Quantitative analysis of temporal patterns in loosely coupled active measurement results

Abstract

With constantly increasing complexity of active measurement methods, the issue of processing measurement results becomes important. Similarly to traditional pattern discovery, temporal patterns found in active measurement samples should be provided effective storage and means to compare to other samples. Traditional time series data mining is not applicable to temporal patterns in active measurement time series. This paper proposes a pattern discovery method based on unique features of active measurement results. The method is implemented in form of a database and is used in the paper to verify the proposed method. © Springer-Verlag Berlin Heidelberg 2007.