Query-based data pricing

Abstract

Data is increasingly being bought and sold online, and Webbased marketplace services have emerged to facilitate these activities. However, current mechanisms for pricing data are very simple: buyers can choose only from a set of explicit views, each with a specific price. In this paper, we propose a framework for pricing data on the Internet that, given the price of a few views, allows the price of any query to be derived automatically. We call this capability "querybased pricing." We first identify two important properties that the pricing function must satisfy, called arbitragefree and discount-free. Then, we prove that there exists a unique function that satisfies these properties and extends the seller's explicit prices to all queries. When both the views and the query are Unions of Conjunctive Queries, the complexity of computing the price is high. To ensure tractability, we restrict the explicit prices to be defined only on selection views (which is the common practice today). We give an algorithm with polynomial time data complexity for computing the price of any chain query by reducing the problem to network flow. Furthermore, we completely characterize the class of Conjunctive Queries without selfjoins that have PTIME data complexity (this class is slightly larger than chain queries), and prove that pricing all other queries is NP-complete, thus establishing a dichotomy on the complexity of the pricing problem when all views are selection queries.

Marketplaces for Digital Data: Quo Vadis?

“…Furthermore, pay-per-use models have not (yet) reached the level of sophistication necessary to prevent arbitrage exploitation. To find technical and policy amendments, research has been conducted (Balazinska, Howe, & Suciu, 2011;Koutris, Upadhyaya, Balazinska, Howe, & Suciu, 2012). Customers favor simpler pricing models as well and are not satisfied with granular pricing models that restrict unfocused data exploration.…”

QueryMarket demonstration

“…We use a lemma [8,Lemma 3.1] to check that prices a seller sets are consistent. The lemma uses the notion of fully-covered columns in a relation that are columns where the selections on all values in the column's domain have been priced.…”

“…In prior work [8], we studied the theoretical foundations of assigning a price to an arbitrary query by extrapolating the prices that sellers set on views consisting of selection queries on a single attribute of a relation (note that all the tuples that satisfy the selection predicate are returned and not just the column being selected). We called this capability "query-based pricing."…”

“…QueryMarket is a cloud-based service for data publishers to sell data and ad-hoc views over that data. QueryMarket allows sellers to assign prices to views defined by selection predicates of the form σa=α and allows buyers to purchase any query in a non-trivial subset of full conjunctive queries without self-joins [8] called the "Generalized Chain Queries" (Section 5), a set that includes all path join queries, star join queries, and their combination.…”

Coccus

“…This solution focuses on cost-recovery and truthfulness at the expense of efficiency. The work in [14] is a theoretic approach that computes the price of a query based on given prices for a set of views. This is orthogonal to [13], in which query execution cost and, building and maintenance cost for data structures is computed directly based on the cost of the cloud infrastructure involved in these operations.…”