Management and similarity analysis of trajectories with NoSQL database

doi:10.13474/j.cnki.11-2246.2020.0186

Abstract

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

CLC Number:

P208

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.

References

[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.