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Developing a pattern discovery method in time series data and its GPU acceleration
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Zhu, H., Gu, Z., Zhao, H., Chen, K., Li, Chang-Tsun and He, Ligang (2018) Developing a pattern discovery method in time series data and its GPU acceleration. Big Data Mining and Analytics, 1 (4). pp. 266-283. doi:10.26599/BDMA.2018.9020021 ISSN 2096-0654.
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Official URL: https://doi.org/ 10.26599/BDMA.2018.9020021
Abstract
The Dynamic Time Warping (DTW) algorithm is widely used in finding the global alignment of time series. Many time series data mining and analytical problems can be solved by the DTW algorithm. However, using the DTW algorithm to find similar subsequences is computationally expensive or unable to perform accurate analysis. Hence, in the literature, the parallelisation technique is used to speed up the DTW algorithm. However, due to the nature of DTW algorithm, parallelising this algorithm remains an open challenge. In this paper, we first propose a novel method that finds the similar local subsequence. Our algorithm first searches for the possible start positions of subsequence, and then finds the best-matching alignment from these positions. Moreover, we parallelise the proposed algorithm on GPUs using CUDA and further propose an optimisation technique to improve the performance of our parallelization implementation on GPU. We conducted the extensive experiments to evaluate the proposed method. Experimental results demonstrate that the proposed algorithm is able to discover time series subsequences efficiently and that the proposed GPU-based parallelization technique can further speedup the processing.
Item Type: | Journal Article | ||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Data mining, Algorithms | ||||||||||||
Journal or Publication Title: | Big Data Mining and Analytics | ||||||||||||
Publisher: | IEEE | ||||||||||||
ISSN: | 2096-0654 | ||||||||||||
Official Date: | December 2018 | ||||||||||||
Dates: |
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Volume: | 1 | ||||||||||||
Number: | 4 | ||||||||||||
Page Range: | pp. 266-283 | ||||||||||||
DOI: | 10.26599/BDMA.2018.9020021 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Date of first compliant deposit: | 24 May 2018 | ||||||||||||
Date of first compliant Open Access: | 24 May 2018 | ||||||||||||
RIOXX Funder/Project Grant: |
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