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Efficient similarity search in sequence databases (1994)

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by Rakesh Agrawal , Christos Faloutsos , Arun Swami
Citations:515 - 19 self
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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   

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