Publication
CIKM 2015
Conference paper

Fast distributed correlation discovery over streaming time-series data

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Abstract

The dramatic rise of time-series data in a variety of contexts, such as social networks, mobile sensing, data centre monitoring, etc., has fuelled interest in obtaining real-time insights from such data using distributed stream processing systems. One such extremely valuable insight is the discovery of correlations in real-time from large-scale time-series data. A key challenge in discovering correlations is that the number of time-series pairs that have to be analyzed grows quadratically in the number of time-series, giving rise to a quadratic increase in both computation cost and communication cost between the cluster nodes in a distributed environment. To tackle the challenge, we propose a framework called AEGIS. AEGIS exploits well-established statistical properties to dramatically prune the number of time-series pairs that have to be evaluated for detecting interesting correlations. Our extensive experimental evaluations on real and synthetic datasets establish the efficacy of AEGIS over baselines.

Date

17 Oct 2015

Publication

CIKM 2015

Authors

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