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Titlebook: Database Systems for Advanced Applications; 19th International C Sourav S. Bhowmick,Curtis E. Dyreson,Bernhard Thal Conference proceedings

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书目名称Database Systems for Advanced Applications
副标题19th International C
编辑Sourav S. Bhowmick,Curtis E. Dyreson,Bernhard Thal
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Database Systems for Advanced Applications; 19th International C Sourav S. Bhowmick,Curtis E. Dyreson,Bernhard Thal Conference proceedings
描述These two volumes set LNCS 8421 and LNCS 8422 constitutes the refereed proceedings of the 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014, held in Bali, Indonesia, in April 2014. The 62 revised full papers presented together with 1 extended abstract paper, 4 industrial papers, 6 demo presentations, 3 tutorials and 1 panel paper were carefully reviewed and selected from a total of 257 submissions. The papers cover the following topics: big data management, indexing and query processing, graph data management, spatio-temporal data management, database for emerging hardware, data mining, probabilistic and uncertain data management, web and social data management, security, privacy and trust, keyword search, data stream management and data quality.
出版日期Conference proceedings 2014
关键词big data; collaborative filtering; collaborative systems; crowdsourcing; data management systems
版次1
doihttps://doi.org/10.1007/978-3-319-05813-9
isbn_softcover978-3-319-05812-2
isbn_ebook978-3-319-05813-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
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Contemporary British Queer Performanceare further proposed based on the total relation. Finally, a multi-objective optimization strategy improves the overall recommendation quality by combining accuracy and novelty scores. Experimental results on a real dataset show that our proposed method outperforms state-of-the-art recommendation methods on both accuracy and novelty.
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Efficient Sampling Methods for Shortest Path Query over Uncertain Graphstain graphs is #P-hard, we propose different kinds of sampling methods to efficiently compute an approximate answer. The error is very small in our algorithm, which is proved and further verified in our experiments.
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