Duration: 41:56

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The past decade has seen tremendous interest in mining of time series and shape datasets, as such data can be found in domains as diverse as entertainment, finance, medicine and astronomy. However, much of this work has focused on toy problems, with a few thousand objects. In recent years, our research group has made an effort to address the problems of classification, clustering, query-by-content, motif discovery, and outlier detection on truly massive datasets, with 100 million-plus objects. In this talk we will summarize our research findings over the last two years, and show that a small set of primitives, shaplets, motifs and discords, allow us to solve essentially all problems in shape/time series data mining with efficient, effective and interpretable results. We will demonstrate the utility of our ideas, with case studies in anthropology, astronomy, entomology, historical manuscript annotation and medicine.