[1]吉根林,王　敏.时空轨迹聚集模式挖掘研究进展[J].南京师范大学学报(自然科学版),2015,38(04):1.
　Ji Genlin,Wang Min.Research Progress of Mining of Gathering Patternin Spatio-Temporal Trajectory[J].Journal of Nanjing Normal University(Natural Science Edition),2015,38(04):1.
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第38卷
期数:
2015年04期
页码:
1
栏目:
综述
出版日期:
2015-12-30

文章信息/Info

Title:
Research Progress of Mining of Gathering Patternin Spatio-Temporal Trajectory
作者:
吉根林; 王　敏
南京师范大学计算机科学与技术学院，江苏 南京 210023
Author(s):
Ji Genlin; Wang Min
School of Computer Science and Technology，Nanjing Normal University，Nanjing 210023，China
关键词:
时空轨迹; 时空数据挖掘; 聚集模式
Keywords:
spatio-temporal trajectory; spatio-temporal data mining; gathering pattern
分类号:
TP181
文献标志码:
A
摘要:
时空轨迹聚集模式是指一组时空移动对象在一定时间内一起移动形成的行为模式. 作为一种重要的时空轨迹模式，聚集模式的应用涉及了人类行为、交通物流、应急疏散管理、动物习性和市场营销等诸多方面. 通过对时空轨迹数据进行挖掘，可以从中提取出有意义的聚集模式，从而帮助我们监控和预测一些不寻常的群体事件. 本文综述了时空轨迹聚集模式的研究进展，首先，给出了聚集模式的分类；然后介绍了各种聚集模式的挖掘算法，并对其特点进行分析和评述；最后讨论了现有方法面临的主要问题和挑战，并对聚集模式的研究进行了展望.
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.

参考文献/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.

相似文献/References:

[1]吉根林,赵 斌.面向大数据的时空数据挖掘综述[J].南京师范大学学报(自然科学版),2014,37(01):1.
　Ji Genlin,Zhao Bin.A Survey of Spatiotemporal Data Mining for Big Data[J].Journal of Nanjing Normal University(Natural Science Edition),2014,37(04):1.

备注/Memo

备注/Memo:
收稿日期：2015-03-16.　
基金项目：国家自然科学基金项目（41471371）　
通讯联系人：吉根林，博士，教授，博士生导师，研究方向：数据挖掘与云计算技术. E-mail：glji@njnu.edu.cn.
更新日期/Last Update: 2015-12-30