Efficient time series data classification and compression in distributed monitoring

Authors:
Sheng Di;Hai Jin;Shengli Li;Jing Tie;Ling Chen
Affiliations:
Cluster and Grid Computing Lab, Services Computing Technology and System Lab, Huazhong University of Science and Technology, Wuhan, China;Cluster and Grid Computing Lab, Services Computing Technology and System Lab, Huazhong University of Science and Technology, Wuhan, China;Cluster and Grid Computing Lab, Services Computing Technology and System Lab, Huazhong University of Science and Technology, Wuhan, China;Department of Computer Science, University of Chicago, Chicago;Cluster and Grid Computing Lab, Services Computing Technology and System Lab, Huazhong University of Science and Technology, Wuhan, China
Venue:
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Year:
2007

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Abstract

As a key issue in distributed monitoring, time series data are a series of values collected in terms of sequential time stamps. Requesting them is one of the most frequent requests in a distributed monitoring system. However, the large scale of these data users request may not only cause heavy loads to the clients, but also cost long transmission time. In order to solve the problem, we design an efficient two-step method: first classify various sets of time series according to their sizes, and then compress the time series with relatively large size by appropriate compression algorithms. This two-step approach is able to reduce the users' response time after requesting the monitoring data, and the compression effects of the algorithms designed are satisfactory.