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.
UR - http://www.scopus.com/inward/record.url?scp=85028457403&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-64367-0_29
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
A2 - Zhang, Chengyang
A2 - Hoel, Erik
A2 - Zhou, Xiaofang
PB - Springer Verlag
T2 - 15th International Symposium on Spatial and Temporal Databases, SSTD 2017
Y2 - 21 August 2017 through 23 August 2017
ER -