Subjective Factors.- Multidimensional Data Visual Exploration by Interactive Information Segments.- Metadata to Support Transformations and Data & Metadata Lineage in a Warehousing Environment.- Data Classification, Extraction and Interpretation.- Classification Based on Attribute Dependency.- OWDEAH: Online Web Data Extraction Based on Access History.- Data Mining Approaches to Diffuse Large B-Cell Lymphoma Gene Expression Data Interpretation.- Data Semantics.- Deriving Multiple Topics to Label Small Document Regions.- Deriving Efficient SQL Sequences via Read-Aheads.- Diversity in Random Subspacing Ensembles.- Association Rule Mining.- Partitioned Approach to Association Rule Mining over Multiple Databases.- A Tree Partitioning Method for Memory Management in Association Rule Mining.- Mining Interesting Association Rules for Prediction in the Software Project Management Area.- Mining Event Sequences.- PROWL: An Efficient Frequent Continuity Mining Algorithm on Event Sequences.- Algorithms for Discovery of Frequent Superset, Rather Than Frequent Subset.- Pattern Mining.- Improving Direct Counting for Frequent Itemset Mining.- Mining Sequential Patterns with Item Constraints.- Mining Borders of the Difference of Two Datacubes.- Mining Periodic Patterns in Sequence Data.