Visualization of Range-Constrained Optimal Density Clustering of Trajectories

  • Muhammed Mas-Ud Hussain
  • Goce Trajcevski
  • Kazi Ashik Islam
  • Mohammed Eunus Ali
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10411)


We present a system for efficient detection, continuous maintenance and visualization of range-constrained optimal density clusters of moving objects trajectories, a.k.a. Continuous Maximizing Range Sum (Co-MaxRS) queries. Co-MaxRS is useful in any domain involving continuous detection of “most interesting” regions involving mobile entities (e.g., traffic monitoring, environmental tracking, etc.). Traditional MaxRS finds a location of a given rectangle R which maximizes the sum of the weighted-points (objects) in its interior. Since moving objects continuously change their locations, the MaxRS at a particular time instant need not be a solution at another time instant. Our system solves two important problems: (1) Efficiently computing Co-MaxRS answer-set; and (2) Visualizing the results. This demo will present the implementation of our efficient pruning schemes and compact data structures, and illustrate the end-user tools for specifying the parameters and selecting datasets for Co-MaxRS, along with visualization of the optimal locations.


  1. 1.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: The next frontier for innovation, competition, and productivityGoogle Scholar
  2. 2.
    Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)CrossRefGoogle Scholar
  3. 3.
    Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.: Interactive visual clustering of large collections of trajectories. In: IEEE Symposium on Visual Analytics Science and Technology (2009)Google Scholar
  4. 4.
    Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)Google Scholar
  5. 5.
    Shen, Y., Zhao, L., Fan, J.: Analysis and visualization for hot spot based route recommendation using short-dated taxi GPS traces. Information 6(2), 134–151 (2015)CrossRefGoogle Scholar
  6. 6.
    Andrienko, N., Andrienko, G., Fuchs, G., Rinzivillo, S., Betz, H.D.: Detection, tracking, and visualization of spatial event clusters for real time monitoring. In: IEEE DSAA (2015)Google Scholar
  7. 7.
    Choi, D.W., Chung, C.W., Tao, Y.: Maximizing range sum in external memory. ACM Trans. Database Syst. 39(3), 21:1–21:44 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Nandy, S.C., Bhattacharya, B.B.: A unified algorithm for finding maximum and minimum object enclosing rectangles and cuboids. Comput. Math. Appl. 29(8), 45–61 (1995)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Hussain, M.M., Islam, K.A., Trajcevski, G., Ali, M.E.: Towards efficient maintenance of continuous MaxRS query for trajectories. In: 20th EDBT (2017)Google Scholar
  10. 10.
  11. 11.
    Vis.js: JavaScript visualization library.
  12. 12.
    Tao, Y., Papadias, D., Sun, J.: The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: VLDB (2003)Google Scholar
  13. 13.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: ACM World Wide Web (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Muhammed Mas-Ud Hussain
    • 1
  • Goce Trajcevski
    • 1
  • Kazi Ashik Islam
    • 2
  • Mohammed Eunus Ali
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceNorthwestern UniversityEvanstonUSA
  2. 2.Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh

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