A disk-aware algorithm for time series motif discovery

Data Mining and Knowledge Discovery
Abdullah MueenNima Bigdely-Shamlo

Abstract

Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.

References

Aug 3, 2006·Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism·Bojana StefanovicAfonso C Silva
Aug 29, 2006·Artificial Intelligence in Medicine·Florence DuchêneVincent Rialle
Nov 9, 2007·Journal of Biomedical Informatics·Thomas GuyetMichel Dojat
Jul 12, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Edgar ChavezGonzalo Navarro
Sep 13, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Antonio TorralbaWilliam T Freeman
Nov 8, 2008·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Nima Bigdely-ShamloScott Makeig
Jan 1, 2009·Proceedings of the ... SIAM International Conference on Data Mining·Abdullah MueenBrandon Westover

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