Eecient Similarity Search in Sequence Databases

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 rst few frequencies are strong. Another important observation is Parseval's theorem, which speci es 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 rst 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 coe cients (1-3) are adequate to provide good performance. The performance gain of our method increases with the number and length of sequences.