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A big data service is any data-originated resource that is offered over theInternet. The performance of a big data service depends on the data bought fromthe data collectors. However, the problem of optimal pricing and dataallocation in big data services is not well-studied. In this paper, we proposean auction-based big data market model. We first define the data cost andutility based on the impact of data size on the performance of big dataanalytics, e.g., machine learning algorithms. The big data services areconsidered as digital goods and uniquely characterized with "unlimited supply"compared to conventional goods which are limited. We therefore propose aBayesian profit maximization auction which is truthful, rational, andcomputationally efficient. The optimal service price and data size are obtainedby solving the profit maximization auction. Finally, experimental results on areal-world taxi trip dataset show that our big data market model and auctionmechanism effectively solve the profit maximization problem of the serviceprovider.

Profit Maximization Auction and Data Management in Big Data Markets