Abstract
Discords are the most unusual subsequences of a time series. Sequential discovery of discords is time consuming. As the scale of datasets increases unceasingly, datasets have to be kept on hard disk, which degrades the utilization of computing resources. Furthermore, the results discovered from segmentations of a time series are non-combinable, which makes discord discovery hard to parallelize. In this paper, we propose Parallel Discord Discovery (PDD), which divides the discord discovery problem in a combinable manner and solves its sub-problems in parallel. PDD accelerates discord discovery with multiple computing nodes and guarantees the correctness of the results. PDD stores large time series in distributed memory and takes advantage of in-memory computing to improve the utilization of computing resources. Experiments show that given 10 computing nodes, PDD is seven times faster than the sequential method HOTSAX. PDD is able to handle larger datasets than HOTSAX does. PDD achieves over 90 % utilization of computing resources, nearly twice as much as the disk-aware method does.
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Acknowledgments
This paper is sponsored by National Natural Science Foundation of China (No. 61373032), the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program and the National High Technology and Research Development Program of China (863 Program, 2015AA050204).
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Huang, T. et al. (2016). Parallel Discord Discovery. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_19
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DOI: https://doi.org/10.1007/978-3-319-31750-2_19
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