Price Sharing for Streaming Data- A Novel Approach for Funding RDF Stream Processing
Author(s): Tobias Grubenmann, Daniele Dell’Aglio, Abraham Bernstein, Dmitry Moor, Sven Seuken
Abstract: RDF Stream Processing (RSP) has proposed solutions to continuously query streams of RDF data. As a result, it is today possible to create complex networks of RSP engines to process streaming data in a distributed and continuous fashion. Indeed, some approaches even allow to distribute the computation across the web. But both producing high-quality data and providing compute power to process it costs money.
The usual approach to financing data on the Web of Data today is that either some sponsor subsidizes it or the consumers are charged. In the stream setting consumers could exploit synergies and, theoretically, share the access and processing fees, should their needs overlap. But what should be the monetary contribution of each consumer when they have varying valuations of the differing outcomes?
In this article, we propose a model for price sharing in the RDF Stream Processing setting. Based on the consumers’ outcome valuations and the pricing of the raw data streams, our algorithm computes utility-maximizing prices different consumers should contribute whilst ensuring that all the participants have no incentive of manipulating the system by providing misinformation about their value, budget, or requested data stream. We show that our algorithm is able to calculate such prices in a reasonable amount of time for up to one thousand simultaneous queries.
Keywords: RDF Streaming Processing; Price Sharing; Equal-Need Sharing