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Titlebook: Databases Theory and Applications; 25th Australasian Da Hua Wang,Mohamed A. Sharaf Conference proceedings 2014 Springer International Publi

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書(shū)目名稱Databases Theory and Applications
副標(biāo)題25th Australasian Da
編輯Hua Wang,Mohamed A. Sharaf
視頻videohttp://file.papertrans.cn/264/263533/263533.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Databases Theory and Applications; 25th Australasian Da Hua Wang,Mohamed A. Sharaf Conference proceedings 2014 Springer International Publi
描述This book constitutes the refereed proceedings of the 25th Australasian Database Conference, ADC 2014, held in Brisbane, NSW, Australia, in July 2014. The 15 full papers presented together with 6 short papers and 2 keynotes were carefully reviewed and selected from 38 submissions. A large variety of subjects are covered, including hot topics such as data warehousing; database integration; mobile databases; cloud, distributed, and parallel databases; high dimensional and temporal data; image/video retrieval and databases; database performance and tuning; privacy and security in databases; query processing and optimization; semi-structured data and XML; spatial data processing and management; stream and sensor data management; uncertain and probabilistic databases; web databases; graph databases; web service management; and social media data management.
出版日期Conference proceedings 2014
關(guān)鍵詞algorithms; big data; clustering; data management systems; data mining; data warehousing; database design
版次1
doihttps://doi.org/10.1007/978-3-319-08608-8
isbn_softcover978-3-319-08607-1
isbn_ebook978-3-319-08608-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
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Scalable Gaussian Process Regression for Prediction of Material Properties,e and efficient Gaussian process regression. The proposed strategy is applied to a real application involving the prediction of materials properties. Comprehensive tests have been conducted on two published properties data sets and the results demonstrate the high accuracy and efficiency of our new approach.
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Discovering Collective Group Relationships,ogeneous datasets. The method integrates canonical correlation analysis (CCA) with graph mining to find top-... Several experimental studies are conducted on both synthetic and real-world datasets to demonstrate the effectiveness and efficiency of the proposed method.
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