Crowdsourcing For Top-K Query Processing Over Uncertain Data Base Paper

Abstract

Uncertain data are inherent in some important application. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. Crowdsourcing has emerged as an effective way to perform tasks that are easy for humans but remain difficult for computers. When data uncertainty cannot be reduced algorithmically, crowdsourcing proves a feasible method, which consists in posting tasks to humans and attaching their decision for improving the confidence about data values or relations. This project tackles the problem of processing Top-K queries over uncertain data with the help of crowdsourcing for quickly converging to the real ordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and distinguished on both artificial and real data sets, with the purpose of minimizing the crowd relations necessary to find the real ordering of the result set. Keywords—User/Machine Systems, Query processing.