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Distributed processing of big mobility data as spatio-temporal data streams

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Abstract

Recent rapid development of wireless communication, mobile computing, global navigation satellite systems (GNSS), and spatially enabled sensors are leading to an exponential growth of available mobility data produced continuously at high speed. Due to these advancements, a new class of monitoring applications has come to the focus, including real-time intelligent transportation systems, traffic monitoring and mobile objects tracking. These new information flow processing (IFP) application domains need to process huge volume of mobility data arriving in the form of continuous data streams from mobile objects. IFP applications are pushing traditional database technologies beyond their limits due to their massively increasing data volumes and demands for real-time processing. Mobility data, i.e. real-time, transient, time-varying sequences of spatio-temporal data items, generated by embedded positioning sensors demonstrates at least two Big Data core features: volume and velocity. Existing distributed data stream management systems (DSMS), real-time computing systems (RTCS) and their processing models are dominantly based on relational paradigm and continuous operator model. Thus, they have rudimentary spatio-temporal capabilities, provide expensive fault recovery requiring either hot replication or long recovery times, and do not handle faults and slow nodes. The framework proposed in this paper is a cornerstone towards efficient real-time managing and monitoring of mobile objects through distributed spatio-temporal streams processing on large clusters. A prototype implementation is rooted in a new stream processing model that overcomes the challenges of current distributed stream processing models and enable seamless integration with batch and interactive processing like MapReduce.

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Notes

  1. In some cases real-time actually means near-real-time or low latency.

  2. The source code for prototype called MobyDick is available at Bitbucket: https://bitbucket. org/DarioOsm/mobydick

  3. Our prototype, including operations on non-temporal types, relies on operations and functions of Apache Flink DataStream library [14] and JTS Topology Suite [58].

  4. Scala programming language doesn’t support interfaces, but enables multiple inheritance by implementing interfaces as traits. For this reason TemporalObject is implemented as a trait that enables TemporalPoint to inherit from two types.

  5. Watermark is special event generated at stream sources that coarsely advances event time. A watermark for time instant τ states that event time has progressed to τ in that particular stream, meaning that no events with a time instant smaller than τ can arrive any more.

  6. On the contrary, some cluster computing frameworks (Spark, Trill, etc.) wraps data streams into mini-batches, i.e., it collects all data that arrives within a certain period of time and runs a regular batch program on the collected data.

  7. Batch processing applications run efficiently as special cases of stream processing applications.

  8. This GPS dataset was collected by 182 users in a period of over five years, from April 2007 to August 2012. It contains approximately 20 million points with a total distance of about 1.2 million kilometers and a total duration of 48,000+ hours. The data were logged in over 30 cities in China, USA, and Europe.

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Acknowledgments

The authors would like to thank Mirta Baranović, Damir Kalpić and anonymous reviewers for their helpful and constructive comments that helped us to improve the paper.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, Department of Applied Computing, University of Zagreb, Unska 3, 10000, Zagreb, Croatia

    Zdravko Galić

  2. Faculty of Electrical Engineering, University of Tuzla, Franjevačka 2, 75000, Tuzla, Bosnia and Herzegovina

    Emir Mešković & Dario Osmanović

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  1. Zdravko Galić
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Galić, Z., Mešković, E. & Osmanović, D. Distributed processing of big mobility data as spatio-temporal data streams. Geoinformatica 21, 263–291 (2017). https://doi.org/10.1007/s10707-016-0264-z

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