Title: Double Auction for Querying the Web of Data
Abstract: Currently, the Web of Data (WoD) suffers from a lack of financial incentives for data providers. In this paper, we address this issue, by proposing a double auction to efficiently allocate answers (from data providers) to queries in the WoD. However, our domain exhibits a number of complicating features. Most importantly, before executing a particular query, the market mechanism only has estimates regarding what result can be expected. Thus, in contrast to other domains, the allocation rule as well as the pricing rule of the auction must operate based on value and cost estimates. New challenges arise from this setting; in particular the auction's participation constraint can no longer be guaranteed to be satisfied. We propose three payment correction rules to address this issue, and compare the efficiency of the resulting payment rules via a computational Bayes-Nash equilibrium analysis.