article

A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases

Authors:

Kyu-Young WhangAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 9, Issue 1

Pages 5 - 28

https://doi.org/10.1023/B:DAMI.0000026902.89522.a3

Published: 01 July 2004 Publication History

Abstract

In this paper, an algorithm is proposed for subsequence matching that supports normalization transform in time-series databases. Normalization transform enables finding sequences with similar fluctuation patterns even though they are not close to each other before the normalization transform. Simple application of existing subsequence matching algorithms to support normalization transform is not feasible since the algorithms do not have information for normalization transform of subsequences of arbitrary lengths. Application of the existing whole matching algorithm supporting normalization transform to the subsequence matching is feasible, but requires an index for every possible length of the query sequence causing serious overhead on both storage space and update time. The proposed algorithm generates indexes only for a small number of different lengths of query sequences. For subsequence matching it selects the most appropriate index among them. Better search performance can be obtained by using more indexes. In this paper, the approach is called index interpolation . It is formally proved that the proposed algorithm does not cause false dismissal. The search performance can be traded off with storage space by adjusting the number of indexes. For performance evaluation, a series of experiments is conducted using the indexes for only five different lengths out of lengths 256~512 of the query sequence. The results show that the proposed algorithm outperforms the sequential scan by up to 2.4 times on the average when the selectivity of the query is 10 ² and up to 14.6 times when it is 10 ⁵. Since the proposed algorithm performs better with smaller selectivities, it is suitable for practical situations, where the queries with smaller selectivities are much more frequent.

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Cited By

Linardi MPalpanas T(2020)Scalable data series subsequence matching with ULISSEThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-020-00619-429:6(1449-1474)Online publication date: 4-Jul-2020
https://dl.acm.org/doi/10.1007/s00778-020-00619-4
Song JLim SKim SHaddad HWainwright RChbeir R(2018)A novel join technique for similar-trend searches supporting normalization on time-series databasesProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3173383(481-486)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3173383
Lee SKim HChoi MMoon Y(2018)A time-series matching approach for symmetric-invariant boundary image matchingMultimedia Tools and Applications10.1007/s11042-017-5323-477:16(20979-21001)Online publication date: 1-Aug-2018
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A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases

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cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 9, Issue 1

July 2004

110 pages

ISSN:1384-5810

Issue’s Table of Contents

Copyright © Copyright © 2004 Kluwer Academic Publishers.

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Kluwer Academic Publishers

United States

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Published: 01 July 2004

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Cited By

Linardi MPalpanas T(2020)Scalable data series subsequence matching with ULISSEThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-020-00619-429:6(1449-1474)Online publication date: 4-Jul-2020
https://dl.acm.org/doi/10.1007/s00778-020-00619-4
Song JLim SKim SHaddad HWainwright RChbeir R(2018)A novel join technique for similar-trend searches supporting normalization on time-series databasesProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3173383(481-486)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3173383
Lee SKim HChoi MMoon Y(2018)A time-series matching approach for symmetric-invariant boundary image matchingMultimedia Tools and Applications10.1007/s11042-017-5323-477:16(20979-21001)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11042-017-5323-4
Niennattrakul VRuengronghirunya PRatanamahatana C(2018)Exact indexing for massive time series databases under time warping distanceData Mining and Knowledge Discovery10.1007/s10618-010-0165-y21:3(509-541)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1007/s10618-010-0165-y
Kang RWang CWang PDing YWang J(2018)Matching Consecutive Subpatterns over Streaming Time SeriesWeb and Big Data10.1007/978-3-319-96893-3_8(90-105)Online publication date: 23-Jul-2018
https://dl.acm.org/doi/10.1007/978-3-319-96893-3_8
Kim BMoon YLee J(2017)Boundary image matching supporting partial denoising using time-series matching techniquesMultimedia Tools and Applications10.1007/s11042-016-3479-y76:6(8471-8496)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s11042-016-3479-y
Kim BMoon YChoi MKim J(2014)Interactive noise-controlled boundary image matching using the time-series moving average transformMultimedia Tools and Applications10.1007/s11042-013-1552-372:3(2543-2571)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1007/s11042-013-1552-3
Kashyap SLee MHsu W(2011)Similar subsequence search in time series databasesProceedings of the 22nd international conference on Database and expert systems applications - Volume Part I10.5555/2035368.2035390(232-246)Online publication date: 29-Aug-2011
https://dl.acm.org/doi/10.5555/2035368.2035390
Patel DHsu WLi Lee MParthasarathy S(2010)Lag patterns in time series databasesProceedings of the 21st international conference on Database and expert systems applications: Part II10.5555/1887568.1887591(209-224)Online publication date: 30-Aug-2010
https://dl.acm.org/doi/10.5555/1887568.1887591
Lim HWhang KMoon Y(2008)Similar sequence matching supporting variable-length and variable-tolerance continuous queries on time-series data streamInformation Sciences: an International Journal10.1016/j.ins.2007.10.026178:6(1461-1478)Online publication date: 20-Mar-2008
https://dl.acm.org/doi/10.1016/j.ins.2007.10.026
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