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Titlebook: Data Management and Analytics for Medicine and Healthcare; Third International Edmon Begoli,Fusheng Wang,Gang Luo Conference proceedings 2

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發(fā)表于 2025-3-21 17:41:42 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Management and Analytics for Medicine and Healthcare
副標(biāo)題Third International
編輯Edmon Begoli,Fusheng Wang,Gang Luo
視頻videohttp://file.papertrans.cn/263/262857/262857.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Data Management and Analytics for Medicine and Healthcare; Third International  Edmon Begoli,Fusheng Wang,Gang Luo Conference proceedings 2
描述This book constitutes the thoroughly refereed conference proceedings of the Third International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2017, in Munich, Germany, in September 2017, held in conjunction with the 43rd International Conference on Very Large Data Bases, VLDB 2017.?. The 9 revised full papers presented together with 2 keynote abstracts were carefully reviewed and selected from 16 initial submissions. The papers are organized in topical sections on data privacy and trustability for electronic health records; biomedical data management and Integration; online mining of Health related data; and clinical data analytics..
出版日期Conference proceedings 2017
關(guān)鍵詞Association Rule Learning; Big Data; Data Analysis; Databases; Genomics; Information management; Mobile Co
版次1
doihttps://doi.org/10.1007/978-3-319-67186-4
isbn_softcover978-3-319-67185-7
isbn_ebook978-3-319-67186-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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https://doi.org/10.1007/978-3-319-67186-4Association Rule Learning; Big Data; Data Analysis; Databases; Genomics; Information management; Mobile Co
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978-3-319-67185-7Springer International Publishing AG 2017
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https://doi.org/10.1007/978-3-319-08278-3and variety of big data, materialized data integration is often infeasible or too expensive including the costs of bandwidth, storage, maintenance, and management. . (.n-demand .ig Data .ntegration, .istribution, and .rchestration .ystem) provides a novel on-demand integration approach for heterogen
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https://doi.org/10.1007/978-1-4302-1145-7merging large number of individual Genome-Wide Associations Studies (GWAS) data. Although the emerging of big data platforms such as Hadoop and Spark shed lights on this problem, the inefficiency of keeping data in total-sorted order as well as the workload imbalance problem limit their performance.
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