Visual analysis of frequent patterns in large time series

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. To find these motifs, we use an advanced temporal data mining algorithm. Since our algorithm usually finds hundreds of motifs, we need to analyze and access the discovered motifs. For this purpose, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. We have applied and evaluated our methods using two real-world data sets: data center cooling and oil well production.