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 court cases of the twenty first overseas convention on Database structures for complicated purposes, 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 disguise the next themes: crowdsourcing, facts caliber, entity identity, info mining and desktop studying, suggestion, semantics computing and information base, textual facts, social networks, complicated queries, similarity computing, graph databases, and miscellaneous, complicated applications.
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
SA ⊆ V ). e. |sA | ≥ τ +1 2 ). Thus, the correctness probability of the majority voting rule is given by p(f (V )) = P r(|sA | ≥ τ +1 )= 2 τ P r(|sA | = k) k= τ +1 2 τ = (1 − a(uj )) a(ui ) k= τ +1 2 sA ∈FK ui ∈sA (2) uj ∈s / A where Fk comprises all possible combinations of users giving accurate votes for sA with size k. We note that 1 − p(f (V )) is a cumulative Poisson binomial distribution, since the accurate probability of each vote vi is diﬀerent. e. E[p(f (V ))]). Then, the expected correctness of the majority voting rule is given by 22 W.
Algorithms In this section, we show how to tackle the complexity of the problem of Crowdseed Selection. We ﬁrst deﬁne an objective function based on the selected crowdseed set S and then propose a greedy algorithm in order to maximize the function. We aim to select a crowdseed set S such that τ feedback answers are obtained from the users. Thus, we set the constraint of the crowdseed set S at δ(G|S) ≥ τ c(ui ) such that δ(G|S) ≥ τ , and formulate the objective function as: minS ui ∈S where τ is the expected query diﬀusion size.
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: NIPS, pp. 1772–1780 (2012) 30. : Socialtransfer: transferring social knowledge for cold-start cowdsourcing. In CIKM, pp. 779–788 (2014) 31. : Crowdseed: query processing on microblogs. In: EDBT, pp. 729–732 (2013) 32. : Crowd-selection query processing in crowdsourcing databases: a task-driven approach.