Answering Skyline Queries over Incomplete Data with Crowdsourcing(Extended Abstract)

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 skyline queries over incomplete data with crowdsourcing. We propose a novel query framework, termed as BayesCrowd, on top of Bayesian network and the typical c-table model on incomplete data. Considering budget and latency constraints, we present a suite of effective task selection strategies. In particular, since the probability computation of each object being an answer object is at least as hard as #SAT problem, we propose an adaptive 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 BayesCrowd to the state-of-the-art method.