Promotor: J.N. Kok, Co-Promotor: A.J. Knobbe
|Links||Thesis in Leiden Repository|
Today, virtually everything, from natural phenomena to complex artificial and physical systems, can be measured and the resulting information collected, stored and analyzed in order to gain new insight. This thesis shows how complex systems often exhibit diverse behavior at different temporal scales, and that data mining methods should be able to cope with the multiple resolutions (scales) at the same time in order to fully understand the data at hand and extract useful information from it. Under these assumptions, we introduce novel data mining and visualization methods for large time series data collected from complex physical systems. In particular, we focus on three fundamental problems: the detection of multi-scale patterns, the recognition of recurrent events, and the interactive visualization of massive time series data. We evaluate our methods on a real-world scenario provided by InfraWatch, a Structural Health Monitoring project centered around the management and analysis of data collected by a large sensor network deployed on a Dutch highway bridge. The application of our methods resulted in the identification of the relevant scales of analysis in the InfraWatch data (and other datasets), the detection of the different recurring motifs and the visualization of terabytes of time series data interactively.