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Titlebook: Big Data – BigData 2023; 12th International C Shunli Zhang,Bo Hu,Liang-Jie Zhang Conference proceedings 2023 The Editor(s) (if applicable)

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發(fā)表于 2025-3-21 19:31:08 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Big Data – BigData 2023
期刊簡(jiǎn)稱12th International C
影響因子2023Shunli Zhang,Bo Hu,Liang-Jie Zhang
視頻videohttp://file.papertrans.cn/186/185722/185722.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Big Data – BigData 2023; 12th International C Shunli Zhang,Bo Hu,Liang-Jie Zhang Conference proceedings 2023 The Editor(s) (if applicable)
影響因子.This book constitutes the refereed proceedings of the 12th International Conference, BigData 2023, Held as Part of the Services Conference Federation, SCF 2023, Honolulu, HI, USA, during September 23–26, 2023.??..The 14?full papers presented together with 2 short papers were carefully reviewed and selected from 27 submissions.?The conference focuses on??research track and application track..
Pindex Conference proceedings 2023
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IoT Lakehouse: A New Data Management Paradigm for?AIoTapplications across various domains. However, managing and utilizing the massive and heterogeneous data generated by IoT and AIoT devices poses significant challenges for traditional data management systems. In this paper, we present a new data management paradigm called IoT Lakehouse, which aims to
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HMGR: A Hybrid Model for Geolocation Recommendationiences and the virtual world. As a significant application of geosocial networks, location recommendation suggests places that individuals may find interesting, offering valuable references for their travels and greatly enhancing their lives. Consequently, the challenge of recommending relevant loca
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Non-essential Perspective on Thinking Law of Data Protection and Utilizationsentialism of theoretical thought, which hinders researchers and legislators from exploring the institutional framework that matches the reality of digital society according to the inherent differences of data concepts, the process of data practice, the application and value tradeoffs in this proces
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Lightweight Weight Update for?Convolutional Neural Networkse of convolutional neural networks. The existing pooling operations result in a large number of weight parameter updates of convolutional neural networks, causing large memory usage. In this paper, a pooling operation called ApproxM is proposed to address this problem. The proposed pooling operation
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