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 structures for complicated functions, DASFAA 2016, held in Dallas, TX, united states, in April 2016.
The sixty one complete papers awarded have been rigorously reviewed and chosen from a complete of 183 submissions. The papers conceal the subsequent subject matters: crowdsourcing, info caliber, entity identity, facts mining and desktop studying, advice, semantics computing and information base, textual information, social networks, advanced queries, similarity computing, graph databases, and miscellaneous, complex applications.
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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
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. In: SIGMOD, pp. 229–240 (2013) 27. : A comparative study of team formation in social networks. A. ) DASFAA 2015. LNCS, vol. 9049, pp. 389–404. Springer, Heidelberg (2015) 28. : The multidimensional wisdom of crowds. In: NIPS, pp. 2424–2432 (2010) 29. : Semi-crowdsourced clustering: generalizing crowd labeling by robust distance metric learning.
In the second step, the SeedMiner returns the crowdseed and we issue the query to the crowdseed in the third step. We collect answers incrementally from microblogs in the fourth step. Finally, we fuse the feedback and deliver the correct solution online using MVoting. In the system, we develop a lottery based incentive mechanism to encourage users to answer and “retweet” the crowdsourced query. We employ the build-in lottery function in the third party  to our system. Contributions. We tackle the problem of crowdsourced query processing on microblogs.
E. e. e. e. δ(G|S) ≥ τ ). However, given a crowdseed set S, the computation of its expected query diﬀusion is #P-hard. Thus, we propose a sampling algorithm to estimate the expected query diﬀusion eﬃciently and eﬀectively. As G = (V, E, P ) is a probabilistic graph, the samples can be obtained by ﬂipping the edges according to the probabilities in P. , Gk = (V, Ek ). Thus, the expected query diﬀusion can be obtained by taking the average of these sample graphs and is given by δ(G|S) = k i=1 δ(Gi |S) k (11) where the sample Gi is a deterministic graph.
Database Systems for Advanced Applications: 21st International Conference, DASFAA 2016, Dallas, TX, USA, April 16-19, 2016, Proceedings, Part I by Shamkant B. Navathe, Weili Wu, Shashi Shekhar, Xiaoyong Du, X. Sean Wang, Hui Xiong