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Titlebook: Web Technologies and Applications; 13th Asia-Pacific We Xiaoyong Du,Wenfei Fan,Mohamed A. Sharaf Conference proceedings 2011 Springer Berli

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發(fā)表于 2025-3-21 18:20:02 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Web Technologies and Applications
副標題13th Asia-Pacific We
編輯Xiaoyong Du,Wenfei Fan,Mohamed A. Sharaf
視頻videohttp://file.papertrans.cn/1022/1021649/1021649.mp4
概述unique visibility state-of-the-art research fast-track conference proceedings
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Web Technologies and Applications; 13th Asia-Pacific We Xiaoyong Du,Wenfei Fan,Mohamed A. Sharaf Conference proceedings 2011 Springer Berli
描述This book constitutes the proceedings of the 13th Asia-Pacific Conference APWeb 2011 held in conjunction with the APWeb 2011 Workshops XMLDM and USD, in Beijing, China, in April 2011. The 26 full papers presented together with 10 short papers, 3 keynote talks, and 4 demo papers were carefully reviewed and selected from 104 submissions. The submissions range over a variety of topics such as classification and clustering; spatial and temporal databases; personalization and recommendation; data analysis and application; Web mining; Web search and information retrieval; complex and social networks; and secure and semantic Web.
出版日期Conference proceedings 2011
關鍵詞RFID; XML schema; cloud computing; deep Web; probabilistic methods
版次1
doihttps://doi.org/10.1007/978-3-642-20291-9
isbn_softcover978-3-642-20290-2
isbn_ebook978-3-642-20291-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Berlin Heidelberg 2011
The information of publication is updating

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Music Review Classification Enhanced by Semantic Informationic reviews, related semantic music information is also utilized. A heuristic SVM classification algorithm is proposed to build the classifiers. SEMEC makes use of the concept of . and the . to combine the results from different classifiers. Extensive experimental results show that SEMEC is effective and efficient.
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Cleaning Uncertain Streams for Query Improvemente efficiency (i.e. data volume). In this paper, we propose a framework for cleaning uncertain data for query effectiveness and efficiency, which processes high-volume streams in parallel, and append new cleaning rules & queries seamlessly. We implement a prototype for video surveillance application over the architecture.
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CkNN Query Processing over Moving Objects with Uncertain Speeds in Road Networksy of an object being a . of query . is shown to be equivalent to the probability of a special line segment being one of the .-nearest lines from ., which greatly simplifies the probability calculation.
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