Abstract
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.
M. Mas-Ud Hussain and G. Trajcevski—Research supported by NSF grants III 1213038 and CNS 1646107, ONR grant N00014-14-10215 and HERE grant 30046005.
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References
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: The next frontier for innovation, competition, and productivity
Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)
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)
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)
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)
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)
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)
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)
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 Maps API. https://developers.google.com/maps/
Vis.js: JavaScript visualization library. https://visjs.org/
Tao, Y., Papadias, D., Sun, J.: The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: VLDB (2003)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: ACM World Wide Web (2009)
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Mas-Ud Hussain, M., Trajcevski, G., Islam, K.A., Ali, M.E. (2017). Visualization of Range-Constrained Optimal Density Clustering of Trajectories. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_29
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DOI: https://doi.org/10.1007/978-3-319-64367-0_29
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