  Incentivized by the enormous economic profits, the data marketplace platform
has been proliferated recently. In this paper, we consider the data marketplace
setting where a data shopper would like to buy data instances from the data
marketplace for correlation analysis of certain attributes. We assume that the
data in the marketplace is dirty and not free. The goal is to find the data
instances from a large number of datasets in the marketplace whose join result
not only is of high-quality and rich join informativeness, but also delivers
the best correlation between the requested attributes. To achieve this goal, we
design DANCE, a middleware that provides the desired data acquisition service.
DANCE consists of two phases: (1) In the off-line phase, it constructs a
two-layer join graph from samples. The join graph consists of the information
of the datasets in the marketplace at both schema and instance levels; (2) In
the online phase, it searches for the data instances that satisfy the
constraints of data quality, budget, and join informativeness, while maximize
the correlation of source and target attribute sets. We prove that the
complexity of the search problem is NP-hard, and design a heuristic algorithm
based on Markov chain Monte Carlo (MCMC). Experiment results on two benchmark
datasets demonstrate the efficiency and effectiveness of our heuristic data
acquisition algorithm.
