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Distributed and parallel processing for real-time and dynamic spatio-temporal graph

  • Junhua FangEmail author
  • Jiafeng Ding
  • Pengpeng Zhao
  • Jiajie Xu
  • An Liu
  • Zhixu Li
Article
  • 29 Downloads
Part of the following topical collections:
  1. Special Issue on Graph Data Management in Online Social Networks

Abstract

As a non-linear data structure consisting of nodes and edges, the graph data span many different domains. In the real world, applications based on such data structures are always time-sensitive, that is, the value of graph data tends to decrease with time. Furthermore, the application based on spatio-temporal graph is one of the typical representatives of time-sensitive, since the time dimension is an inherent feature of spatio-temporal data. The Distributed Stream Processing Engine (DSPE) seems an excellent choice for the above requirement, which is commonly partitioned and concurrently processed by a number of threads to maximize the throughput. However, it is not feasible to do such mission directly using the traditional DSPE. In this paper, we propose a computational model suitable for handling the spatio-temporal graph in DSPE, by reconstructing the DSPE’s parallel processing slots. Specifically, our proposal includes a general processing framework to deal with the data structure of the spatio-temporal graph, a state information compensation mechanism to ensure the correctness of processing such stateful operation in DSPE, a lightweight summary information calculation method to ensure the performance of the system. Empirical studies on real-world stream applications validate the usefulness of our proposals and prove the considerable advantage of our approaches over state-of-the-art solutions in the literature.

Keywords

Distributed and parallel computing Real-time processing Spatio-temporal graph 

Notes

Acknowledgments

This work is partially supported by NSFC (No.61802273), the Postdoctoral Science Foundation of China under Grant (No. 2017M621813), the Postdoctoral Science Foundation of Jiangsu Province of China under Grant (No. 2018K029C), and the Natural Science Foundation for Colleges and Universities in Jiangsu Province of China under Grant (No. 18KJB520044).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Junhua Fang
    • 1
    Email author
  • Jiafeng Ding
    • 1
  • Pengpeng Zhao
    • 1
  • Jiajie Xu
    • 1
  • An Liu
    • 1
  • Zhixu Li
    • 1
  1. 1.Institute of Artificial Intelligence, School of Computer Science and TechnologySoochow UniversitySuzhouChina

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