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Titlebook: Data Science; 30th British Interna Sebastian Maneth Conference proceedings 2015 Springer International Publishing Switzerland 2015 Web data

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发表于 2025-3-21 16:31:26 | 显示全部楼层 |阅读模式
书目名称Data Science
副标题30th British Interna
编辑Sebastian Maneth
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Data Science; 30th British Interna Sebastian Maneth Conference proceedings 2015 Springer International Publishing Switzerland 2015 Web data
描述.This book constitutes the refereed conference proceedings of the 30th British International Conference on Databases, BICOD 2015 - formerly known as BNCOD (British National Conference on Databases) - held in Edinburgh, UK, in July 2015..The 19 revised full papers, presented together with three invited.keynotes and three invited lectures were carefully reviewed and selected from 37 submissions. Special focus of the conference has been "Data Science" and so the papers cover a wide range of topics related to databases and data-centric computation..
出版日期Conference proceedings 2015
关键词Web databases; Web information extraction; big data; classification; cooperative game; data integration; d
版次1
doihttps://doi.org/10.1007/978-3-319-20424-6
isbn_softcover978-3-319-20423-9
isbn_ebook978-3-319-20424-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

书目名称Data Science影响因子(影响力)




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书目名称Data Science网络公开度




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书目名称Data Science被引频次




书目名称Data Science被引频次学科排名




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书目名称Data Science年度引用学科排名




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书目名称Data Science读者反馈学科排名




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A Fast Approach for Detecting Overlapping Communities in Social Networks Based on Game Theoryuch more connected to each other than to the rest of the network. The cooperative theory and non-cooperative game theory have been used separately for detecting communities. In this paper, we develop a new approach that utilizes both cooperative and non-cooperative game theory to detect communities.
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Query-Oriented Summarization of RDF Graphs given an input RDF graph ., find an RDF graph . which summarizes . as accurately as possible, while being possibly orders of magnitude smaller than the original graph. Our approach is ., i.e., querying a summary of a graph should reflect whether the query has some answers against this graph. The su
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