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Identifying Similar Subsequences in Data Streams

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Database and Expert Systems Applications (DEXA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5181))

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Abstract

Similarity search has been studied in a domain of time series data mining, and it is an important technique in stream mining. Since sampling rates of streams are frequently different, and their time period varies in practical situations, the method which deals with time warping such as Dynamic Time Warping (DTW) is suitable for measuring similarity. However, finding pairs of similar subsequences between co-evolving sequences is difficult due to increase of the complexity because DTW is a method for detecting sequences that are similar to a given query sequence.

In this paper, we focus on the problem of finding pairs of similar subsequences and periodicity over data streams. We propose a method to detect similar subsequences in streaming fashion. Our approach for measuring similarity relies on a proposed scoring function that incrementally updates a score, which is suitable for data stream processing. We also present an efficient algorithm based on the scoring function. Our experiments on real and synthetic data demonstrate that our method detects the pairs of qualifying subsequence correctly and that it is dramatically faster than the existing method.

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Authors and Affiliations

  1. NTT Information Sharing Platform laboratories, NTT Corporation, 9–11, Midori-cho 3-Chome Musashino-shi, Tokyo, 180–8585, Japan

    Machiko Toyoda & Toshikazu Ichikawa

  2. NTT Communication Science Laboratories, NTT Corporation, 2–4, Hikaridai, Seika-cho, Keihanna Science City, Kyoto, 619–0237, Japan

    Yasushi Sakurai

Authors
  1. Machiko Toyoda
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  2. Yasushi Sakurai
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  3. Toshikazu Ichikawa
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Editor information

Sourav S. Bhowmick Josef Küng Roland Wagner

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Toyoda, M., Sakurai, Y., Ichikawa, T. (2008). Identifying Similar Subsequences in Data Streams. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2008. Lecture Notes in Computer Science, vol 5181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85654-2_23

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  • DOI: https://doi.org/10.1007/978-3-540-85654-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85653-5

  • Online ISBN: 978-3-540-85654-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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