Abstract
There are a large number of applications that find the most similar pairs of time sequences in a given time-series database. However, similarity join operation in vast amounts of data is a big challenge in a single machine. For such data-intensive computing, distributed parallel processing framework such as MapReduce is getting a lot of attention. In this paper, we investigate how to operate subsequence similarity joins using MapReduce framework. We first show a sequential subsequence similarity join algorithm. Next, we propose two efficient algorithms to minimize the subsequence similarity join computation. We finally perform the experiments with synthetic data sets. The performance shows that the effectiveness of our MapReduce algorithms.
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Acknowledgments
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-00253, Development of an Advanced Open Data Distribution Platform based on International Standards).
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Park, K., Won, H.S., Ryu, K.H. (2018). A Study on Subsequence Similarity Join in Time Series Data Using MapReduce. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_135
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DOI: https://doi.org/10.1007/978-981-10-7605-3_135
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