Visualization of range-constrained optimal density clustering of trajectories

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 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 languageEnglish (US)
Title of host publicationAdvances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings
EditorsWei-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
PublisherSpringer Verlag
Pages427-432
Number of pages6
ISBN (Print)9783319643663
DOIs
StatePublished - Jan 1 2017
Event15th International Symposium on Spatial and Temporal Databases, SSTD 2017 - Arlington, United States
Duration: Aug 21 2017Aug 23 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10411 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Symposium on Spatial and Temporal Databases, SSTD 2017
CountryUnited States
CityArlington
Period8/21/178/23/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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