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 Lp 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.
Bibliographical noteFunding Information:
This work has been supported by Korea Research Foundation with Grant KRF-2003-041-D00486, the IT Research Center via Kangwon National University, and the University Research Program (C1-2002-146-0-3) of IITA. Sang-Wook Kim would like to thank Jung-Hee Seo, Suk-Yeon Hwang, Grace (Joo-Young) Kim, and Joo-Sung Kim for their encouragement and support.
All Science Journal Classification (ASJC) codes
- Information Systems
- Hardware and Architecture