Revenue Maximization for Query Pricing

TL;DR

This paper investigates revenue maximization strategies for query pricing in data markets, proposing new heuristics and empirically evaluating their performance against existing algorithms.

Contribution

It introduces new heuristics for revenue maximization in query pricing and provides a comprehensive empirical evaluation on real-world data.

Findings

Algorithms with the best theoretical bounds are not always the most effective empirically.

Certain heuristics perform consistently well across different valuation distributions.

Fast algorithms can achieve high revenue with good empirical performance.

Abstract

Buying and selling of data online has increased substantially over the last few years. Several frameworks have already been proposed that study query pricing in theory and practice. The key guiding principle in these works is the notion of {\em arbitrage-freeness} where the broker can set different prices for different queries made to the dataset, but must ensure that the pricing function does not provide the buyers with opportunities for arbitrage. However, little is known about revenue maximization aspect of query pricing. In this paper, we study the problem faced by a broker selling access to data with the goal of maximizing her revenue. We show that this problem can be formulated as a revenue maximization problem with single-minded buyers and unlimited supply, for which several approximation algorithms are known. We perform an extensive empirical evaluation of the performance of several pricing algorithms for the query pricing problem on real-world instances. In addition to previously known approximation algorithms, we propose several new heuristics and analyze them both theoretically and experimentally. Our experiments show that algorithms with the best theoretical bounds are not necessarily the best empirically. We identify algorithms and heuristics that are both fast and also provide consistently good performance when valuations are drawn from a variety of distributions.