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Dragoon: a hybrid and efficient big trajectory management system for offline and online analytics

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Abstract

With the explosive use of GPS-enabled devices, increasingly massive volumes of trajectory data capturing the movements of people and vehicles are becoming available, which is useful in many application areas, such as transportation, traffic management, and location-based services. As a result, many trajectory data management and analytic systems have emerged that target either offline or online settings. However, some applications call for both offline and online analyses. For example, in traffic management scenarios, offline analyses of historical trajectory data can be used for traffic planning purposes, while online analyses of streaming trajectories can be adopted for congestion monitoring purposes. Existing trajectory-based systems tend to perform offline and online trajectory analysis separately, which is inefficient. In this paper, we propose a hybrid and efficient framework, called Dragoon, based on Spark, to support both offline and online big trajectory management and analytics. The framework features a mutable resilient distributed dataset model, including RDD Share, RDD Update, and RDD Mirror, which enables hybrid storage of historical and streaming trajectories. It also contains a real-time partitioner capable of efficiently distributing trajectory data and supporting both offline and online analyses. Therefore, Dragoon provides a hybrid analysis pipeline. Support for several typical trajectory queries and mining tasks demonstrates the flexibility of Dragoon. An extensive experimental study using both real and synthetic trajectory datasets shows that Dragoon (1) has similar offline trajectory query performance with the state-of-the-art system UlTraMan; (2) decreases up to doubled storage overhead compared with UlTraMan during trajectory editing; (3) achieves at least 40% improvement of scalability compared with popular streaming processing frameworks (i.e., Flink and Spark Streaming); and (4) offers an average doubled performance improvement for online trajectory data analytics.

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Notes

  1. https://github.com/OpenHFT/Chronicle-Map.

  2. https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/stream/state/.

  3. https://research.microsoft.com/en-us/projects.

  4. This is a proprietary dataset.

  5. https://iapg.jade-hs.de/personen/brinkhoff/generator/.

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Acknowledgements

This work was supported in part by the NSFC under Grant Nos. 62025206 and 61972338, the National Key R&D Program of China under Grant No. 2018YFB1004 003, and the NSFC-Zhejiang Joint Fund under Grant No. U1609217. Yunjun Gao is the corresponding author of the work.

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Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, China

    Ziquan Fang, Lu Chen, Yunjun Gao & Lu Pan

  2. Alibaba–Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China

    Yunjun Gao

  3. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Christian S. Jensen

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  1. Ziquan Fang
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Correspondence to Yunjun Gao.

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Fang, Z., Chen, L., Gao, Y. et al. Dragoon: a hybrid and efficient big trajectory management system for offline and online analytics. The VLDB Journal 30, 287–310 (2021). https://doi.org/10.1007/s00778-021-00652-x

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