Answering Skyline Queries Over Incomplete Data With Crowdsourcing
Due to the pervasiveness of incomplete data, incomplete data queries are vital in a large number of real life scenarios. Current models and approaches for incomplete data queries mainly rely on the machine power. In this paper, we study the problem of <italic>skyline queries over incomplete data with crowdsourcing</italic> We propose a novel query framework, termed as <inline formula><tex math notation="LaTeX"\sf BayesCrowd}/tex math><alternatives><mml:math><mml:mi mathvariant="sans serif">BayesCrowd</mml:mi>/mml:math><inline graphic xlink:href="gao ieq1 2946798.gif"/alternatives>/inline formula> which takes into account the data correlation using the Bayesian network. We leverage the typical <italic>c table</italic> model on incomplete data to represent objects. Considering budget and latency constraints, we present a suite of effective task selection strategies. Moreover, we introduce a <italic>marginal utility</italic> function to measure the benefit of crowdsourcing one task. In particular, the probability computation of each object being an answer object is at least as hard as #SAT problem. To this end, we propose an <italic>adaptive</italic> DPLL (i.e. Davis Putnam Logemann Loveland) algorithm to speed up the computation. Extensive experiments using both real and synthetic data sets confirm the superiority of <inline formula><tex math notation="LaTeX"\sf BayesCrowd}/tex math><alternatives><mml:math><mml:mi mathvariant="sans serif">BayesCrowd</mml:mi>/mml:math><inline graphic xlink:href="gao ieq2 2946798.gif"/alternatives>/inline formula> to the state of the art method, in terms of execution time, monetary cost, and latency minimization.
IEEE Transactions on Knowledge and Data Engineering