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Data Warehousing and Knowledge Discovery [electronic resource] :13th International Conference, DaWaK 2011, Toulouse, France, August 29-September 2,2011. Proceedings / edited by Alfredo Cuzzocrea, Umeshwar Dayal.

by Cuzzocrea, Alfredo [editor.]; Dayal, Umeshwar [editor.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 6862Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.Description: XIV, 498p. 180 illus. online resource.ISBN: 9783642235443.Subject(s): Computer science | Computer Communication Networks | Data encryption (Computer science) | Computer software | Information systems | Information Systems | Computer Science | Computer Communication Networks | Management of Computing and Information Systems | Data Encryption | Computers and Society | Information Systems Applications (incl.Internet) | Algorithm Analysis and Problem ComplexityDDC classification: 004.6 Online resources: Click here to access online In: Springer eBooksSummary: This book constitutes the refereed proceedings of the 13th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2011 held in Toulouse, France in August/September 2011. The 37 revised full papers presented were carefully reviewed and selected from 119 submissions. The papers are organized in topical sections on physical and conceptual data warehouse models, data warehousing design methodologies and tools, data warehouse performance and optimization, pattern mining, matrix-based mining techniques and stream, sensor and time-series mining.
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This book constitutes the refereed proceedings of the 13th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2011 held in Toulouse, France in August/September 2011. The 37 revised full papers presented were carefully reviewed and selected from 119 submissions. The papers are organized in topical sections on physical and conceptual data warehouse models, data warehousing design methodologies and tools, data warehouse performance and optimization, pattern mining, matrix-based mining techniques and stream, sensor and time-series mining.

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