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Erschienen in:
2018 | OriginalPaper | Buchkapitel
A Study on Subsequence Similarity Join in Time Series Data Using MapReduce
verfasst von : Kyounghyun Park, Hee Sun Won, Keun Ho Ryu
Erschienen in: Advances in Computer Science and Ubiquitous Computing
Verlag: Springer Singapore
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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.