Abstract
The proliferation of GPS-enabled devices has resulted in massive trajectory data streams. Moving objects’ trajectories contain patterns which are useful for many applications, for instance, traffic monitoring, fleet management, etc. Pattern matching is a prerequisite of complex event processing, which is used to find complex patterns in data sequences. A number of distributed frameworks, like Apache Flink, Storm, etc., support pattern matching and complex event processing. However, they do not natively support pattern matching over trajectory streams. To address this problem, we propose a framework, TraPM, to support online pattern matching over trajectory streams. In addition, to accelerate spatial predicate evaluation, TraPM utilizes spatial indexing, i.e., Rtree and grid index. Moreover, it employs partition-based data distribution to distribute data across the cluster nodes. Extensive experiments on a real dataset demonstrate that our proposed framework can effectively detect patterns from trajectory streams and achieve higher throughput than the baseline approach.
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Acknowledgement
This paper is based on results obtained from “Research and Development Project of the Enhanced Infrastructures for Post-5G Information and Communication Systems” (JPNP20017) commissioned by NEDO, JPNP14004 commissioned by NEDO, JST CREST Grant Number JPMJCR22M2, AMED Grant Number JP21zf0127005, and JSPS KAKENHI Grant Numbers JP22H03694 and JP23H03399.
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Trisminingsih, R., Shaikh, S.A., Amagasa, T., Kitagawa, H., Matono, A. (2023). TraPM: A Framework for Online Pattern Matching Over Trajectory Streams. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_45
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DOI: https://doi.org/10.1007/978-3-031-48316-5_45
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