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Collaborative Streaming: Trust Requirements for Price Sharing


Grubenmann, Tobias; Dell'Aglio, Daniele; Bernstein, Abraham (2019). Collaborative Streaming: Trust Requirements for Price Sharing. In: 4th Workshop on Real-time & Stream Analytics in Big Data & Stream Data Management, Los Angels, CA, USA, 10 December 2019 - 10 December 2019, 3498-3505.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Physical Sciences > Computer Networks and Communications
Physical Sciences > Information Systems
Social Sciences & Humanities > Information Systems and Management
Language:English
Event End Date:10 December 2019
Deposited On:09 Mar 2020 10:03
Last Modified:13 May 2020 23:44
Publisher:IEEE
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/BigData47090.2019.9005470
Related URLs:https://workshop.euranova.eu/bigdata19.html (Organisation)
Other Identification Number:merlin-id:18780

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