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Titlebook: Data Warehousing and Knowledge Discovery; 16th International C Ladjel Bellatreche,Mukesh K. Mohania Conference proceedings 2014 The Editor(

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书目名称Data Warehousing and Knowledge Discovery
副标题16th International C
编辑Ladjel Bellatreche,Mukesh K. Mohania
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Data Warehousing and Knowledge Discovery; 16th International C Ladjel Bellatreche,Mukesh K. Mohania Conference proceedings 2014 The Editor(
描述This book constitutes the refereed proceedings of the 16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014 held in Munich, Germany, September 2014, in conjunction with DEXA 2014. The 34 revised full papers and 8 short papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on modeling and ETL; ontology-based data warehouses; advanced data warehouses and OLAP; uncertainty; preferences and recommendation; query performance and HPC; cube & OLAP; optimization; classification; social networks and recommendation systems; knowledge data discovery; industrial applications; mining and processing data stream; mining and similarity.
出版日期Conference proceedings 2014
关键词MapReduce; data integration; data management systems; data mining; data streams; database query processin
版次1
doihttps://doi.org/10.1007/978-3-319-10160-6
isbn_softcover978-3-319-10159-0
isbn_ebook978-3-319-10160-6Series 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
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Discovering Community Preference Influence Network by Social Network Opinion Posts Mininghrough opinion mining of friendship networks. OBIN mines opinions using extended OpinionMiner that considers multiple posts and relationships (influences) between users. Approach used includes frequent pattern mining algorithm for determining community (positive or negative) preferences for a given
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Computer Assisted Orthopedic Surgeryithms use simple safe and correct strategies, but are not optimal in the sense that they maximize the join service rate. In this paper we analyze strategies for selecting an appropriate lookup element, particularly for skewed stream data. We show that a good selection strategy can improve the perfor
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Comparison of the Iterative Methods,tation of formulas an extended drill-down operator is defined, which is capable to expand an indicator into its components, enabling a novel mode of data exploration. Effectiveness and efficiency are briefly discussed on a prototype introduced as a proof-of concept.
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Ashish Sharma,Ambuja Salgaonkar . the user has a spectrum of options according to his . on . and .. Accordingly, the mined preference model is ., in the sense that it is capable to predict, given two new objects u and v, the degree of preference the user would assign to these objects. The efficiency of FuzzyPrefMiner is analysed through a series of experiments on real datasets.
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