Shape-based retrieval in time-series databases

  • Authors:
  • Sang-Wook Kim;Jeehee Yoon;Sanghyun Park;Jung-Im Won

  • Affiliations:
  • College of Information and Communications, Hanyang University, 17 Haengdang, Seongdong, Seoul 133-791, Republic of Korea;Division of Information Engineering and Telecommunications, Hallym University, 39 Hallymdaehak-gil, Chuncheon, Kangwon 200-702, Republic of Korea;Department of Computer Science, Yonsei University, 134 Sinchon, Seodaemoon Gu, Seoul 120-749, Republic of Korea;Department of Computer Science, Yonsei University, 134 Sinchon, Seodaemoon Gu, Seoul 120-749, Republic of Korea

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2006

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Abstract

The shape-based retrieval is defined as the operation that searches for the (sub)sequences whose shapes are similar to that of a query sequence regardless of their actual element values. In this paper, we propose a similarity model suitable for shape-based retrieval and present an indexing method for supporting the similarity model. The proposed similarity model enables to retrieve similar shapes accurately by providing the combination of multiple shape-preserving transformations such as normalization, moving average, and time warping. Our indexing method stores every distinct subsequence concisely into the disk-based suffix tree for efficient and adaptive query processing. We allow the user to dynamically choose a similarity model suitable for a given application. More specifically, we allow the user to determine the parameter p of the distance function L"p when submitting a query. The result of extensive experiments revealed that our approach not only successfully finds the subsequences whose shapes are similar to a query shape but also significantly outperforms the sequential scan method.