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  4. Fast Distributed Correlation Discovery Over Streaming Time-Series Data
 
conference paper not in proceedings

Fast Distributed Correlation Discovery Over Streaming Time-Series Data

Guo, Tian  
•
Sathe, Saket  
•
Aberer, Karl  
2015
ACM International Conference on Information and Knowledge Management (CIKM 2015)

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.

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Type
conference paper not in proceedings
DOI
10.1145/2806416.2806440
Author(s)
Guo, Tian  
Sathe, Saket  
Aberer, Karl  
Date Issued

2015

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LSIR  
Event nameEvent placeEvent date
ACM International Conference on Information and Knowledge Management (CIKM 2015)

Melbourne, Australia

Oct 19-23, 2015

Available on Infoscience
August 3, 2015
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/116798
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