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Titlebook: Database Systems for Advanced Applications; 20th International C Matthias Renz,Cyrus Shahabi,Muhammad Aamir Cheema Conference proceedings 2

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書(shū)目名稱Database Systems for Advanced Applications
副標(biāo)題20th International C
編輯Matthias Renz,Cyrus Shahabi,Muhammad Aamir Cheema
視頻videohttp://file.papertrans.cn/264/263408/263408.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Database Systems for Advanced Applications; 20th International C Matthias Renz,Cyrus Shahabi,Muhammad Aamir Cheema Conference proceedings 2
描述.This two volume set LNCS 9049 and LNCS 9050 constitutes the refereed proceedings of the 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015, held in Hanoi, Vietnam, in April 2015..The 63 full papers presented were carefully reviewed and selected from a total of 287 submissions. The papers cover the following topics: data mining; data streams and time series; database storage and index; spatio-temporal data; modern computing platform; social networks; information integration and data quality; information retrieval and summarization; security and privacy; outlier and imbalanced data analysis; probabilistic and uncertain data; query processing..
出版日期Conference proceedings 2015
關(guān)鍵詞Web information systems; big data; collaborative filtering; crowdsourcing; data management systems; data
版次1
doihttps://doi.org/10.1007/978-3-319-18123-3
isbn_softcover978-3-319-18122-6
isbn_ebook978-3-319-18123-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
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Changes in Government Goods and the Tax CodeOT2LD has been evaluated on 15 real-world data sets. The experimental results have shown that our method outperforms some other existing methods including SMOTE, Borderline-SMOTE, ADASYN, and MWMOTE, in terms of G-mean and F-measure.
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A Synthetic Minority Oversampling Method Based on Local Densities in Low-Dimensional Space for ImbalOT2LD has been evaluated on 15 real-world data sets. The experimental results have shown that our method outperforms some other existing methods including SMOTE, Borderline-SMOTE, ADASYN, and MWMOTE, in terms of G-mean and F-measure.
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Tracing Errors in Probabilistic Databases Based on the Bayesian Networkdels, we then define a notion of blame for ranking candidate errors. Next, we provide an efficient method for computing the degree of blame for each candidate error based on the probabilistic inference upon the ABN. Experimental results show the effectiveness and efficiency of our method.
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