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Finding Time Series Motifs in Disk-Resident Data
| Citations: | 7 - 1 self |
BibTeX
@MISC{Mueen_findingtime,
author = {Abdullah Mueen and Eamonn Keogh and Nima Bigdely-shamlo},
title = {Finding Time Series Motifs in Disk-Resident Data},
year = {}
}
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Abstract
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 exact time series motifs 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 describe for the first time a disk-aware algorithm to find exact time series motifs 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. Keywords-time series motif; exact method; disk aware; EEG I.
Keyphrases
disk-resident data time series motif exact time series motif abstract time series motif computer networking keywords-time series motif long time series recent year time series meaningful motif first time approximate motif small datasets several higher-level data mining multi-gigabyte database open problem main memory previous effort exact method similar subsequence image processing many order extensive research disk-aware algorithm massive database diverse area
