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Titlebook: Database Systems for Advanced Applications; 26th International C Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen Conference proceedings 2021 T

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发表于 2025-3-21 17:44:44 | 显示全部楼层 |阅读模式
书目名称Database Systems for Advanced Applications
副标题26th International C
编辑Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Database Systems for Advanced Applications; 26th International C Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen Conference proceedings 2021 T
描述.The three-volume set LNCS 12681-12683 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021, held in Taipei, Taiwan, in April 2021...The total of 156 papers presented in this three-volume set was carefully reviewed and selected from 490 submissions...The topic areas for the selected papers include information retrieval, search and recommendation techniques; RDF, knowledge graphs, semantic web, and knowledge management; and spatial, temporal, sequence, and streaming data management, while the dominant keywords are network, recommendation, graph, learning, and model. These topic areas and keywords shed the light on the direction where the research in DASFAA is moving towards...Due to the Corona pandemic this event was held virtually..
出版日期Conference proceedings 2021
关键词big data; graph data management; spatial data management; temporal data management; data mining; machine
版次1
doihttps://doi.org/10.1007/978-3-030-73194-6
isbn_softcover978-3-030-73193-9
isbn_ebook978-3-030-73194-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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978-3-030-73193-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263409.jpg
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International Series on Consumer ScienceWe propose a data model for investigating constraints that enforce the entity integrity of semi-structured big data. Particular support is given for the volume, variety, and veracity dimensions of big data.
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Lester D. Taylor,H.S. Houthakkeral whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited. In this paper, we propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs. In previous work, there
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Towards Consumer Demographic Perspectiveso the query and analysis of massive data. However, the insufficient utilization of system resources is an inherent problem of distributed computing engines. Particularly, when more jobs lead to execution blocking, the system schedules multiple jobs on a first-come-first-executed (FCFE) basis, even i
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Jo M. Martins,Farhat Yusuf,David A. Swansonshift, i.e. that the conditional distributions of data in different clients are disagreeing. A natural solution is to group clients with similar conditional distributions into the same cluster. However, methods following this approach leverage features extracted in federated settings (e.g., model we
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