  Data fusion has played an important role in data mining because high-quality
data is required in a lot of applications. As on-line data may be out-of-date
and errors in the data may propagate with copying and referring between
sources, it is hard to achieve satisfying results with merely applying existing
data fusion methods to fuse Web data. In this paper, we make use of the crowd
to achieve high-quality data fusion result. We design a framework selecting a
set of tasks to ask crowds in order to improve the confidence of data. Since
data are correlated and crowds may provide incorrect answers, how to select a
proper set of tasks to ask the crowd is a very challenging problem. In this
paper, we design an approximation solution to address this challenge since we
prove that the problem is at NP-hard. To further improve the efficiency, we
design a pruning strategy and a preprocessing method, which effectively improve
the performance of the proposed approximation solution. Furthermore, we find
that under certain scenarios, we are not interested in all the facts, but only
a specific set of facts. Thus, for these specific scenarios, we also develop
another approximation solution which is much faster than the general
approximation solution. We verify the solutions with extensive experiments on a
real crowdsourcing platform.
