A Sampling-Based Framework for Crowdsourced Select Query with Multiple Predicates

In this paper, we consider the crowdsourced select query with multiple predicates. We find that different predicates have different selectivities. An important problem is to determine a good predicate order. However it is rather hard to obtain an optimal order. To address this problem, we propose a sampling-based framework to find a high-quality order. We devise a minimum random selection method by randomly selecting the predicate sequence. Since minimum random selection randomly selects predicate permutations over predicates, which may bring large cost, we propose a filtering based algorithm to further reduce the cost. We evaluate our method using a real-world dataset. Experimental results indicate that our methods significantly reduce the monetary cost.