Efficient similarity search in sequence databases

  • Rakesh Agrawal
  • Christos Faloutsos
  • Arun Swami
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 730)


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 coefficients, we use R * -trees to index the sequences and efficiently 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.


Discrete Fourier Transform Fourier Coefficient Range Query Pink Noise False Dismissal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Rakesh Agrawal
    • 1
  • Christos Faloutsos
    • 1
  • Arun Swami
    • 1
  1. 1.IBM Almaden Research CenterSan Jose

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