Efficient Semantic Enrichment Process for Spatiotemporal Trajectories
Abstract
The increasing availability of location-acquisition technologies has enabled collecting large-scale spatiotemporal trajectories, from which we can derive semantic information in urban environments, including location, time, direction, speed, and point of interest. Such semantic information can give us a semantic interpretation of movement behaviors of moving objects. However, existing semantic enrichment process approaches, which can produce semantic trajectories, are generally time-consuming. In this paper, we propose an efficient semantic enrichment process framework to annotate spatiotemporal trajectories by using geographic and application domain knowledge. The framework mainly includes preannotated semantic trajectory storage phase, spatiotemporal similarity measurement phase, and semantic information matching phase. Having observed the common trajectories in the same geospatial object scenes, we propose a semantic information matching algorithm to match semantic information in preannotated semantic trajectories to new spatiotemporal trajectories. In order to improve the efficiency of this approach, we build a spatial index to enhance the preannotated semantic trajectories. Finally, the experimental results based on a real dataset demonstrate the effectiveness and efficiency of our proposed approaches.
References
[1]
D. Daowd and S. Mallappa, “Semantic analysis techniques using twitter datasets on big data: comparative analysis study,” Computer Systems Science and Engineering, vol. 35, no. 6, pp. 495–512, 2020.
[2]
L. H. Qi, R. G. Chen, and X. Wen, “Research on the LBS matching based on stay point of the semantic trajectory,” Journal of Geo-Information Science, vol. 16, no. 5, pp. 720–726, 2014.
[3]
A. Hussain, B. N. Keshavamurthy, and R. Prasad, “Accurate location prediction of social-users using mHMM,” Intelligent Automation & Soft Computing, vol. 25, no. 3, pp. 473–486, 2019.
[4]
F. Zhu, J. Gao, and C. Xu, “On selecting effective patterns for fast support vector regression training,” IEEE transactions on neural networks and learning systems, vol. 29, no. 8, pp. 3610–3622, 2018.
[5]
T. Bry and T. Fureche, “Web and semantic web query languages: a survey,” Reasoning Web, Msida, Malta: Computer Science, vol. 3564, pp. 35–133, 2005.
[6]
Z. X. Yan, D. Chakraborty, and C. Parent, “Semantic trajectories,” ACM TIST, vol. 4, no. 3, pp. 1–38, 2013.
[7]
C. Parent, S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko, V. Bogorny, M. L. Damiani, A. Gkoulalas-Divanis, J. Macedo, N. Pelekis, Y. Theodoridis, and Z. Yan, “Semantic trajectories modeling and analysis,” ACM Computing Surveys, vol. 45, no. 4, pp. 1–32, 2013.
[8]
A. Daniel and S. Thad, “Using GPS to learn significant locations and predict movement across multiple users,” Personal and Ubiquitous Computing, vol. 7, no. 5, pp. 275–286, 2003.
[9]
K. John and H. Eric, “Predestination: inferring destinations from partial trajectories,” in International Conference on Ubiquitous Computing, pp. 243–260, Orange County, CA, USA, 2006.
[10]
A. T. Palma, V. Bogorny, B. Kuijpers, and L. O. Alvares, “A clustering-based approach for discovering interesting places in trajectories,” in Proceedings of the 2008 ACM symposium on Applied computing - SAC '08, pp. 863–868, Fortaleza, Ceara, Brazil, 2008.
[11]
Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W.-Y. Ma, “Recommending friends and locations based on individual location history,” ACM Transactions on the Web, vol. 5, no. 1, pp. 1–44, 2011.
[12]
S. Spaccapietra, C. Parent, M. L. Damiani, J. A. de Macedo, F. Porto, and C. Vangenot, “A conceptual view on trajectories,” Data & Knowledge Engineering, vol. 65, no. 1, pp. 126–146, 2008.
[13]
M. Baglioni, J. Macêdo, and C. Renso, “Towards semantic interpretation of movement behavior,” in 12th AGILE Conference Advances in GIS, pp. 271–288, Hannover, 2009.
[14]
A. Vandecasteele, R. Devillers, and A. Napoli, “From movement data to objects behavior using semantic trajectory and semantic events,” Marine Geodesy, vol. 37, no. 2, pp. 126–144, 2014.
[15]
P. T. Nogueira, R. B. Braga, and H. Martin, “An ontology- based approach to represent trajectory characteristics,” in The 5th International Conference on Computing for Geospatial Research and Application, pp. 102–107, USA, 2014.
[16]
T. P. Nogueira and H. Martin, “Qualitative representation of dynamic attributes of trajectories,” in 17th AGILE Conference on Geographic Information Science, Castellón, Spain, 2014.
[17]
T. P. Nogueira, R. B. Braga, and C. T. Oliveira, “FrameSTEP: a framework for annotating semantic trajectories based on episodes,” Expert Systems with Applications, vol. 92, pp. 533–545, 2018.
[18]
L. G. Xiang, T. Wu, and J. Y. Gong, “A geo-spatial information oriented trajectory model and spatio-temporal pattern quering,” Acta Geodactica et Catographica Sinica, vol. 43, no. 9, pp. 982–988, 2014.
[19]
T. Sun, Z. Huang, H. Zhu, Y. Huang, and P. Zheng, “Congestion pattern prediction for a busy traffic zone based on the hidden Markov model,” IEEE Access, vol. 9, pp. 2390–2400, 2021.
[20]
E. Martin, K. P. Hans, and S. Jorg, “A density-based algorithm for discovering clusters in large spatial databases with noise,” KDD, Portland, Oregon, USA, 1996.
[21]
D. Mountain and J. Raper, “Modelling human spatio-temporal behaviour: a challenge for location-based services,” in Proceedings of 6th International Conference on Geocomputation, The University of Queensland, Brisbane, Australia, 2001.
[22]
M. P. Dubuisson and A. K. Jain, “A modified Hausdorff distance for object matching,” in Proceedings of 12th international conference on pattern recognition, pp. 566–568, Jerusalem, Israel, 1994.
[23]
J. Kima and S. Mahmassanibhan, “Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories,” Symposium on Transportation and Traffic Theory, vol. 9, pp. 164–184, 2015.
[24]
M. M. Fréchet, “Sur quelques points du calcul fonctionnel,” Rendiconti del Circolo Matematico di Palermo (1884-1940), vol. 22, no. 1, pp. 1–72, 1906.
[25]
Z. Chen and H. T. Shen, “Searching trajectories by locations: an efficiency study,” in Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 255–266, Indianapolis, Indiana, USA, 2010.
[26]
F. Zhu, J. Yang, J. Gao, C. Xu, S. Xu, and C. Gao, “Finding the samples near the decision plane for support vector learning,” Information Sciences, vol. 382-383, pp. 292–307, 2017.
[27]
A. Guttman, “R-trees: a dynamic index structure for spatial searching,” in Proceedings of the 1984 ACM SIGMOD international conference on Management of data, pp. 47–57, Boston, Massachusetts, USA, 1984.
[28]
R. Kanth, S. Ravada, and D. Abugov, “Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data,” in Proceedings of the 2002 ACM SIGMOD international conference on Management of data, pp. 546–557, Madison, Wisconsin, USA, 2002.
[29]
X. F. Xu, L. Xiong, and V. S. Sunderam, “D-Grid: Abn in-memory dual space grid index for moving object databases,” in 2016 17th IEEE International Conference on Mobile Data Management (MDM), pp. 252–261, Porto, Portugal, 2016.
Recommendations
Comments
Information & Contributors
Information
Published In
Copyright © 2021 Bin Zhao et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
John Wiley and Sons Ltd.
United Kingdom
Publication History
Published: 01 January 2021
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Feb 2025