Continuously monitoring the correlations of massive discrete streams

Authors:
Yueguo Chen;Wei Wang;Xiaoyong Du;Xiaofang Zhou
Affiliations:
Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, China, Beijing, China;School of Information, Renmin University of China, Beijing, China;School of Information, Renmin University of China, Beijing, China;University of Queensland, Brisbane, Australia
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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Abstract

The problem of monitoring the correlations of discrete streams is to continuously monitor the temporal correlations among massive discrete streams. A temporal correlation of two streams is defined as a tracking behavior, i.e., the most recent pattern of one stream is very similar to a historical pattern of another stream. The challenge is that both the tracking stream and the tracked stream are evolving, which causes the frequent updates of the correlation-ships. The straightforward way of monitoring correlations by brute-force subsequence matching will be very expensive for massive streams. We propose techniques that are able to significantly reduce the number of expensive subsequence matching calls, by continuously pruning and refining the correlated streams. Extensive experiments on the streaming trajectories show the significant performance improvement achieved by the proposed algorithms.