Presentation 2007-07-02
Trend Detection under the Time Warping
Machiko TOYODA, Toshikazu ICHIKAWA, Yasushi SAKURAI,
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Abstract(in English) Data stream is becoming increasingly important in several domains such as finance, sensor network environment, manufacture, network monitoring. The most fundamental support needed in these applications is efficient monitoring of time series data streams, and a subsequece-matching mechanism is required. In this paper, we propose a new function to recognize the similarity between subsequences, and then present an efficient sequence matching algorithm for data streams. Several experiments with synthetic and real data sets show that our algorithm works well as expected ; it can efficiently detect sequence trends in the data sets.
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Keyword(in English) Data Stream / Time Warping / Trend Detection / Subsequence Matching
Paper # DE2007-28
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Conference Information
Committee DE
Conference Date 2007/6/25(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Trend Detection under the Time Warping
Sub Title (in English)
Keyword(1) Data Stream
Keyword(2) Time Warping
Keyword(3) Trend Detection
Keyword(4) Subsequence Matching
1st Author's Name Machiko TOYODA
1st Author's Affiliation NTT Information Sharing Platform laboratories, NTT Corpration()
2nd Author's Name Toshikazu ICHIKAWA
2nd Author's Affiliation NTT Information Sharing Platform laboratories, NTT Corpration
3rd Author's Name Yasushi SAKURAI
3rd Author's Affiliation NTT Communication Science Laboratories, NTT Corporation
Date 2007-07-02
Paper # DE2007-28
Volume (vol) vol.107
Number (no) 131
Page pp.pp.-
#Pages 6
Date of Issue