Download Database Systems for Advanced Applications: 21st by Shamkant B. Navathe, Weili Wu, Shashi Shekhar, Xiaoyong Du, PDF

By Shamkant B. Navathe, Weili Wu, Shashi Shekhar, Xiaoyong Du, X. Sean Wang, Hui Xiong

This quantity set LNCS 9642 and LNCS 9643 constitutes the refereed lawsuits of the twenty first overseas convention on Database platforms for complicated functions, DASFAA 2016, held in Dallas, TX, united states, in April 2016.

The sixty one complete papers offered have been rigorously reviewed and chosen from a complete of 183 submissions. The papers conceal the subsequent themes: crowdsourcing, facts caliber, entity identity, info mining and computing device studying, suggestion, semantics computing and information base, textual info, social networks, advanced queries, similarity computing, graph databases, and miscellaneous, complicated applications.

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Read or Download Database Systems for Advanced Applications: 21st International Conference, DASFAA 2016, Dallas, TX, USA, April 16-19, 2016, Proceedings, Part I PDF

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Additional resources for Database Systems for Advanced Applications: 21st International Conference, DASFAA 2016, Dallas, TX, USA, April 16-19, 2016, Proceedings, Part I

Sample text

A general framework of iterative crowdsourcing result inference. Once workers complete their tasks, the Result Inference component aggregates the results from these n workers and generates the inference result. Then, the Iterative Decision component checks whether the inference result can be further improved: if the result is good enough, the iterative process will be terminated; otherwise it will ask another n workers and repeat the above three steps. In other words, the Iterative Decision component decides whether the iterative improvement process should be terminated or not.

1203–1212 (2012) 32 W. Chen et al. 20. : Learning from crowds. JMLR 11, 1297–1322 (2010) 21. : Pushing the boundaries of crowd-enabled databases with query-driven schema expansion. VLDB 5(6), 538–549 (2012) 22. Sojump. com 23. : Crowdsourced enumeration queries. In: ICDE, pp. 673–684 (2013) 24. : Max algorithms in crowdsourcing environments. In: WWW, pp. 989–998 (2012) 25. : Serf and turf: crowdturfing for fun and profit. In: WWW, pp. 679–688 (2012) 26. : Leveraging transitive relations for crowdsourced joins.

Error rate 7 Conclusions We explore a new approach to processing crowdsourced queries on microblogs. Our goal is to minimize the cost of the crowdsourced query processing while the aggregated answer satisfies a specified accuracy threshold. We develop a new query diffusion model and formulate the problem of Crowdseed Selection. However, we prove that this problem is NP-hard and, given a crowdseed set S, the computation of query diffusion is #P-hard. We then develop a greedy algorithm to tackle the problem and a sampling algorithm to compute the query diffusion of the selected crowdseed set.

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