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Titlebook: Web and Big Data; 6th International Jo Bohan Li,Lin Yue,Toshiyuki Amagasa Conference proceedings 2023 The Editor(s) (if applicable) and The

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发表于 2025-3-21 19:38:16 | 显示全部楼层 |阅读模式
书目名称Web and Big Data
副标题6th International Jo
编辑Bohan Li,Lin Yue,Toshiyuki Amagasa
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Web and Big Data; 6th International Jo Bohan Li,Lin Yue,Toshiyuki Amagasa Conference proceedings 2023 The Editor(s) (if applicable) and The
描述This three-volume set, LNCS 13421, 13422 and 13423, constitutes the thoroughly refereed proceedings of the 6th International Joint Conference, APWeb-WAIM 2022, held in Nanjing, China, in August 2022..The 75 full papers presented together with 45 short papers, and 5 demonstration papers were carefully reviewed and selected from 297 submissions. The papers are organized around the following topics: Big Data Analytic and Management, Advanced database and web applications, Cloud Computing and Crowdsourcing, Data Mining, Graph Data and Social Networks, Information Extraction and Retrieval, Knowledge Graph, Machine Learning, Query processing and optimization, Recommender Systems, Security, privacy, and trust and Blockchain data management and applications, and Spatial and multi-media data..
出版日期Conference proceedings 2023
关键词artificial intelligence; bandwidth; communication systems; computer hardware; computer networks; computer
版次1
doihttps://doi.org/10.1007/978-3-031-25158-0
isbn_softcover978-3-031-25157-3
isbn_ebook978-3-031-25158-0Series 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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A Context Model for Personal Data Streamsotations, as they can be collected from mobile devices, e.g. smartphones, smartwatches or fitness trackers. The proposed model is validated on a very large dataset about the everyday life of one hundred and fifty-eight people over four weeks, twenty-four hours a day.
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978-3-031-25157-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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https://doi.org/10.1007/978-3-031-25158-0artificial intelligence; bandwidth; communication systems; computer hardware; computer networks; computer
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MCSketch: An Accurate Sketch for Heavy Flow Detection and Heavy Flow Frequency Estimationer accuracy than existing algorithms for heavy flow detection. Moreover, MCSketch reduces the average relative error by approximately 1 to 3 orders of magnitude in comparison to other state-of-art approaches.
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MCSketch: An Accurate Sketch for Heavy Flow Detection and Heavy Flow Frequency Estimationer accuracy than existing algorithms for heavy flow detection. Moreover, MCSketch reduces the average relative error by approximately 1 to 3 orders of magnitude in comparison to other state-of-art approaches.
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