轨迹数据的非关系管理及相似性分析

doi:10.13474/j.cnki.11-2246.2020.0186

测绘通报 ›› 2020, Vol. 0 ›› Issue (6): 81-86.doi: 10.13474/j.cnki.11-2246.2020.0186

轨迹数据的非关系管理及相似性分析

黄亚锋¹, 向隆刚², 高萌²

1. 南京电子技术研究所, 江苏南京 210039;
2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2019-09-27 修回日期:2020-03-23 出版日期:2020-06-25 发布日期:2020-07-01
作者简介:黄亚锋(1981-),男,博士,高级工程师,研究方向为轨迹数据处理。E-mail:hexabasic@163.com
基金资助:
南宁市科技计划项目（20175032）

Management and similarity analysis of trajectories with NoSQL database

HUANG Yafeng¹, XIANG Longgang², GAO Meng²

1. Nanjing Research Institute of Electronics Technology, Nanjing 210039, China;
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2019-09-27 Revised:2020-03-23 Online:2020-06-25 Published:2020-07-01

摘要/Abstract

摘要： 针对高动态、半结构化的轨迹数据，充分利用文档型非关系数据库MongoDB的特性，本文首先提出了一套分层、分区、分片的存储策略，设计了以整条轨迹为基本粒度的非关系组织模型，能够有效应对轨迹数据的海量性和动态性挑战。然后据此开展轨迹相似性分析的研究，提出了一种兼顾时间维和轨迹形状的轨迹相似性度量方法DTWEUCLI，可计算长短不一且含有噪声的轨迹数据之间的相似性。最后基于轨迹的非关系存储和相似性计算，开展了轨迹簇生成的试验与分析，设计实现了基于轨迹相似性计算的轨迹聚类计算框架。基于3个轨迹数据集的试验表明，DTWEUCLI算法能够对多源轨迹数据集进行有效聚类，输出轨迹簇。

关键词: 轨迹数据, 非关系管理, 相似性分析, 聚类簇, MongoDB

Abstract: Based on the dynamic and semi-structured trajectory data, this paper makes full use of the characteristics of the document-type non-relational database MongoDB, proposes a layered, partitioned and sliced storage strategy, and designs a non-relational organization with the whole trajectory as the basic unit. The model can effectively cope with the massive and dynamic challenges of trajectory data. On this basis, this paper studies the trajectory similarity calculation, and proposes a trajectory similarity measurement method DTWEUCLI that takes into account both time information and trajectory shapes, which can effectively calculate the similarity between trajectory data with different lengths and noises. Finally, based on trajectory-based non-relational storage and similarity calculation, this paper carries out the experiment and analysis of trajectory cluster. The trajectory cluster computing framework based on trajectory similarity calculation is proposed. Experiments based on three data sets show that the DTWEUCLI algorithm can effectively calculate the trajectory cluster of multi-source trajectory data sets and export the trajectory cluster.

Key words: trajectory data, NoSQL management, similarity analysis, clustering group, MongoDB

中图分类号:

P208

黄亚锋, 向隆刚, 高萌. 轨迹数据的非关系管理及相似性分析[J]. 测绘通报, 2020, 0(6): 81-86.

HUANG Yafeng, XIANG Longgang, GAO Meng. Management and similarity analysis of trajectories with NoSQL database[J]. Bulletin of Surveying and Mapping, 2020, 0(6): 81-86.

参考文献

[1] ALEXANDER F J, HOISIE A, SZALAY A S, et al. Big Data[J]. Computing in Science and Engineering, 2011, 13(6):10-13.
[2] Wikiloc Company. Trails of the world[EB/OL].2019-03-15[2020-03-21].https://www.wikiloc.com/.
[3] CATTELL R. Scalable SQL and NoSQL data stores[J]. Acm Sigmod Record, 2011, 39(4):12-27.
[4] BREWER E A. CAP twelve years later:how the "rules" have changed[J]. IEEE Computer, 2012, 45(2):23-29.
[5] PRITCHETT D. Base:an acid alternative[J].Queue, 2008,6(3):48-55.
[6] AGRAWAL R, FALOUTSOS C, SWAMI A. Efficient similarity search in sequence databases[C]//Inter-national Conference on Foundations of Data Organization and Algorithms.Chicago:Springer Verlag,1993:69-84.
[7] LEE S, CHUN S, KIM D, et al. Similarity search for multidimensional data sequences[C]//Proceedings of the 16th International Conference on Data Engineering. Washington D.C.:IEEE Computer Society, 2000:599-608.
[8] GUAN B, LIU L, CHEN J. Using relative distance and Hausdorff distance to mine trajectory clusters[J]. Telkomnika Indonesian Journal of Electrical Engineering, 2013, 11(1):115-122.
[9] LEE J, HAN J, WHANG K, et al. Trajectory clustering:a partition-and-group framework[C]//Proceedings of the 2007 ACM SIGMOD international conference on Management of data.Beijing:Association for Computing Machinery,2007:593-604.
[10] SANKOFF D, KRUSKAL J. Time warps, string edits, and macromolecules:the theory and practice of sequence comparison[M]. MA:Addison-Wesley, 1983.
[11] VLACHOS M, KOLLIOS G, GUNOPULOS D. Discovering similar multidimensional trajectories[C]//Proceedings of the 18th International Conference on Data Engineering. Washington DC:IEEE Computer Society,2002:673-684.
[12] CHEN L, ÖZSU M T, ORIA V. Robust and fast similarity search for moving object trajectories[C]//Proceedings of the 2005 ACM SIGMOD international conference on Management of data. New York:Association for Computing Machinery, 2005:491-502.
[13] BENHAMOU S. How to reliably estimate the tortuosity of an animal's path:straightness, sinuosity, or fractal dimension[J]. Journal of Theoretical Biology, 2004, 229(2):209-20.
[14] SOKAL R R. A statistical method for evaluating systematic relationships[J]. University of Kansas Sci Bull, 1958, 38(2):1409-1438.
[15] BESSE P C, GUILLOUET B, LOUBES J M, et al. Review and perspective for distance-based clustering of vehicle trajectories[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(11):3306-3317.

轨迹数据的非关系管理及相似性分析

Management and similarity analysis of trajectories with NoSQL database

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