Visualization of range-constrained optimal density clustering of trajectories

Muhammed Mas-Ud Hussain; Goce Trajcevski; Kazi Ashik Islam; Mohammed Eunus Ali

doi:10.1007/978-3-319-64367-0_29

Visualization of range-constrained optimal density clustering of trajectories

Muhammed Mas-Ud Hussain^*, Goce Trajcevski, Kazi Ashik Islam, Mohammed Eunus Ali

^*Corresponding author for this work

Master of Science in Machine Learning and Data Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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.

Original language	English (US)
Title of host publication	Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings
Editors	Wei-Shinn Ku, Agnes Voisard, Haiquan Chen, Chang-Tien Lu, Siva Ravada, Matthias Renz, Yan Huang, Michael Gertz, Liang Tang, Chengyang Zhang, Erik Hoel, Xiaofang Zhou
Publisher	Springer Verlag
Pages	427-432
Number of pages	6
ISBN (Print)	9783319643663
DOIs	https://doi.org/10.1007/978-3-319-64367-0_29
State	Published - 2017
Event	15th International Symposium on Spatial and Temporal Databases, SSTD 2017 - Arlington, United States Duration: Aug 21 2017 → Aug 23 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10411 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	15th International Symposium on Spatial and Temporal Databases, SSTD 2017
Country/Territory	United States
City	Arlington
Period	8/21/17 → 8/23/17

Funding

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.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-64367-0_29

Cite this

Mas-Ud Hussain, M., Trajcevski, G., Islam, K. A., & Ali, M. E. (2017). Visualization of range-constrained optimal density clustering of trajectories. In W.-S. Ku, A. Voisard, H. Chen, C.-T. Lu, S. Ravada, M. Renz, Y. Huang, M. Gertz, L. Tang, C. Zhang, E. Hoel, & X. Zhou (Eds.), Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings (pp. 427-432). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10411 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-64367-0_29

Mas-Ud Hussain, Muhammed ; Trajcevski, Goce ; Islam, Kazi Ashik et al. / Visualization of range-constrained optimal density clustering of trajectories. Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings. editor / Wei-Shinn Ku ; Agnes Voisard ; Haiquan Chen ; Chang-Tien Lu ; Siva Ravada ; Matthias Renz ; Yan Huang ; Michael Gertz ; Liang Tang ; Chengyang Zhang ; Erik Hoel ; Xiaofang Zhou. Springer Verlag, 2017. pp. 427-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Visualization of range-constrained optimal density clustering of trajectories",

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.",

author = "{Mas-Ud Hussain}, Muhammed and Goce Trajcevski and Islam, {Kazi Ashik} and Ali, {Mohammed Eunus}",

note = "Funding Information: 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. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 15th International Symposium on Spatial and Temporal Databases, SSTD 2017 ; Conference date: 21-08-2017 Through 23-08-2017",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Wei-Shinn Ku and Agnes Voisard and Haiquan Chen and Chang-Tien Lu and Siva Ravada and Matthias Renz and Yan Huang and Michael Gertz and Liang Tang and Chengyang Zhang and Erik Hoel and Xiaofang Zhou",

booktitle = "Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings",

}

Mas-Ud Hussain, M, Trajcevski, G, Islam, KA & Ali, ME 2017, Visualization of range-constrained optimal density clustering of trajectories. in W-S Ku, A Voisard, H Chen, C-T Lu, S Ravada, M Renz, Y Huang, M Gertz, L Tang, C Zhang, E Hoel & X Zhou (eds), Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10411 LNCS, Springer Verlag, pp. 427-432, 15th International Symposium on Spatial and Temporal Databases, SSTD 2017, Arlington, United States, 8/21/17. https://doi.org/10.1007/978-3-319-64367-0_29

Visualization of range-constrained optimal density clustering of trajectories. / Mas-Ud Hussain, Muhammed; Trajcevski, Goce; Islam, Kazi Ashik et al.
Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings. ed. / Wei-Shinn Ku; Agnes Voisard; Haiquan Chen; Chang-Tien Lu; Siva Ravada; Matthias Renz; Yan Huang; Michael Gertz; Liang Tang; Chengyang Zhang; Erik Hoel; Xiaofang Zhou. Springer Verlag, 2017. p. 427-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10411 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Visualization of range-constrained optimal density clustering of trajectories

AU - Mas-Ud Hussain, Muhammed

AU - Trajcevski, Goce

AU - Islam, Kazi Ashik

AU - Ali, Mohammed Eunus

N1 - Funding Information: 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. Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

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DO - 10.1007/978-3-319-64367-0_29

M3 - Conference contribution

AN - SCOPUS:85028457403

SN - 9783319643663

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 427

EP - 432

BT - Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings

A2 - Ku, Wei-Shinn

A2 - Voisard, Agnes

A2 - Chen, Haiquan

A2 - Lu, Chang-Tien

A2 - Ravada, Siva

A2 - Renz, Matthias

A2 - Huang, Yan

A2 - Gertz, Michael

A2 - Tang, Liang

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A2 - Hoel, Erik

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PB - Springer Verlag

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Mas-Ud Hussain M, Trajcevski G, Islam KA, Ali ME. Visualization of range-constrained optimal density clustering of trajectories. In Ku WS, Voisard A, Chen H, Lu CT, Ravada S, Renz M, Huang Y, Gertz M, Tang L, Zhang C, Hoel E, Zhou X, editors, Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings. Springer Verlag. 2017. p. 427-432. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-64367-0_29

Visualization of range-constrained optimal density clustering of trajectories

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