On pricing approximate queries

Nowadays, data is being bought and sold in data markets whose prices are usually determined by vendors. In a data market, approximate aggregate queries over big data sets are often expensive due to computing time, machine resources and data prices. Therefore, sometimes consumers cannot obtain query results within preferred deadlines or under budgetary. Approximate aggregate queries with reasonable accuracy is a key technique to address this problem. However, there is no mature theory for pricing approximate aggregate 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. (C) 2018 Elsevier Inc. All rights reserved.