Crowdsourcing for top-K query processing over uncertain data

Both social media and sensing infrastructures are producing an unprecedented mass of data characterized by their uncertain nature, due to either the noise inherent in sensors or the imprecision of human contributions. Therefore query processing over uncertain data has become an active research field. In the well-known class of applications commonly referred to as top-K queries, the objective is to find the best K objects matching the user's information need, formulated as a scoring function over the objects' attribute values. If both the data and the scoring function are deterministic, the best K objects can be univocally determined and totally ordered so as to produce a single ranked result set (as long as ties are broken by some deterministic rule). However, in application scenarios involving uncertain data and fuzzy information needs, this does not hold: when either the attribute values or the scoring function are nondeterministic, there may be no consensus on a single ordering, but rather a space of possible orderings. To determine the correct ordering, one needs to acquire additional information so as to reduce the amount of uncertainty associated with the queried data and consequently the number of orderings in such a space. An emerging trend in data processing is crowdsourcing, defined as the systematic engagement of humans in the resolution of tasks through online distributed work. Our approach combines human and automatic computation in order to solve complex problems: when data ambiguity can be resolved by human judgment, crowdsourcing becomes a viable tool for converging towards a unique or at least less uncertain query result. The goal of this paper is to define and compare task selection policies for uncertainty reduction via crowdsourcing, with emphasis on the case of top-K queries.