|Table of Contents|

Research Progress of Mining of Gathering Patternin Spatio-Temporal Trajectory(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2015年04期
Page:
1-
Research Field:
综述
Publishing date:

Info

Title:
Research Progress of Mining of Gathering Patternin Spatio-Temporal Trajectory
Author(s):
Ji GenlinWang Min
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
spatio-temporal trajectoryspatio-temporal data mininggathering pattern
PACS:
TP181
DOI:
-
Abstract:
The gathering pattern of spatio-temporal trajectory is a behavior pattern of a set of spatio-temporal moving objects,moving together within a certain period of time. As an important pattern of spatio-temporal trajectory,the application range of gathering patterns covers human behavior,transport and logistics,emergency evacuation management,animal behavior,marketing and many other fields. Through mining the spatio-temporal trajectory data,we can discover meaningful gathering patterns to help us monitor and predict some unusual group incidents. This paper summarizes the research progress of mining of gathering pattern in spatio-temporal trajectory systematically. Firstly,we give the classification of gathering pattern. Afterwards,we introduce the mining algorithms of gathering patterns respectively,and also analyse and review its characteristics. Finally,we discuss the major issues and challenges faced by existing methods,in addition,outlook the research of mining gathering patterns.

References:

[1]WANG Y,LIM E P,HWANG S Y. On mining group patterns of mobile users[C]//Database and Expert Systems Applications. Berlin Heidelberg:Springer,2003:287-296.
[2]LAUBE P,VAN KREVELD M,IMFELD S. Finding REMO detecting relative motion patterns in geospatial lifelines[C]//Developments in Spatial Data Handling. Berlin Heidelberg:Springer,2005:201-215.
[3]LAUBE P,IMFELD S,WEIBEL R. Discovering relative motion patterns in groups of moving point objects[J]. International journal of geographical information science,2005,19(6):639-668.
[4]KALNIS P,MAMOULIS N,BAKIRAS S. On discovering moving clusters in spatio-temporal data[C]//Advances in Spatialand Temporal Databases. Berlin Heidelberg:Springer,2005:364-381.
[5]TANG LUAN,ZHENG YU,YUAN JING,et al. On discovery of traveling companions from streaming trajectories[C]//IEEE 28th International Conference on Data Engineering. Washington,USA,2012:186-197.
[6]ZHENG K,ZHENG Y,YUAN N J,et al. On discovery of gathering patterns from trajectories[C]//IEEE 29th International Conference on Data Engineering. Brisbane,Queensland,2013:242-253.
[7]ZHENG K,ZHENG Y,YUAN N,et al. Online discovery of gathering patterns over trajectories[J]. IEEE transactions on knowledge and data engineering,2013,8(26):1 974-1 988.
[8]LAUBE P,IMFELD S. Analyzing relative motion within groups of trackable moving point objects[C]//Geographic Information Science. Berlin Heidelberg:Springer,2002:132-144.
[9]BENKERT M,GUDMUNDSSON J,HüBNER F,et al. Reporting flock patterns[J]. Computational geometry,2008,41(3):111-125.
[10]WANG Y,LIM E P,HWANG S Y. Effective group pattern mining using data summarization[C]//9th International Conference on Database Systems for Advanced Application. Seoul,Korea,2004.
[11]JEUNG H,SHEN H T,ZHOU X. Convoy queries in spatio-temporal databases[C]//IEEE 24th International Conference on Data Engineering. Cancun,Mexico,2008:1 457-1 459.
[12]JEUNG H,YIU M L,ZHOU X,et al. Discovery of convoys in trajectory databases[J]. Proceedings of the VLDB endowment,2008,1(1):1 068-1 080.
[13]AUNG H H,TAN K L. Discovery of evolving convoys[C]//Scientifi and Statistical Database Management. Heidelberg,Germany,2010:196-213.
[14]LI Z,DING B,HAN J,et al. Swarm:Mining relaxed temporal moving object clusters[J]. Proceedings of the VLDB endowment,2010,3(1/2):723-734.
[15]EPPSTEIN D,GOODRICH M T,SUN J Z. The skip quadtree:A simple dynamic data structure for multidimensional data[C]//Proceedings of the 21st ACM Symposium on Computational Geometry. Pisa,Italy,2005:296-305.
[16]GUDMUNDSSON J,van KREVELD M. Computing longest duration flocksin trajectory data[C]//Proceedings of the 14th annual ACM international Symposium on Advances in Geographic Information Systems. Lzmir,Turkey,2006:35-42.
[17]AL-NAYMAT G,CHAWLA S,GUDMUNDSSON J. Dimensionality reduction for long duration and complex spatio-temporal queries[C]//Proceedings of the 2007 ACM Symposium on Applied Computing. Seoul,Korea,2007:393-397.
[18]BAGNALL A,KEOGH E,LONARDI S,et al. A bit level representation for time series data mining with shape based similarity[J]. Data mining and knowledge discovery,2006,13(1):11-40.
[19]BINGHAM E,MANNILA H. Random projection in dimensionality reduction:applications to image and text data[C]//Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco,CA,USA,2001:245-250.
[20]HWANG S Y,LIU Y H,CHIU J K,et al. Mining mobile group patterns:a trajectory-based approach[C]//Advances in Knowledge Discovery and Data Mining. Berlin Heidelberg:Springer,2005:713-718.
[21]WANG Y,LIM E P,HWANG S Y. Efficient algorithms for mining maximal valid groups[J]. The international journal on very large data bases,2008,17(3):515-535.
[22]MERATNIA N,ROLF A. Spatiotemporal compression techniques for moving point objects[C]//Advances in Database Technology. Berlin Heidelberg:Springer,2004:765-782.
[23]LI Z,LEE J G,LI X,et al. Incremental clustering for trajectories[C]//15th International Conference on Database Systems for Advanced Applications. Tsukuba,Japan,2010:32-46.

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Last Update: 2015-12-30