Best-Match Time Series Subsequence Search on the Intel Many Integrated Core Architecture
نویسنده
چکیده
Subsequence similarity search is one of the basic problems of time series data mining. Nowadays Dynamic Time Warping (DTW) is considedered as the best similarity measure. However despite various existing software speedup techniques DTW is still computationally expensive. There are approaches to speed up DTW computation by means of parallel hardware (e.g. GPU and FPGA) but accelerators based on the Intel Many Integrated Core architecture have not been payed attention. The paper presents a parallel algorithm for best-match time series subsequence search based on DTW distance for the Intel Xeon Phi coprocessor. The experimental results on synthetic and real data sets confirm the efficiency of the algorithm.
منابع مشابه
Time Series Subsequence Similarity Search Under Dynamic Time Warping Distance on the Intel Many-core Accelerators
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 paral...
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