Price-Optimal Querying with Data APIs.

Data is increasingly being purchased online in data markets and REST APIs have emerged as a favored method to acquire such data. Typically, sellers charge buyers based on how much data they purchase. In many scenarios, buyers need to make repeated calls to the seller's API. The challenge is then for buyers to keep track of the data they purchase and avoid purchasing the same data twice. In this paper, we propose lightweight modifications to data APIs to achieve optimal history-aware pricing so that buyers are only charged once for data that they have purchased and that has not been updated. The key idea behind our approach is the notion of refunds: buyers buy data as needed but have the ability to ask for refunds of data that they had already purchased before. We show that our techniques can provide significant data cost savings while reducing overheads by two orders of magnitude as compared to the state-of-the-art competing approaches.