Efficient Time Series Data Classification and Compression in Distributed Monitoring

Abstract

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. Finally, we test our method, and the results show that: 1) This two-step approach is really able to reduce the users' response time after requesting the monitoring data.2) The compression effects of the algorithms designed by us are satisfactory.

Compression of time series by extracting major extrema

“…Researchers have studied series compression in the context of many different applications (Box, Jenkins, and Reinsel 2008;Montgomery, Jennings, and Kulahci 2008;Cryer and Chan 2009), from the analysis of financial data (Fu, Chung, Luk, and Ng 2008;Dorr and Denton 2009) to distributed monitoring systems (Di, Jin, Li, Tie, and Chen 2007) and manufacturing applications (Eruhimov, Martyanov, Raulefs, and Tuv 2006). In particular, they have considered various feature sets for compressing series and measuring similarity between them.…”