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Titlebook: Database Systems for Advanced Applications; DASFAA 2018 Internat Chengfei Liu,Lei Zou,Jianxin Li Conference proceedings 2018 Springer Inter

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發(fā)表于 2025-3-21 18:57:02 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Database Systems for Advanced Applications
副標題DASFAA 2018 Internat
編輯Chengfei Liu,Lei Zou,Jianxin Li
視頻videohttp://file.papertrans.cn/264/263397/263397.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Database Systems for Advanced Applications; DASFAA 2018 Internat Chengfei Liu,Lei Zou,Jianxin Li Conference proceedings 2018 Springer Inter
描述.This book constitutes the workshop proceedings of the 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, held in Gold Coast, QLD, Australia, in May 2018.?..The 23 full papers presented were carefully selected and reviewed from 44 submissions to the four following workshops: the 5th International Workshop on Big Data Management and Service, BDMS 2018; the Third International Workshop on Big Data Quality Management, BDQM 2018; the Second International Workshop on Graph Data Management and Analysis, GDMA 2018; and the 5th International Workshop on Semantic Computing and Personalization, SeCoP 2018..
出版日期Conference proceedings 2018
關鍵詞artificial intelligence; automata theory; computer networks; data management applications; data manageme
版次1
doihttps://doi.org/10.1007/978-3-319-91455-8
isbn_softcover978-3-319-91454-1
isbn_ebook978-3-319-91455-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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https://doi.org/10.1007/978-3-030-81896-8on brings new challenges including high scalability, coexistence of tautonymy and synonym, complex similarity metrics as well as the requirement of data quality evaluation based on entity resolution. Facing these challenges, we introduce our solutions briefly and discuss the possible future work for entity resolution in big data era.
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Andranik S. Tangian,Josef Gruber, it is significant and essential to infer point-of-interests (POI) categories from trajectory data for user modeling and user preferences mining in many location-based services (LBS). Recent researches focus more on recommendation and prediction of next POI, which are based on the check-in data. Ch
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Experiments with Preference Functionsed in some applications. In this paper, we proposed an approach based on ALO and differential mutation operator that called ALO-DM. In this method, differential mutation operator and greedy strategy enhance the diversity of the population. In addition, combining it with data mining algorithms can be
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