Abstract
Multi-resolution representation has been successfully used for indexing and retrieval of time series. In a previous work we presented Tight-MIR, a multi-resolution representation method which speeds up the similarity search by using distances pre-computed at indexing time. At query time Tight-MIR applies two pruning conditions to filter out non-qualifying time series. Tight-MIR has the disadvantage of storing all the distances corresponding to all resolution levels, even those whose pruning power is low. At query time Tight-MIR also processes all stored resolution levels. In this paper we optimize the Tight-MIR algorithm by enabling it to store and process only the resolution levels with the maximum pruning power. The experiments we conducted on the new optimized version show that it does not only require less storage space, but it is also faster than the original algorithm.
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Muhammad Fuad, M.M. (2015). Optimized Multi-Resolution Indexing and Retrieval Scheme of Time Series. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_61
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DOI: https://doi.org/10.1007/978-3-319-23485-4_61
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