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In this paper, we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. The existing subsequence matching algorithm by Faloutsos et al. would require an index for each moving average order, which causes serious storage and CPU time overhead. In this paper, we solve the problem using index interpolation. The proposed algorithm can use only a few indexes for pre-selected moving average orders k and performs subsequence matching for arbitrary order m (≤ k). We prove that the proposed algorithm causes no false dismissal. For selectivities less than 10-2, the degradation of search performance compared with the fully-indexed case is no more than 17.2 % when two out of 128 indexes are used. The algorithm works better with smaller selectivities.

A subsequence matching algorithm supporting moving average transform of arbitrary order in time-series databases using index interpolation