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Titlebook: Database Systems for Advanced Applications; 22nd International C Sel?uk Candan,Lei Chen,Wen Hua Conference proceedings 2017 Springer Intern

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發(fā)表于 2025-3-21 18:20:24 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Database Systems for Advanced Applications
副標(biāo)題22nd International C
編輯Sel?uk Candan,Lei Chen,Wen Hua
視頻videohttp://file.papertrans.cn/264/263428/263428.mp4
概述Includes supplementary material:
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Database Systems for Advanced Applications; 22nd International C Sel?uk Candan,Lei Chen,Wen Hua Conference proceedings 2017 Springer Intern
描述This two volume set LNCS 10177 and 10178 constitutes the refereed proceedings of the 22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017, held in Suzhou, China, in March 2017. .?The 73 full papers, 9 industry papers, 4 demo papers and 3 tutorials were carefully selected from a total of 300 submissions. The papers are organized around the following topics: semantic web and knowledge management; indexing and distributed systems; network embedding; trajectory and time series data processing; data mining; query processing and optimization; text mining; recommendation; security, privacy, senor and cloud; social network analytics; map matching and spatial keywords; query processing and optimization; search and information retrieval; string and sequence processing; stream date processing; graph and network data processing; spatial databases; real time data processing; big data; social networks and graphs..
出版日期Conference proceedings 2017
關(guān)鍵詞big data; cloud computing; data mining; machine learning; recommender systems; algorithms; classification;
版次1
doihttps://doi.org/10.1007/978-3-319-55753-3
isbn_softcover978-3-319-55752-6
isbn_ebook978-3-319-55753-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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CirE: Circular Embeddings of Knowledge Graphscontinuous vector spaces and then constructs . triples. However, KG embedding models are sensitive to infrequent and uncertain objects. Furthermore, there is a contradiction between learning ability and learning cost. To this end, we propose circular embeddings (CirE) to learn representations of ent
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