Abstract
Recently, sensor-based applications have emerged and collected plenty of long time series. Traditional whole matching similarity search can only query full length time series. However, for long time series, similarity search on arbitrary time windows is more attractive and important. In this paper, we address the problem of window-based KNN search of time series data on HBase. Based on PAA approximation, we propose a composite index structure comprising Horizontal Segment Tree and Vertical Inverted Table. VI-Table is capable to prune time series by data summary in high levels, while HS-Tree leverages data summary in low levels to reduce access of the raw time series data. Both VI-Table and HS-Tree can be built parallel and incrementally. Our experiment results show the effectiveness and robustness of the proposed approach.
The work was supported by the Ministry of Science and Technology of China, National Key Research and Development Program under No. 2016YFB1000700, National Key Basic Research Program of China under No. 2015CB358800 and NSFC (61672163, U1509213).
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References
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Wang, X., Fang, Z., Wang, P., Zhu, R., Wang, W. (2017). A Distributed Multi-level Composite Index for KNN Processing on Long Time Series. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_14
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DOI: https://doi.org/10.1007/978-3-319-55753-3_14
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