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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Collaborative Streaming: Trust Requirements for Price Sharing
Organization Unit
  • Tobias Grubenmann
  • Daniele Dell'Aglio
  • Abraham Bernstein
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Page Range 3498 - 3505
Event Title 4th Workshop on Real-time & Stream Analytics in Big Data & Stream Data Management
Event Type workshop
Event Location Los Angels, CA, USA
Event Start Date December 10 - 2019
Event End Date December 10 - 2019
Place of Publication Washington DC, USA
Publisher IEEE
Abstract Text Stream Processing (SP) is an important Big Data technology enabling continuous querying of data streams. The stream setting offers the opportunity to exploit synergies and, theoretically, share the access and processing costs between multiple different collaborators. But what should be the monetary contribution of each consumer when they do not trust each other and have varying valuations of the differing outcomes? In this article, we present Collaborative Stream Processing (CSP), a model where the costs, which are set exogenously by providers, are shared between multiple consumers, the collaborators. For this, we identify three important requirements for CSP to establish trust between the collaborators and propose a CSP al- gorithm, ENCSPA, adhering to these requirements. Based on the collaborators’ outcome valuations and the costs of the raw data streams, ENCSPA computes the payment for each collaborator. At the same time, ENCSPA ensures that no collaborator has an incentive to manipulate the system by providing misinformation about her/his value, budget, or time limit. We show that ENCSPA can calculate payments in a reasonable amount of time for up to one thousand collaborators.
Related URLs
Digital Object Identifier 10.1109/BigData47090.2019.9005470
Other Identification Number merlin-id:18780
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