Towards Query Pricing on Incomplete Data

Xiaoye Miao, Yunjun Gao, Lu Chen, Huanhuan Peng, Jianwei Yin, Qing Li

Abstract: Data have significant economic or social value in many application fields including science, business, governance, etc. This naturally leads to the emergence of many data markets such as GBDEx and YoueData. As a result, the data trade through data markets has started to receive attentions from both industry and academia. During the data buying and selling, how to price the data is an indispensable problem. However, pricing incomplete data is more challenging, even though incomplete data exist pervasively in a vast lot of real-life scenarios. In this paper, we attempt to explore the <i>pricing problem for queries over incomplete data</i> . We propose a sophisticated pricing mechanism, termed as <inline-formula><tex-math notation="LaTeX">${\sf iDBPricer}$</tex-math></inline-formula> , which takes a series of essential factors into consideration, including the <i>data contribution/usage</i> , <i>data completeness</i> , and <i>query quality</i> . We present two novel price functions, namely, the usage, and completeness-aware price function ( <i>UCA price</i> for short) and the quality, usage, and completeness-aware price function ( <i>QUCA price</i> for short). Moreover, we develop efficient algorithms for deriving the query prices. Extensive experiments using both real and benchmark datasets demonstrate <inline-formula><tex-math notation="LaTeX">${\sf iDBPricer}$</tex-math></inline-formula> is of excellent performance in terms of effectiveness and scalability, compared with the state-of-the-art price functions.