Efficient Time Series Data Classification and Compression in Distributed Monitoring

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.