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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書目名稱Big Data影響因子(影響力)




書目名稱Big Data影響因子(影響力)學科排名




書目名稱Big Data網(wǎng)絡公開度




書目名稱Big Data網(wǎng)絡公開度學科排名




書目名稱Big Data被引頻次




書目名稱Big Data被引頻次學科排名




書目名稱Big Data年度引用




書目名稱Big Data年度引用學科排名




書目名稱Big Data讀者反饋




書目名稱Big Data讀者反饋學科排名

公式

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

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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

郊外

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

拱墻

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

深陷

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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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