Pricing for Data Markets

The increasing accessability and speed of Internet has enabled emergence of an online market where data providers publish their valuable information and from which customers subscribe and query data of all types. This virtual space in which the supply-demand forces exist and the exchange of information is incentivized by monetary gain is referred to as the data market (e.g. Windows Azure Marketplace DataMarket). The commonly observed pricing models for information goods and data markets are per unit pricing (metered usage pricing) and site licensing (unlimited usage pricing). There hasn’t been considerable work on determining a pricing model and parameters for profit maximization for pricing datasets which has great relevance and applicability in the datamarket. The paper determines the optimal prices of datasets for profit maximization from an Economics framework both for unit and step pricing scheme. This paper validates a pricing model against properties such as arbitrage & competition. The paper presents necessary and sufficient conditions for a valid pricing scheme and provides an O(n) algorithm to validate multi-step pricing models of existing datasets in the datamarket. The paper also formulates the problem of computing the closest valid pricing model(under L1 Norm) as linear program for invalid pricing models.