Cited By
View all- Patel D(2015)On Discovery of Spatiotemporal Influence-Based Moving ClustersACM Transactions on Intelligent Systems and Technology10.1145/26319266:1(1-23)Online publication date: 11-Mar-2015
Mining multi-object spatial-temporal movement patterns
Abstract
In this paper, we present an ongoing PhD research on mining Multi-object Spatial-temporal Movement Patterns (M-STEM Patterns) from a Trajectory Database (TJDB). Information of the M-STEM Pattern instances has numerous applications in epidemiology, ecology, location-based services, transportation, and social and behaviour sciences since it supplements the information provided by a traditional GIS. We describe the research we had conducted to find instances of two M-STEM Patterns, namely the Meeting pattern and the Convoy pattern. We conclude this paper after introducing our ongoing research on discovering instances of another M-STEM pattern called Tried-and-True Route pattern.
References
[1]
R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, VLDB, pages 487--499. Morgan Kaufmann, 1994.
[2]
H. H. Aung and K.-L. Tan. Discovery of evolving convoys. In M. Gertz and B. Ludäscher, editors, SSDBM, volume 6187 of Lecture Notes in Computer Science, pages 196--213. Springer, 2010.
[3]
H. H. Aung and K.-L. Tan. Finding closed memos. In J. B. Cushing, J. C. French, and S. Bowers, editors, SSDBM, volume 6809 of Lecture Notes in Computer Science, pages 369--386. Springer, 2011.
[4]
K. Buchin, M. Buchin, M. van Kreveld, and J. Luo. Finding long and similar parts of trajectories. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '09, pages 296--305, New York, NY, USA, 2009. ACM.
[5]
J. Elzinga and D. W. Hearn. Geometrical solutions for some minimax location problems. TRANSPORTATION SCIENCE, 6(4): 379--394, 1972.
[6]
J. Gudmundsson and M. J. van Kreveld. Computing longest duration flocks in trajectory data. In R. A. de By and S. Nittel, editors, GIS, pages 35--42. ACM, 2006.
[7]
J. G. Jetcheva, Y.-C. Hu, S. PalChaudhuri, A. K. Saha, and D. B. Johnson. CRAWDAD data set rice/ad_hoc_city (v. 2003-09-11). Downloaded from http://crawdad.cs.dartmouth.edu/rice/ad_hoc_city, Sept. 2003.
[8]
H. Jeung, H. T. Shen, and X. Zhou. Convoy queries in spatio-temporal databases. In G. Alonso, J. A. Blakeley, and A. L. P. Chen, editors, ICDE, pages 1457--1459. IEEE, 2008.
[9]
P. Kalnis, N. Mamoulis, and S. Bakiras. On discovering moving clusters in spatio-temporal data. In C. B. Medeiros, M. J. Egenhofer, and E. Bertino, editors, SSTD, volume 3633 of Lecture Notes in Computer Science, pages 364--381. Springer, 2005.
[10]
J.-G. Lee, J. Han, and K.-Y. Whang. Trajectory clustering: a partition-and-group framework. In C. Y. Chan, B. C. Ooi, and A. Zhou, editors, SIGMOD Conference, pages 593--604. ACM, 2007.
[11]
Z. Li, B. Ding, J. Han, and R. Kays. Swarm: Mining relaxed temporal moving object clusters. PVLDB, 3(1): 723--734, 2010.
[12]
I. Rhee, M. Shin, S. Hong, K. Lee, and S. Chong. On the levy-walk nature of human mobility. In INFOCOM, pages 924--932. IEEE, 2008.
[13]
D. Sacharidis, K. Patroumpas, M. Terrovitis, V. Kantere, M. Potamias, K. Mouratidis, and T. K. Sellis. On-line discovery of hot motion paths. In EDBT, volume 261 of ACM International Conference Proceeding Series, pages 392--403. ACM, 2008.
[14]
M. R. Vieira, P. Bakalov, and V. J. Tsotras. On-line discovery of flock patterns in spatio-temporal data. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '09, pages 286--295, New York, NY, USA, 2009. ACM.
[15]
J. J.-C. Ying, W.-C. Lee, T.-C. Weng, and V. S. Tseng. Semantic trajectory mining for location prediction. In I. F. Cruz, D. Agrawal, C. S. Jensen, E. Ofek, and E. Tanin, editors, GIS, pages 34--43. ACM, 2011.
[16]
M. J. Zaki. Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng., 12(3): 372--390, 2000.
Index Terms
- Mining multi-object spatial-temporal movement patterns
Recommendations
Post sequential patterns mining: a new method for discovering structural patterns
Intelligent information processing IIIn this paper we present a novel data mining technique, known as Post Sequential Patterns Mining, which can be used to discover Structural Patterns. A Structural Pattern is a new pattern, which is composed of sequential patterns, branch patterns or ...
Mining condensed spatial co-location patterns
MobiGIS '15: Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information SystemsThe discovery of co-location patterns among spatial events is an important task in spatial data mining. We introduce a new kind of spatial co-location patterns, named condensed spatial co-location patterns, that can be considered as a lossy compressed ...
Towards distributed convoy pattern mining
SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information SystemsMining movement data to reveal interesting behavioral patterns has gained attention in recent years. One such pattern is the convoy pattern which consists of at least m objects moving together for at least k consecutive time instants where m and k are ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2012 Authors.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 November 2012
Published in SIGSPATIAL Volume 4, Issue 3
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 207Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 26 Sep 2024
Other Metrics
Citations
Cited By
View all- Patel D(2015)On Discovery of Spatiotemporal Influence-Based Moving ClustersACM Transactions on Intelligent Systems and Technology10.1145/26319266:1(1-23)Online publication date: 11-Mar-2015
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in