Data is a modern commodity. Yet the pricing models in use on electronic data markets either focus on the usage of computing resources, or are proprietary, opaque, most likely ad hoc, and not conducive of a healthy commodity market dynamics. In this paper we propose a generic data pricing model that is based on minimal provenance, i.e. minimal sets of tuples contributing to the result of a query. We show that the proposed model fulfills desirable properties such as contribution mono-tonicity, bounded-price and contribution arbitrage-freedom. We present a baseline algorithm to compute the exact price of a query based on our pricing model. We show that the problem is NP-hard. We therefore devise, present and compare several heuristics. We conduct a comprehensive experimental study to show their effectiveness and efficiency.
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