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Efficient similarity search in sequence databases (1994)
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BibTeX
@MISC{Agrawal94efficientsimilarity,
author = {Rakesh Agrawal and Christos Faloutsos and Arun Swami},
title = {Efficient similarity search in sequence databases},
year = {1994}
}
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Abstract
We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong. Another important observation is Parseval's theorem, which specifies that the Fourier transform preserves the Euclidean distance in the time or frequency domain. Having thus mapped sequences to a lower-dimensionality space by using only the first few Fourier coe cients, we use R-trees to index the sequences and e ciently answer similarity queries. We provide experimental results which show that our method is superior to search based on sequential scanning. Our experiments show that a few coefficients (1-3) are adequate to provide good performance. The performance gain of our method increases with the number and length of sequences.
Keyphrases
sequence database efficient similarity search similarity query frequency domain time sequence crucial observation discrete fourier transform good performance fourier transform sequential scanning practical interest indexing method important observation euclidean distance lower-dimensionality space performance gain experimental result first frequency first fourier coe cients
