|Table of Contents|

Research on Storage Methods of Spatio-Temporal Trajectories(PDF)

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

Issue:
2017年03期
Page:
38-
Research Field:
·计算机科学·
Publishing date:

Info

Title:
Research on Storage Methods of Spatio-Temporal Trajectories
Author(s):
Zheng Haoquan1He Haoqi2Liu Jiaye2Zhao Bin2*Ji Genlin2Yu Zhaoyuan3
(1.State Grid Electric Power Research Institute,Nanjing 211100,China)(2.School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China)(3.School of Geography Science,Nanjing Normal University,Nanjing 210023,China)
Keywords:
trajectorydisk storagecolumn storetrajectory data mining
PACS:
TP392
DOI:
10.3969/j.issn.1001-4616.2017.03.006
Abstract:
Spatiotemporal trajectory data storage is an important research issue in trajectory data management,which directly affects the performances of trajectory data mining algorithms. In this paper,we propose three trajectory storage methods according to data access methods,including the trajectory storage based on chronological orders,the trajectory storage based on spatial attributes and the trajectory storage based on temporal attributes. The basic principle of trajectory storage is that the data involved in one operation should be stored as close as possible. The three trajectory storage methods are implemented,and the experimental results based on a real data set show that,employing appropriate trajectory storage methods according to the characteristics of data access may significantly improve the efficiency of trajectory data mining algorithms and help to better support trajectory data analysis and mining tasks.

References:

[1] MEDIANO M R,CASANOVA M A,DREUX M. V-Trees a storage method for long vector data[C]//Proceedings of 20th International Conference on Very Large Data Bases. Santiago de Chile,Chile:Morgan Kaufmann,1994:321-330.
[2]CHAKKA V P,EVERSPAUGH A,PATEL J M. Indexing large trajectory data sets with SETI[C]//First Biennial Conference on Innovative Data Systems Research. Asilomar,CA,USA:www.cidrdb.org,2003.
[3]BOTEA V,MALLETT D,NASCIMENTO M A,et al. PIST:an efficient and practical indexing technique for historical spatio-temporal point data[J]. GeoInformatica,2008,12(2):143-168.
[4]MAUROUX P C,WU E,MADDEN S. TrajStore:an adaptive storage system for very large trajectory data sets[C]//Proceedings of the 26th International Conference on Data Engineering. Long Beach,California,USA:IEEE Computer Society,2010:109-120.
[5]Wang H Z,Zheng K,Xu J J,et al. SharkDB:an In-memory column-oriented trajectory storage[C]//Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. Melbourne,Victoria,Australia:ACM,2015:1 099-1 104.
[6]ZHENG Y. Trajectory data mining:an overview[J]. ACM transactions on intelligent systems and technology,2015,6(3):29:1-29:41.
[7]吉根林,赵斌. 时空轨迹大数据模式挖掘研究进展[J]. 数据采集与处理,2015,30(1):47-58.
[8]吉根林,赵斌. 面向大数据的时空数据挖掘综述[J]. 南京师大学报(自然科学版),2014,37(1):1-7.
[9]吉根林,孙鸿艳,赵斌. 时空轨迹群体运动模式挖掘研究进展[J]. 南京航空航天大学学报,2016,48(5):615-624.
[10]LEE J G,HAN J W,WHANG KY. Trajectory clustering:a partition-and-group framework[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data. Beijing,China:ACM,2007:593-604.
[11]ZHENG Y,CHEN Y,LI Q,et al. Understanding transportation modes based on GPS data for web applications[J]. Acm transactions on the Web,2010,4(1):495-507.
[12]PATEL D. Incorporating duration and region association information in trajectory classification[J]. Journal of location based services,2013,7(4):246-271.
[13]JEUNG H,YIU M L,ZHOU X F,et al. Discovery of convoys in trajectory databases[J]. Proceedings of the VLDB endowment,2008,1(1):1 068-1 080.
[14]ZHENG K,ZHENG Y,YUAN N J,et al. On discovery of gathering patterns from trajectories[C]//29th IEEE International Conference on Data Engineering. Brisbane,Australia:IEEE Computer Society,2013,242-253.
[15]GIANNOTTI F,NANNI M,PINELLI F,et al. Trajectory pattern mining[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,San Jose,California,USA:ACM,2007:330-339.
[16]CAO H P,MAMOULIS N,CHEUNG D W. Discovery of periodic patterns in spatiotemporal sequences[J]. IEEE transactions on knowledge and data engineering,2007,19(4):453-467.
[17]ESTER M,KRIEGEL H P,SANDER J,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining(KDD-96). Portland,Oregon,USA:AAAI Press,1996:226-231.
[18]LEE J G,HAN J W,LI X L. Trajectory outlier detection:a partition-and-detect framework[C]//Proceedings of the 24th International Conference on Data Engineering. Cancun,Mexico,2008:140-149.

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Last Update: 2017-09-30