Download Database Systems for Advanced Applications: 18th by Peter Apers (auth.), Weiyi Meng, Ling Feng, Stéphane PDF

By Peter Apers (auth.), Weiyi Meng, Ling Feng, Stéphane Bressan, Werner Winiwarter, Wei Song (eds.)

This quantity set LNCS 7825 and LNCS 7826 constitutes the refereed lawsuits of the 18th overseas convention on Database structures for complicated functions, DASFAA 2013, held in Wuhan, China, in April 2013. The fifty one revised complete papers and 10 brief papers provided including 2 invited keynote talks, 1 invited paper, three business papers, nine demo shows, four tutorials and 1 panel paper have been conscientiously reviewed and chosen from a complete of 227 submissions. the themes lined partly 1 are social networks; question processing; nearest neighbor seek; index; question research; XML facts administration; privateness defense; and unsure facts administration; and partly 2: graph information administration; actual layout; wisdom administration; temporal information administration; social networks; question processing; info mining; purposes; and database applications.

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Extra info for Database Systems for Advanced Applications: 18th International Conference, DASFAA 2013, Wuhan, China, April 22-25, 2013. Proceedings, Part I

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We use the score obtained for each answer as its quality score. A Score(ui , uj , qk ) = RankSV M (ui , uj , qk ) where RankSV M (ui , uj , qk ) describes the ranking score of uj ’s answer for ui ’s question qk . The accuracy of ranked answer approach is mainly decided by the training data set and the features used. We will discuss this in section 5. 3 Information Retrieval Approach (Sim) Traditionally, cosine similarity value is used in document retrieval and search. Although we believe that this is not a very good measure for answer quality, we use this to demonstrate the effect of answer content information for CQA data sets.

That is, A Quality(ui , uj ) = |QA(ui ,uj )| j=1 N A Score(ui , uj , qk ) |QA(ui , uj )| where A Quality(ui, uj ) describes the answer quality (of an edge from ui to uj ) and |QA(ui , uj )| is the number of ui ’s questions answered by uj . Usually, if A Quality(ui, uj ) is high, user uj has been able to answer user ui ’s questions well. In addition to answer quality, the fraction of ui ’s questions answered by uj captures whether or not user uj is familiar with user ui ’s questions. Hence, uj ’s quality of answers need to be tempered by the fraction of ui ’s questions answered by uj and is calculated as Q F actor(ui , uj ) = |QA(ui , uj )| |Ques(ui )| where |QA(ui , uj )| is the number of ui ’s questions answered by uj and |Ques(uj )| is the total number of user ui ’s questions.

Of the International Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 404–411 (2004) 16. : Making sense of Cronbach’s alpha. International Journal of Medical Education 2, 245–246 (2011) 17. : A Latent Topic Model for Complete Entity Resolution. In: Proc. of the International Conference on Data Engineering, ICDE (2009) 18. : Social Influence Analysis in Large-scale Networks. In: Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, SIGKDD, pp.

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