Efficient Similarity Search in Multi-attribute Time Series Databases


The KIPS Transactions:PartD, Vol. 14, No. 7, pp. 727-732, Dec. 2007
10.3745/KIPSTD.2007.14.7.727,   PDF Download:

Abstract

Most of previous work on indexing and searching time series focused on the similarity matching and retrieval of one-attribute time series. However, multimedia databases such as music, video need to handle the similarity search in multi-attribute time series. The limitation of the current similarity models for multi-attribute sequences is that there is no consideration for attributes’ sequences. The multi-attribute sequences are composed of several attributes’ sequences. Since the users may want to find the similar patterns considering attributes’s sequences, it is more appropriate to consider the similarity between two multi-attribute sequences in the viewpoint of attributes’ sequences. In this paper, we propose the similarity search method based on attributes’s sequences in multi-attribute time series databases. The proposed method can efficiently reduce the search space and guarantees no false dismissals. In addition, we give preliminary experimental results to show the effectiveness of the proposed method.


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Cite this article
[IEEE Style]
S. J. Lee, "Efficient Similarity Search in Multi-attribute Time Series Databases," The KIPS Transactions:PartD, vol. 14, no. 7, pp. 727-732, 2007. DOI: 10.3745/KIPSTD.2007.14.7.727.

[ACM Style]
Sang Jun Lee. 2007. Efficient Similarity Search in Multi-attribute Time Series Databases. The KIPS Transactions:PartD, 14, 7, (2007), 727-732. DOI: 10.3745/KIPSTD.2007.14.7.727.