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Titlebook: Database Systems for Advanced Applications. DASFAA 2023 International Workshops; BDMS 2023, BDQM 2023 Amr El Abbadi,Gillian Dobbie,Hongzhi

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書目名稱Database Systems for Advanced Applications. DASFAA 2023 International Workshops
副標(biāo)題BDMS 2023, BDQM 2023
編輯Amr El Abbadi,Gillian Dobbie,Hongzhi Yin
視頻videohttp://file.papertrans.cn/264/263445/263445.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Database Systems for Advanced Applications. DASFAA 2023 International Workshops; BDMS 2023, BDQM 2023 Amr El Abbadi,Gillian Dobbie,Hongzhi
描述This volume constitutes the papers of several workshops which were held in conjunction with the 28th International Conference on Database Systems for Advanced Applications, DASFAA 2023, held in Tanjin, China, in April 2023.. The 23 revised full papers presented in this book were carefully reviewed and selected from 40 submissions. DASFAA 2023 presents the following four workshops:. 9th International Workshop on Big Data Management and Service (BDMS 2023), 8th International Workshop on Big Data Quality Management (BDQM 2023); 7th International Workshop on Graph Data Management and Analysis (GDMA 2023); 1st International Workshop on Bundle-based Recommendation Systems (BundleRS 2023)..
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer science; computer security; compu
版次1
doihttps://doi.org/10.1007/978-3-031-35415-1
isbn_softcover978-3-031-35414-4
isbn_ebook978-3-031-35415-1Series 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
The information of publication is updating

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978-3-031-35414-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Gel Entrapped Multistep Systemsnce of safety constraints may lead to collisions in the real world, especially in a complex environment. In this paper, we tackle this problem with a novel trajectory prediction algorithm called Efficient Graph Learning (EGL), which utilizes road network information, self-attention mechanism, and sa
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Hermann Locarek-Junge,Stefan Albersill two significant challenges. First, the model requires to be fine-tuned regularly with the increasing number of patients; Secondly, the quality of text generation needs to be improved because medical observations are complex. In order to solve above challenges, we propose Memory Enhanced Pretrain
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