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Dynamic source weight computation for truth inference over data streams

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Yang, Y, Bai, Q ORCID: 0000-0003-1214-6317 and Liu, Q 2019 , 'Dynamic source weight computation for truth inference over data streams', in AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems , International Foundation for Autonomous Agents and MultiAgent Systems (IFAAMAS), USA, pp. 277-285 , doi: 10.5555/3306127.3331704.

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Abstract

Truth inference, a method that resolves conflicts among multi-agent data, has been widely studied in the field of AI. Most existing truth inference methods use iterative approaches to achieve high accuracy, but are inefficient to infer object truths over data streams. The methods developed for streaming data can achieve high efficiency but suffer from low accuracy. In this paper, we propose a novel truth inference method, Dynamic Source Weight Computation truth inference (DSWC), that can work with a wide range of iterative-based truth inference methods to dynamically compute source weights over data streams. Specifically, we use Taylor expansion to analyze the unit error of object truths inferred by source weights computed at a previous timestamp. If the source weight at present is predicted to be able to limit the error under a threshold, we use the source weights computed previously to approximate object truths at present to avoid the expensive source weight computation step. Compared with the existing work, the proposed method is more effective in predicting source weights and can be applied to a wider range of applications. Experimental results based on four real-world datasets demonstrate that DSWC is both accurate and efficient for truth inference over data streams.

Item Type: Conference Publication
Authors/Creators:Yang, Y and Bai, Q and Liu, Q
Keywords: truth discovery, knowledge discovery, trust
Journal or Publication Title: AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems
Publisher: International Foundation for Autonomous Agents and MultiAgent Systems (IFAAMAS)
DOI / ID Number: 10.5555/3306127.3331704
Copyright Information:

Copyright 2019 2019 International Foundation for Autonomous Agents andMultiagent Systems

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