Dynamic source weight computation for truth inference over data streams

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.