Abstract
We address the problem of the similarity search in large multidimensional sequence databases. Most of previous work focused on similarity matching and retrieval of one-dimensional sequences. However, many new applications such as weather data or music databases need to handle multidimensional sequences. In this paper, we present the efficient search method for finding similar sequences to a given query sequence in multidimensional sequence databases. The proposed method can efficiently reduce the search space and guarantees no false dismissals. We give preliminary experimental results to show the effectiveness of the proposed method.
This work is supported by the Korea Research Foundation Grant (KRF-2004-005-D00198).
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Lee, S. et al. (2005). Efficient Pattern Matching of Multidimensional Sequences. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_22
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DOI: https://doi.org/10.1007/11548706_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
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