research-article

Online discovery and maintenance of time series motifs

Authors:

Abdullah Mueen,

Eamonn KeoghAuthors Info & Claims

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1089 - 1098

https://doi.org/10.1145/1835804.1835941

Published: 25 July 2010 Publication History

Abstract

The detection of repeated subsequences, time series motifs, is a problem which has been shown to have great utility for several higher-level data mining algorithms, including classification, clustering, segmentation, forecasting, and rule discovery. In recent years there has been significant research effort spent on efficiently discovering these motifs in static offline databases. However, for many domains, the inherent streaming nature of time series demands online discovery and maintenance of time series motifs. In this paper, we develop the first online motif discovery algorithm which monitors and maintains motifs exactly in real time over the most recent history of a stream. Our algorithm has a worst-case update time which is linear to the window size and is extendible to maintain more complex pattern structures. In contrast, the current offline algorithms either need significant update time or require very costly pre-processing steps which online algorithms simply cannot afford.

Our core ideas allow useful extensions of our algorithm to deal with arbitrary data rates and discovering multidimensional motifs. We demonstrate the utility of our algorithms with a variety of case studies in the domains of robotics, acoustic monitoring and online compression.

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Supporting webpage containing Data, Code, Videos, Excel sheet and Presentation slides. Link: http://www.cs.ucr.edu/~mueen/OnlineMotif/index.html

Cited By

Zhong SMueen A(2024)MASS: distance profile of a query over a time seriesData Mining and Knowledge Discovery10.1007/s10618-024-01005-238:3(1466-1492)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-024-01005-2
Chao ZGao HMiao DWang H(2023)Discovering time series motifs of all lengths using dynamic time warpingWorld Wide Web10.1007/s11280-023-01207-626:6(3815-3836)Online publication date: 20-Sep-2023
https://doi.org/10.1007/s11280-023-01207-6
Akhavan ZEsmaeili MDevetsikiotis MZarkesh-Ha P(2022)Cross-Correlation for Streaming Seismic Time Series to Detect Events using Parallel and Real-time Methods2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON54665.2022.9965662(0008-0013)Online publication date: 26-Oct-2022
https://doi.org/10.1109/UEMCON54665.2022.9965662
Show More Cited By

Index Terms

Online discovery and maintenance of time series motifs
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

July 2010

1240 pages

ISBN:9781450300551

DOI:10.1145/1835804

General Chairs:
Bharat Rao
Siemens
,
Balaji Krishnapuram
Siemens
,
Program Chairs:
Andrew Tomkins
Google Inc.
,
Qiang Yang
Hong Kong University of Science and Technology

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 July 2010

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KDD '10

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KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 25 - 28, 2010

DC, Washington, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Zhong SMueen A(2024)MASS: distance profile of a query over a time seriesData Mining and Knowledge Discovery10.1007/s10618-024-01005-238:3(1466-1492)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-024-01005-2
Chao ZGao HMiao DWang H(2023)Discovering time series motifs of all lengths using dynamic time warpingWorld Wide Web10.1007/s11280-023-01207-626:6(3815-3836)Online publication date: 20-Sep-2023
https://doi.org/10.1007/s11280-023-01207-6
Akhavan ZEsmaeili MDevetsikiotis MZarkesh-Ha P(2022)Cross-Correlation for Streaming Seismic Time Series to Detect Events using Parallel and Real-time Methods2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON54665.2022.9965662(0008-0013)Online publication date: 26-Oct-2022
https://doi.org/10.1109/UEMCON54665.2022.9965662
Chatzigeorgakidis GSkoutas DPatroumpas KPalpanas TAthanasiou SSkiadopoulos S(2022)Efficient Range and kNN Twin Subsequence Search in Time SeriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3167257(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3167257
Yamaguchi AUeo KKashima H(2022)Learning Evolvable Time-series Shapelets2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00064(793-805)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00064
van Leeuwen FBosma Bden Born APostma E(2022)Normalization in Motif DiscoveryMachine Learning, Optimization, and Data Science10.1007/978-3-031-25891-6_24(314-325)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-25891-6_24
Zhu BJiang YGu MDeng Y(2021)A GPU Acceleration Framework for Motif and Discord Based Pattern MiningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.305576532:8(1987-2004)Online publication date: 1-Aug-2021
https://doi.org/10.1109/TPDS.2021.3055765
Park JYi DJi S(2020)Analysis of Recurrent Neural Network and PredictionsSymmetry10.3390/sym1204061512:4(615)Online publication date: 13-Apr-2020
https://doi.org/10.3390/sym12040615
Onwongsa CRatanamahatana C(2020)An Enhanced Time Series Motif Discovery Using Approximated Matrix ProfileProceedings of the 2020 2nd International Conference on Image Processing and Machine Vision10.1145/3421558.3421586(180-189)Online publication date: 5-Aug-2020
https://dl.acm.org/doi/10.1145/3421558.3421586
Muralidhar NTabassum AChen LChinthavali SRamakrishnan NPrakash B(2020)Cut-n-RevealACM Transactions on Intelligent Systems and Technology10.1145/339411811:5(1-26)Online publication date: 28-Jul-2020
https://dl.acm.org/doi/10.1145/3394118
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