CFP last date
20 January 2025
Reseach Article

Modeling, Mining, and Analyzing Semantic Trajectories: The Process to Extract Meaningful Behaviors of Moving Objects

by Sana Chakri, Said Raghay, Salah El Hadaj
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 8
Year of Publication: 2015
Authors: Sana Chakri, Said Raghay, Salah El Hadaj
10.5120/ijca2015905542

Sana Chakri, Said Raghay, Salah El Hadaj . Modeling, Mining, and Analyzing Semantic Trajectories: The Process to Extract Meaningful Behaviors of Moving Objects. International Journal of Computer Applications. 124, 8 ( August 2015), 15-21. DOI=10.5120/ijca2015905542

@article{ 10.5120/ijca2015905542,
author = { Sana Chakri, Said Raghay, Salah El Hadaj },
title = { Modeling, Mining, and Analyzing Semantic Trajectories: The Process to Extract Meaningful Behaviors of Moving Objects },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 8 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number8/22122-2015905542/ },
doi = { 10.5120/ijca2015905542 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:50.665288+05:30
%A Sana Chakri
%A Said Raghay
%A Salah El Hadaj
%T Modeling, Mining, and Analyzing Semantic Trajectories: The Process to Extract Meaningful Behaviors of Moving Objects
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 8
%P 15-21
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mobile devices leave a huge number of digital traces that are collected as trajectories, describing the movement of its users or a path followed by any moving object in geographical space over some period of time. However, those mobile devices provide just raw trajectories (x, y, t), ignoring information about their related contextual data, these additional data contribute in producing significant knowledge about movements and provide applications with richer and more meaningful knowledge. Therefore, researchers focus on transforming raw trajectories into semantic trajectories by combining the raw mobility tracks with related contextual data and creating a new type of trajectories called “semantic trajectories”, then applying mining techniques. This paper study closely the current researches on modeling and mining semantic trajectories so far, and try to investigate by proposing a descriptive schema including all steps that users can browse from the construction of the trajectories to the analyze of behaviors extracted.

References
  1. Van Hage W R, Wielemaker J, and Schreiber G 2010 The space package: Tight integration between space and semantics. Transactions in GIS 14: 131–46
  2. Parent C, Pelekis N, Theodoridis Y, Yan Z, Spaccapietra S, Renso C, Andrienko G, Andrienko N, Bogorny V, Damiani ML, Gkoulalas-Divanis A, Macedo J (2013) Semantic trajectories modeling and analysis. ACM Comput Surv 45:1–32
  3. 8. Yan Z, Spaccapietra S (2009). Towards semantic trajectory data analysis: a conceptual and computational approach. VLDB PhD Work 15(2):165– 190
  4. Luis Otavio Alvares , Vania Bogorny, Bart Kuijpers, Bart Moelans, Jose Antonio Fernandes de Macedo, Andrey Tietbohl Palma. Towards Semantic Trajectory Knowledge Discovery. Databases and Theoretical Computer Science 2007
  5. Basma H. Albanna, Ibrahim F. Moawad, Sherin M. Moussa, Mahmoud A. Sakr. Semantic Trajectories: A Survey from Modeling to Application. Information Fusion and Geographic Information Systems (IF&GIS' 2015) Lecture Notes in Geoinformation and Cartography 2015, pp 59-76
  6. Gudmundsson, J. Vankrevled, M. and Speckmann, B. 2007. Efficient detection of patterns in 2d trajectories of moving points. Geoinformatica 11, 195–215
  7. Benkert, M., Gudmundsson, J., Hubner, F., and Wolle, T. 2008. Reporting flock patterns. Comp. Geom. 41, 111–125.
  8. H. Fan, Q. Wu and Y. Lin. Behavior-Based Cleaning for Unreliable RFID Data Sets. Sensors, 2012, 12: 10196-10207.
  9. Jun, J, Guensler, R., and Ogle, J. 2006. Smoothing methods to minimize impact of global positioning system random error on travel distance, speed, and acceleration profile estimates. Transport. Res. Rec. J. Transport. Res. Board 1972, 1, 141–150.
  10. Quddus, M. A., Ochieng, W. Y., and Noland, R. B. 2007. Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transport. Res. Part C: Emerging Technol. 15, 5, 312–328
  11. Schmid, F., Richter, K.-F., and Laube, P. 2009. Semantic trajectory compression. In Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases. 411–416.
  12. Spaccapietra, S., Parent, C., Damiani, M. L., Macedo, J. A., Porto, F., and Vangenot, C. 2008. A conceptual view on trajectories. Data Knowl. Engin. 65, 126–146
  13. Alvares, L. O., Bogorny, V., Kuijpers, B., Demacedo, J. A. F., Moelans, B., and Vaisman, A. 2007. A model for enriching trajectories with semantic geographical information. In Proceedings of the 15th ACM International Symposium on Advances in Geographic Information Systems (ACM-GIS’07). 162–169, ACM Press, New York.
  14. Palma, A. T; Bogorny, V.; Kuijpers, B.; Alvares, L.O. A Clustering-based Approach for Discovering Interesting Places in Trajectories. In: 23rd Annual Symposium on Applied Computing, (ACM-SAC'08), Fortaleza, Ceara, 16-20 March (2008) Brazil. pp. 863-868
  15. Nanni, M., Pedreschi, D. 2006. Time-focused clustering of trajectories of moving objects. Journal of Intelligent Information Systems 27(3) (2006) 267–289
  16. Manso, J. A. ; Times, V. C. ; Oliveira, G. ; Alvares, Luis Otavio ; Bogorny, V. DB-SMoT: A Direction-Based Spatio-Temporal Clustering Method. In: IEEE International Conference on Intelligent Systems (IS), 2010, Londres. Proceedings of the IEEE International Conference on Intelligent Systems.
  17. Z., Chakraborty, D., Parent, C., Spaccapietra, S., and Aberer, K. 2011. SeMiTri: A framework for semantic annotation of heterogeneous trajectories. In Proceedings of the 14th International Conference on Extending Database Technology (EDBT’11). 259–270
  18. Zheng Y, Chen Y, Li Q, Xie X, Ma W-Y (2010)
  19. Understanding transportation modes based on GPS data for web applications. ACM Trans Web 4(1)
  20. Yan Z, Spremic L, Chakraborty D, Parent C, Spaccapietra S, Aberer K (2010) Automatic construction and multi-level visualization of semantic trajectories. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems—GIS ’ 10, vol 12, p 524.
  21. Xiu-Li Z, Wei-Xiang X (2009) A clustering-based approach for discovering interesting places in a single trajectory. In: 2009 second international conference on intelligent computation technology and automation, pp 429–432
  22. Moreno B, Times V, Renso C, Bogorny V (2010) Looking inside the stops of trajectories of moving objects. In: Geoinfo
  23. Zhixian Y (2011) Semantic trajectories: computing and understanding mobility data In: Ph.D. dissertation, Swiss Federal Institute of Technology, Information and Communication Dept., Lausanne
  24. Ying JJ-C, Lee W-C, Tseng VS (2013) Mining geographic-temporal—semantic patterns in trajectories for location prediction. ACM Trans Intell Syst Techno l 5
  25. Ilarri S, Stojanovic D, Ray C (2015) Semantic management of moving objects: a vision towards smart mobility. Expert Syst Appl 42(3):1418– 1435
  26. Han, B., LIU, L., and Omiecinski, E. 2012. NEAT: Road network aware trajectory clustering. In Proceedings of the 32nd International Conference on Distributed Computing Systems (ICDS’12). 142–151.
  27. Panagiotakis, C., Pelekis, N., Kopanakis, I., Ramasso, E., and Theodoridis, Y. 2012. Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans. Knowl. Data Engin 24, 7, 1328– 1343.
  28. Rinvizillo, S., Pedreschi, D., nanni, M., Giannotti, F., Aandrieko, N., and Andrieko, G. 2008. Visually–driven analysis of movement data by progressive clustering. Inf. Vis. 7, 3–4, 225–239.
  29. Pelekis, N., Kopanakis, I., Kotsifakos, E. E., Frentzos, E., and Theodoridis, Y. 2011. Clustering uncertain trajectories. Knowl. Inf. Syst. 28, 1, 117–147.
  30. Alvares L, Palma A, Oliveira G, Bogorny V (2010) Weka-STPM: from trajectory samples to semantic trajectories. In: Proceedings of the Workshop Open Source Code, vol 1.
  31. V Bogorny, H Avancini, B C de Paula, C R Kuplich and L O Alvares. Weka-STPM: a Software Architecture and Prototype for Semantic Trajectory Data Mining and Visualization Transactions in GIS, 2011, 15(2): 227–248
  32. Li Z, Ji M, Lee J, Tang L, Yu Y, Han J, Kays R (2010) MoveMine. In: Proceedings of the 2010 international conference on management of data—SIGMOD ’ 10, p 1203
  33. Bogorny V, Kuijpers B, and Alvares L O 2009 St-dmql: A semantic trajectory data mining query language. International Journal of Geographical Information Science 23: 1245–76
Index Terms

Computer Science
Information Sciences

Keywords

Semantic trajectories extracting knowledge semantic enrichment spatial data mining.