Data as a commodity has always been purchased and sold. Recently, web services that are data marketplaces have emerged that match data buyers with data sellers. So far there are no guidelines how to price queries against a database. We consider the recently proposed query-based pricing framework of Koutris et al. [13] and ask the question of computing optimal input prices in this framework by f...

We show that computing the revenue-optimal deterministic auction in unit-demand single-buyer Bayesian settings, i.e. the optimal itempricing, is computationally hard even in single-item settings where the buyer’s value distribution is a sum of independently distributed attributes, or multi-item settings where the buyer’s values for the items are independent. We also show that it is intractable ...

We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on observations at single historical price? The optimizes over (potentially randomized) policies to maximize worst-case ratio of revenue it can garner compared an oracle with full knowledge distribution values, when latter is only assumed belong broad non-parametric set. In pa...

We study optimal service pricing in server farms where customers arrive according to a renewal process and have independent identical ($i.i.d.$) exponential times $i.i.d.$ valuations of the service. The provider charges time varying fee aiming at maximizing its revenue rate. that find free servers fees lesser than their valuation join for else they leave without waiting. consider both finite in...

Abstract Reverse pricing is a market mechanism under which a consumer’s bid for a product leads to a sale if the bid exceeds a hidden acceptance threshold the seller has set in advance. The seller faces two key decisions in designing such a mechanism: First, he must decide where in the process to collect the revenue—that is, whether to commit to a minimum markup above cost (and thus define the ...