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Titlebook: Big Data; 10th CCF Conference, Tianrui Li,Rui Mao,Jie Hu Conference proceedings 2022 The Editor(s) (if applicable) and The Author(s), under

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樓主
發(fā)表于 2025-3-21 17:17:49 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Big Data
期刊簡(jiǎn)稱10th CCF Conference,
影響因子2023Tianrui Li,Rui Mao,Jie Hu
視頻videohttp://file.papertrans.cn/186/185571/185571.mp4
學(xué)科分類Communications in Computer and Information Science
圖書(shū)封面Titlebook: Big Data; 10th CCF Conference, Tianrui Li,Rui Mao,Jie Hu Conference proceedings 2022 The Editor(s) (if applicable) and The Author(s), under
影響因子This book constitutes the refereed proceedings of the 10th CCF Conference on BigData 2022, which took place in Chengdu, China, in November 2022.?.The 8 full papers presented in this volume were carefully reviewed and selected from 28 submissions. The topics of accepted papers include theories and methods of data science, algorithms and applications of big data..
Pindex Conference proceedings 2022
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書(shū)目名稱Big Data影響因子(影響力)




書(shū)目名稱Big Data影響因子(影響力)學(xué)科排名




書(shū)目名稱Big Data網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Big Data網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Big Data被引頻次




書(shū)目名稱Big Data被引頻次學(xué)科排名




書(shū)目名稱Big Data年度引用




書(shū)目名稱Big Data年度引用學(xué)科排名




書(shū)目名稱Big Data讀者反饋




書(shū)目名稱Big Data讀者反饋學(xué)科排名




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沙發(fā)
發(fā)表于 2025-3-21 20:28:29 | 只看該作者
1865-0929 022.?.The 8 full papers presented in this volume were carefully reviewed and selected from 28 submissions. The topics of accepted papers include theories and methods of data science, algorithms and applications of big data..978-981-19-8330-6978-981-19-8331-3Series ISSN 1865-0929 Series E-ISSN 1865-0937
板凳
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地板
發(fā)表于 2025-3-22 08:25:46 | 只看該作者
J?rg-Uwe Nieland,Dagmar Hoffmannory matching tasks, shape-based trajectory search (STS) aims to find all trajectories that are similar in shape to the query trajectory, which may be judged to be dissimilar based on their coordinates. STS can be useful in various real world applications, such as geography discovery, animal migratio
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發(fā)表于 2025-3-22 12:21:32 | 只看該作者
Rationalit?tsverlust durch Intimisierung?n intuitive way to show how they understand and control semantics. In order to identify interpretable directions in GAN’s latent space, both supervised and unsupervised approaches have been proposed. But the supervised methods can only find the directions consistent with the supervised conditions. H
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發(fā)表于 2025-3-22 13:12:18 | 只看該作者
Patrik Ettinger,Mark Eisenegger,Roger BlumN) is an adaptive neighbor concept for solving this problem, which combines k-nearest neighbors and reverse k-nearest neighbors to adaptively obtain . value. It has been proven effective in clustering analysis, classification and outlier detection. However, the existing algorithms for searching NaN
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發(fā)表于 2025-3-22 20:41:40 | 只看該作者
J?rg-Uwe Nieland,Dagmar Hoffmannuyers to solve. To address this problem, we propose a knowledge graph convolutional network with user history and item entity augmentation for auto parts recommendation system. First, the knowledge graph of auto parts and the knowledge graph of users to find a set of node examples. Second, collect c
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發(fā)表于 2025-3-23 07:00:44 | 只看該作者
Intimit?t in p?dagogischen Beziehungench facilitates the use of KGs in downstream applications. However, most existing models ignore the semantic correlations among similar entities and relations. Indeed, we find there exist semantically similarities in entities or relations. To take advantage of these semantic correlations, we utilize
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