Efficient motif discovery for large-scale time series in healthcare

Document Type

Article

Publication Date

6-1-2015

Abstract

Analyzing time series data can reveal the temporal behavior of the underlying mechanism producing the data. Time series motifs, which are similar subsequences or frequently occurring patterns, have significant meanings for researchers especially in medical domain. With the fast growth of time series data, traditional methods for motif discovery are inefficient and not applicable to large-scale data. This work proposes an efficient Motif Discovery method for Large-scale time series (MDLats). By computing standard motifs, MDLats eliminates a majority of redundant computation in the related arts and reuses existing information to the maximum. All the motif types and subsequences are generated for subsequent analysis and classification. Our system is implemented on a Hadoop platform and deployed in a hospital for clinical electrocardiography classification. The experiments on real-world healthcare data show that MDLats outperform the state-of-the-art methods even in large time series.

Identifier

84937430139 (Scopus)

Publication Title

IEEE Transactions on Industrial Informatics

External Full Text Location

https://doi.org/10.1109/TII.2015.2411226

ISSN

15513203

First Page

583

Last Page

590

Issue

3

Volume

11

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