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Titlebook: Data Warehousing and Knowledge Discovery; First International Mukesh Mohania,A Min Tjoa Conference proceedings 1999 Springer-Verlag Berlin

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書目名稱Data Warehousing and Knowledge Discovery
副標題First International
編輯Mukesh Mohania,A Min Tjoa
視頻videohttp://file.papertrans.cn/264/263343/263343.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Data Warehousing and Knowledge Discovery; First International  Mukesh Mohania,A Min Tjoa Conference proceedings 1999 Springer-Verlag Berlin
描述This book constitutes the refereed proceedings of the First International Conference on Data Warehousing and Knowledge Discovery, DaWaK‘99, held in Florence, Italy in August/September 1999. The 31 revised full papers and nine short papers presented were carefully reviewed and selected from 88 submissions. The book is divided in topical sections on data warehouse design; online analytical processing; view synthesis, selection, and optimization; multidimensional databases; knowledge discovery; association rules; inexing and object similarities; generalized association rules and data and web mining; time series data bases; data mining applications and data analysis.
出版日期Conference proceedings 1999
關(guān)鍵詞Data warehousing; Web Mining; algorithms; data analysis; data mining; data warehouse; database; design; gene
版次1
doihttps://doi.org/10.1007/3-540-48298-9
isbn_softcover978-3-540-66458-1
isbn_ebook978-3-540-48298-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 1999
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Compressed Datacubes for Fast OLAP Applicationsith expected value and variance) of range query answers and compare the accuracies of their estimations. We finally present a comparative analysis with other recently proposed techniques; the results confirm the effectiveness of our approach.
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Genetic Algorithm for Materialized View Selection in Data Warehouse Environmentsized is challenging. Several heuristic algorithms have been proposed in the past to tackle with this problem. In this paper, we propose a completely different approach, Genetic Algorithm, to choose materialized views and demonstrate that it is practical and effective compared with heuristic approaches.
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