research-article

Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories

Authors:

Ickjai LeeAuthors Info & Claims

Volume 122, Issue C

Pages 85 - 101

https://doi.org/10.1016/j.eswa.2018.12.047

Published: 15 May 2019 Publication History

Highlights

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Propose a way to find hierarchical periodic patterns from irregular trajectories.

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Incorporate spatiality, temporality, and semantics to find reference spots.

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Provide experimental results supporting the efficiency of proposed method.

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Provide experimental results supporting the effectiveness of proposed method.

Abstract

A large number of spatio-temporal trajectory data is being generated from GPS enabled devices such as cars, smartphones, and sensors. These trajectory datasets representing objects’ movements provide new opportunities for enhanced spatio-temporal periodic pattern mining. These GPS collected trajectory datasets represent real-world movement phenomena and thus they are spatially placed, temporally recorded, aspatial semantically meaningful, hierarchically structured, and irregularly sampled. Periodic pattern mining from spatio-temporal trajectories is to find temporal regularities from these spatio-temporal trajectories, and thus must take into these five characterisics into account in order not to miss any spatio-temporally, semantically and hierarchically meaningful patterns from irregularly sampled spatio-temporal trajectories. Traditional periodic pattern mining fails to consider these five conditions simultaneously, and in this paper, we propose a hierarchical clustering based semantic periodic pattern mining to consider the five aspects: spatiality, temporality, semantics, hierarchy, and irregularity. Experimental results demonstrate the effectiveness of our proposed method against traditional periodic pattern mining approaches.

References

[1]

S. Bar-David, I. Bar-David, P.C. Cross, S.J. Ryan, C.U. Knechtel, W.M. Getz, Methods for assessing movement path recursion with application to african buffalo in south africa, Ecology 90 (2009) 2467–2479,.

[2]

L. Bermingham, I. Lee, A framework of spatio-temporal trajectory simplification methods, International Journal of Geographical Information Science 31 (6) (2017) 1128–1153.

[3]

R.J.G.B. Campello, D. Moulavi, J. Sander, Density-based clustering based on hierarchical density estimates., in: J. Pei, V.S. Tseng, L. Cao, H. Motoda, G. Xu (Eds.), PAKDD (2), in: Lecture Notes in Computer Science, 7819, Springer, 2013, pp. 160–172.

[4]

H. Cao, D.W. Cheung, N. Mamoulis, Discovering partial periodic patterns in discrete data sequences, in: H. Dai, R. Srikant, C. Zhang (Eds.), Advances in knowledge discovery and data mining, Springer Berlin Heidelberg, 2004, pp. 653–658.

[5]

H. Cao, N. Mamoulis, D.W. Cheung, Discovery of periodic patterns in spatiotemporal sequences, IEEE Transactions on Knowledge and Data Engineering 19 (4) (2007) 453–467,.

Digital Library

[6]

H. Cao, N. Mamoulis, D.W. Cheung, Discovery of periodic patterns in spatiotemporal sequences, IEEE Transactions on Knowledge and Data Engineering 19 (4) (2007) 453–467,.

Digital Library

[7]

M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of the second international conference on knowledge discovery and data mining, KDD’96, AAAI Press, 1996, pp. 226–231.

[8]

S. Halder, M. Samiullah, Y.-K. Lee, Supergraph based periodic pattern mining in dynamic social networks, Expert Systems with Applications 72 (2017) 430–442.

[9]

K.-Y. Huang, C.-H. Chang, Mining periodic patterns in sequence data, Springer Berlin Heidelberg, 2004, pp. 401–410.

[10]

J. Han, G. Dong, Y. Yin, Efficient mining of partial periodic patterns in time series database, Proceedings of the 15th international conference on data engineering, ICDE ’99, IEEE Computer Society, 1999, p. 106.

[11]

A.K. Jain, R.C. Dubes, Algorithms for clustering data, Prentice-Hall, Inc., 1988.

Digital Library

[12]

T. Jindal, P. Giridhar, L.-A. Tang, J. Li, J. Han, Spatiotemporal periodical pattern mining in traffic data, Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, UrbComp ’13, ACM, 2013.

[13]

H. Kargupta, J. Srivastava, C. Kamath, A. Goodman, Proceedings of the 2005 siam international conference on data mining - on periodicity detection and structural periodic similarity, 2005, pp. 449–460,.

[14]

S. Kullback, R.A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics 22 (1951) 79–86,.

[15]

M. Lahiri, T.Y. Berger-Wolf, Periodic subgraph mining in dynamic networks, Knowledge and Information Systems 24 (3) (2010) 467–497.

[16]

J.-G. Lee, J. Han, K.-Y. Whang, Trajectory clustering: A partition-and-group framework, Proceedings of the 2007 ACM SIGMOD international conference on management of data, SIGMOD ’07, ACM, 2007, pp. 593–604,.

Digital Library

[17]

Z. Li, B. Ding, J. Han, R. Kays, P. Nye, Mining periodic behaviors for moving objects, Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’10, ACM, 2010, pp. 1099–1108,.

Digital Library

[18]

Z. Li, J. Han, Mining periodicity from dynamic and incomplete spatiotemporal data, W.W. Chu (Ed.), Springer Berlin Heidelberg, 2014, pp. 41–81.

[19]

Z. Li, J. Han, B. Ding, R. Kays, Mining periodic behaviors of object movements for animal and biological sustainability studies, Data Mining and Knowledge Discovery 24 (2) (2012) 355–386,.

Digital Library

[20]

Z. Li, J. Han, M. Ji, L.-A. Tang, Y. Yu, B. Ding, et al., Movemine: Mining moving object data for discovery of animal movement patterns, ACM Transactions on Intelligent Systems and Technology 2 (4) (2011),.

Digital Library

[21]

N.R. Lomb, Least-squares frequency analysis of unequally spaced data, Astrophysics and Space Science 39 (1976) 447–462,.

[22]

M. Lv, L. Chen, Z. Xu, Y. Li, G. Chen, The discovery of personally semantic places based on trajectory data mining, Neurocomputing 173 (P3) (2016) 1142–1153.

[23]

S. Parthasarathy, S. Mehta, S. Srinivasan, Robust periodicity detection algorithms, Proceedings of the 15th ACM international conference on information and knowledge management, CIKM ’06, ACM, 2006, pp. 874–875,.

Digital Library

[24]

L. Rabiner, B. Juang, An introduction to hidden markov models, IEEE ASSP Magazine 3 (1986) 4–16,.

[25]

J.D. Scargle, Studies in astronomical time series analysis. ii - statistical aspects of spectral analysis of unevenly spaced data, The Astrophysical Journal 263 (1982) 835–853,.

[26]

C. Sheng, W. Hsu, M.L. Lee, Mining dense periodic patterns in time series data, Proceedings of the 22nd international conference on data engineering, ICDE ’06, IEEE Computer Society, 2006, p. 115,.

Digital Library

[27]

A. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory 13 (1967) 260–269,.

Digital Library

[28]

B.J. Worton, Kernel methods for estimating the utilization distribution in home-range studies, Ecology 70 (1989) 164–168,.

[29]

Yang, J., Wang, W., & Yu, P. S. (2000). Mining asynchronous periodic patterns in time series data. (pp. 275–279). https://doi.org/10.1145/347090.347150.

[30]

J.J.-C. Ying, W.-C. Lee, T.-C. Weng, V.S. Tseng, Semantic trajectory mining for location prediction, Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’11, ACM, 2011, pp. 34–43.

[31]

D. Zhang, K. Lee, I. Lee, Hierarchical trajectory clustering for spatio-temporal periodic pattern mining, Expert Systems with Applications 92 (C) (2018) 1–11,.

Digital Library

[32]

D. Zhang, K. Lee, I. Lee, Semantic periodic pattern mining from spatio-temporal trajectories, Information Sciences (2018).

[33]

M. Zhang, B. Kao, D.W. Cheung, K.Y. Yip, Mining periodic patterns with gap requirement from sequences, ACM Transactions on Knowledge Discovery from Data 1 (2) (2007),.

Digital Library

[34]

Y.L. Zhu, S.J. Li, N.N. Bao, D.S. Wan, Mining approximate periodic pattern in hydrological time series, Journal of Computational Information Systems 9 (2012) 515.

Cited By

Gudavalli CZhang BLevenson CLore KManjunath B(2024)ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of LifeProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698452(43-51)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681765.3698452
Kim HKim HKim SKim HCho MVo BChun-Wei Lin JYun U(2023)An advanced approach for incremental flexible periodic pattern mining on time-series dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120697230:COnline publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120697
He LNiu XChen TMei KLi M(2022)Spatio-temporal trajectory anomaly detection based on common sub-sequenceApplied Intelligence10.1007/s10489-021-02754-z52:7(7599-7621)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10489-021-02754-z
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Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories
1. Human-centered computing

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 122, Issue C

May 2019

406 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © 2018.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 May 2019

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Cited By

Gudavalli CZhang BLevenson CLore KManjunath B(2024)ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of LifeProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698452(43-51)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681765.3698452
Kim HKim HKim SKim HCho MVo BChun-Wei Lin JYun U(2023)An advanced approach for incremental flexible periodic pattern mining on time-series dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120697230:COnline publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120697
He LNiu XChen TMei KLi M(2022)Spatio-temporal trajectory anomaly detection based on common sub-sequenceApplied Intelligence10.1007/s10489-021-02754-z52:7(7599-7621)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10489-021-02754-z
Fournier-Viger PWang YYang PLin JYun UKiran R(2022)TSPIN: mining top-k stable periodic patternsApplied Intelligence10.1007/s10489-020-02181-652:6(6917-6938)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10489-020-02181-6
Belcastro LMarozzo FPerrella E(2021)Automatic detection of user trajectories from social media postsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115733186:COnline publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115733

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