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Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito

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书目名称Database Systems for Advanced Applications
副标题29th International C
编辑Makoto Onizuka,Jae-Gil Lee,Kejing Lu
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito
描述.The seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024...The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions...Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included...The conference presents papers on subjects such as:..Part I: Spatial and temporal data; database core technology; federated learning...Part II: Machine learning; text processing...Part III: Recommendation; multi-media...Part IV: Privacy and security; knowledge base and graphs...Part V: Natural language processing; large language model; time series and stream data...Part VI: Graph and network; hardware acceleration...Part VII: Emerging application; industry papers; demo papers... .
出版日期Conference proceedings 2024
关键词Cloud data management; Data mining and knowledge discovery; Data quality and credibility; Data model an
版次1
doihttps://doi.org/10.1007/978-981-97-5552-3
isbn_softcover978-981-97-5551-6
isbn_ebook978-981-97-5552-3Series 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 Singapor
The information of publication is updating

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om Raw-ETA. After that, we design a SPETAformer block to ensure that the extract module can capture the multidimensional correlation of the above features. The recurrent module further enhances the learning ability of the MIPM in the temporal domain by week, daily, recent, and weather four different
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Don Syme,Adam Granicz,Antonio Cisterninorelationships. We also incorporate transformer-encoder or RNN modules to enhance the ability to retain temporal dynamics for time series feature extraction. Several comparisons and ablation experiments on three multivariate time series datasets have been conducted. Our results demonstrate that TimeG
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Marketing Your Business Worldwideew entities’ data via neighbors merging. Secondly, we propose an informative temporal memory buffer to help the model emphasize valuable timestamps extracted using influence functions within time intervals. This memory buffer allows INF-GNN to discern influential temporal patterns. Finally, RI loss
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