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Time Series Similarity Search for Streaming Data in Distributed Systems
Citation key ZiehnCHM19
Author Ariane Ziehn, Marcela Charfuelan, Holmer Hemsen, Volker Markl
Year 2019
Address An Erratum is available below under "link to publication".
Journal DARLI_AP Workshop, co-located with EDBT/ICDT 2019
Abstract In this paper we propose a practical study and demonstration of time series similarity search in modern distributed data processing platforms for stream data. After an intensive literature review, we implement a flexible similarity search application in Apache Flink, which includes the most commonly used distance measurements: Euclidean distance and Dynamic Time Warping. For efficient and accurate similarity search we evaluate normalization and pruning techniques developed for single machine processing and demonstrate that they can be adapted and leveraged for those distributed platforms. Our final implementation is capable of monitoring many time series in real-time and parallel. Further, we demonstrate that the number of required parameters can be reduced and optimally derived from data properties. We evaluate our application by comparing its performance with electrocardiogram data on a cluster with several nodes. We reach average response times of less than 0,1 ms for windows of 2 s of data, which allow fast reactions on matching sequences.
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