Crowd sourcing for Top-K Query Processing over Uncertain Data

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Crowdsourcing for Top-K Query Processing over Uncertain Data

Querying uncertain data has become a prominent application due to the proliferation of user-generated content from social media and of data streams from sensors. When data ambiguity cannot be reduced algorithmically, crowdsourcing proves a viable approach, which consists in posting tasks to humans and harnessing their judgment for improving the confidence about data values or relationships. This paper tackles the problem of processing top-K queries over uncertain data with the help of crowdsourcing to quickly converging to the real ordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and contrasted on both synthetic and real datasets, with the aim of minimizing the crowd interactions necessary to find the real ordering of the result set.

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