Abstract
Subsequence similarity search is one of the most important problems of time series data mining. Nowadays there is empirical evidence that Dynamic Time Warping (DTW) is the best distance metric for many applications. However in spite of sophisticated software speedup techniques DTW still computationally expensive. There are studies devoted to acceleration of the DTW computation by means of parallel hardware (e.g. computer-cluster, multi-core, FPGA and GPU). In this paper we present an approach to acceleration of the subsequence similarity search based on DTW distance using the Intel Many Integrated Core architecture. The experimental evaluation on synthetic and real data sets confirms the efficiency of the approach.
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Movchan, A., Zymbler, M. (2015). Time Series Subsequence Similarity Search Under Dynamic Time Warping Distance on the Intel Many-core Accelerators. In: Amato, G., Connor, R., Falchi, F., Gennaro, C. (eds) Similarity Search and Applications. SISAP 2015. Lecture Notes in Computer Science(), vol 9371. Springer, Cham. https://doi.org/10.1007/978-3-319-25087-8_28
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DOI: https://doi.org/10.1007/978-3-319-25087-8_28
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