On pricing approximate queries

In this paper, we first propose a novel theoretical framework to support pricing approximate aggregate queries. By using a sampling technique to achieve an error-bounded approximate answer over data queries, a transforming function is provided to convert the original pricing function to the one that supports approximate aggregate queries. We further adopt a statistical method to estimate consumers' payments. The proposed transform function preserves the arbitrage free property. We implement a prototype system and through comparing our framework with two benchmark pricing methods, experiments show that our pricing method is much suitable for pricing approximate aggregate queries.