›› 2018,Vol. 33 ›› Issue (4): 849-862.doi: 10.1007/s11390-018-1860-1

所属专题: Data Management and Data Mining

• Special Section on Computer Networks and Distributed Computing • 上一篇    下一篇

从地理标记数据中挖掘语义轨迹模式

Guochen Cai, Kyungmi Lee, Ickjai Lee*, Member, ACM   

  1. Information Technology Academy, College of Business, Law and Governance, James Cook University Queensland, QLD 4870, Australia
  • 收稿日期:2017-03-31 修回日期:2018-05-17 出版日期:2018-07-05 发布日期:2018-07-05
  • 通讯作者: Ickjai Lee,E-mail:Ickjai.Lee@jcu.edu.au E-mail:Ickjai.Lee@jcu.edu.au
  • 作者简介:Guochen Cai received his Ph.D. degree, in 2017, in information technology from James Cook University, Queensland. His current research areas include trajectory data mining, semantic trajectories, recommender systems, and spatio-temporal data mining.

Mining Semantic Trajectory Patterns from Geo-Tagged Data

Guochen Cai, Kyungmi Lee, Ickjai Lee*, Member, ACM   

  1. Information Technology Academy, College of Business, Law and Governance, James Cook University Queensland, QLD 4870, Australia
  • Received:2017-03-31 Revised:2018-05-17 Online:2018-07-05 Published:2018-07-05
  • Contact: Ickjai Lee,E-mail:Ickjai.Lee@jcu.edu.au E-mail:Ickjai.Lee@jcu.edu.au
  • About author:Guochen Cai received his Ph.D. degree, in 2017, in information technology from James Cook University, Queensland. His current research areas include trajectory data mining, semantic trajectories, recommender systems, and spatio-temporal data mining.

用户生成的标记了地理信息的社交媒体数据展现了动态时空轨迹信息。这些日益增长的流动性数据为提升对人们迁移行为的理解提供了潜在的机遇。现有好几个轨迹数据挖掘方法利用这些丰富的数据集,但它们不能在挖掘中合并非空间语义。本文调研了从地理标记数据中挖掘具有迁移时间的地理实体的频繁移动序列。与之前只有轨迹的地理特征的分析不同,本文主要提取具有丰富语境语义的模式。我们扩展了由地理标记数据生成的原始的地理轨迹,该数据具有丰富语境语义标注,使用地理兴趣表征名胜点,使用非空间语义标注丰富它们,并提出了一个语义轨迹模式挖掘算法,该算法返回基本的和多维的语义轨迹模式。实验结果表明我们方法所得到的语义轨迹模式在语义上呈现了语义上有意义的模式,并且展现了更加丰富的语义知识。

Abstract: User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.

[1] Goodchild M F. Citizens as sensors:The world of volunteered geography. GeoJournal, 2007, 69(4):211-221.

[2] Girardin F, Fiore F D, Ratti C, Blat J. Leveraging explic itly disclosed location information to understand tourist dy-namics:A case study. Journal of Location Based Services, 2008, 2(1):41-56.

[3] Lee I, Cai G, Lee K. Exploration of geo-tagged photos through data mining approaches. Expert Systems with Applications, 2014, 41(2):397-405.

[4] Bermingham L, Lee I. Spatio-temporal sequential pattern mining for tourism sciences. Procedia Computer Science, 2014, 29:379-389.

[5] Cai G, Hio C, Bermingham L, Lee K, Lee I. Sequential pattern mining of geo-tagged photos with an arbitrary regionsof-interest detection method. Expert Systems with Applications, 2014, 41(7):3514-3526.

[6] Giannotti F, Nanni M, Pinelli F, Pedreschi D. Trajectory pattern mining. In Proc. the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2007, pp.330-339.

[7] Ying J C, Lee W C, Weng T C, Tseng V S. Semantic trajectory mining for location prediction. In Proc. the Conference on ACM SIGSPATIAL GIS, Nov. 2011, pp.33-43.

[8] Parent C, Spaccapietra S, Renso C, Andrienko G, Andrienko N, Bogorny V, Damiani M L, Gkoulalas-Divanis A, Macedo J, Pelekis N et al. Semantic trajectories modeling and analysis. ACM Computing Surveys (CSUR), 2013, 45(4):Article No.42.

[9] Zhang C, Han J, Shou L, Lu J, La Porta T. Splitter:Mining fine-grained sequential patterns in semantic trajectories. Proc. VLDB Endow., May 2014, 7(9):769-780.

[10] Giannotti F, Nanni M, Pedreschi D. Efficient mining of temporally annotated sequences. In Proc. the 6th SIAM International Conference on Data Mining, April 2006, pp.348-359.

[11] Chen C C, Kuo C H, Peng W C. Mining spatial-temporal semantic trajectory patterns from raw trajectories. In Proc. IEEE International Conference on Data Mining Workshop (ICDMW), November 2015, pp.1019-1024.

[12] Chen C C, Chiang M F. Trajectory pattern mining:Exploring semantic and time information. In Proc. Conference on Technologies and Applications of Artificial Intelligence (TAAI), November 2016, pp.130-137.

[13] Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu M. PrefixSpan:Mining sequential patterns by prefixprojected growth. In Proc. the 17th International Conference on Data Engineering, April 2001, pp.215-224.

[14] Alvares L O, Bogorny V, Kuijpers B, Macedo J A F, Moelans B, Vaisman A. A model for enriching trajectories with semantic geographical information. In Proc. the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, Nov. 2007.

[15] Cai G, Lee K, Lee I. Mining semantic sequential patterns from geo-tagged photos. In Proc. the 49th Hawaii International Conference on System Sciences (HICSS), January 2016, pp.2187-2196.

[16] Zheng Y T, Zha Z J, Chua T S. Mining travel patterns from geotagged photos. ACM Trans. Intell. Syst. Technol., 2012, 3(3):56:1-56:18.

[17] Cai G, Lee K, Lee I. A framework for mining semanticlevel tourist movement behaviours from geo-tagged photos. In Proc. the 29th Australasian Joint Conference in AI, December 2016, pp.519-524.

[18] Majid A, Chen L, Mirza H T, Hussain I, Chen G. A system for mining interesting tourist locations and travel sequences from public geotagged photos. Data and Knowledge Engineering, 2015, 95:66-86.

[19] Beyer K, Ramakrishnan R. Bottom-up computation of sparse and iceberg cube. ACM SIGMOD Record, 1999, 28(2):359-370.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 周笛;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] 李未;. A Structural Operational Semantics for an Edison Like Language(2)[J]. , 1986, 1(2): 42 -53 .
[3] 李万学;. Almost Optimal Dynamic 2-3 Trees[J]. , 1986, 1(2): 60 -71 .
[4] 高庆狮; 张祥; 杨树范; 陈树清;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[5] 潘启敬;. A Routing Algorithm with Candidate Shortest Path[J]. , 1986, 1(3): 33 -52 .
[6] 吴恩华;. A Graphics System Distributed across a Local Area Network[J]. , 1986, 1(3): 53 -64 .
[7] 屈延文;. AGDL: A Definition Language for Attribute Grammars[J]. , 1986, 1(3): 80 -91 .
[8] 王建潮; 魏道政;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
[9] 郑国梁; 李辉;. The Design and Implementation of the Syntax-Directed Editor Generator(SEG)[J]. , 1986, 1(4): 39 -48 .
[10] 唐同诰; 招兆铿;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
版权所有 © 《计算机科学技术学报》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn
总访问量: