Regions Trajectories Data: Evolution of Modeling and Construction Methods

  • Marwa MassaâbiEmail author
  • Olfa Layouni
  • Assawer Zekri
  • Mohammad Aljeaid
  • Jalel Akaichi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)


Tracking movement and trajectory data analysis are very important in the era of sensor devices and technological evolution. The movement can be produced by an object represented by a point, a line or a region. The region can be in movement, but its movement is special in some way because it changes its position, shape and extent unpredictably when moving (such as tumors, massive rainfalls, etc.). However, representing moving regions trajectories without interfering or modifying their unstable aspect is more or less ignored by the most recent literature. Therefore, this paper investigates trajectories evolutions, construction and modeling techniques, in order to highlight the gap concerning regions’ trajectory. Subsequently, we focus on regions types and their trajectories modeling techniques.


Moving region Modeling Trajectory Construction 


  1. 1.
    Bogorny, V., Heuser, C.A., Alvares, L.O.: A conceptual data model for trajectory data mining. In: Geographic Information Science, pp. 1–15. Springer (2010)Google Scholar
  2. 2.
    Bogorny, V., Renso, C., Aquino, A.R., Lucca Siqueira, F., Alvares, L.O.: Constant-a conceptual data model for semantic trajectories of moving objects. Trans. GIS 18(1), 66–88 (2014)CrossRefGoogle Scholar
  3. 3.
    Cetateanu, A., Luca, B.A., Popescu, A.A., Page, A., Cooper, A., Jones, A.: A novel methodology for identifying environmental exposures using GPS data. Int. J. Geogr. Inf. Sci. 30(10), 1–17 (2016)CrossRefGoogle Scholar
  4. 4.
    Chakri, S., Raghay, S., et al.: Enriching trajectories with semantic data for a deeper analysis of patterns extracted. In: International Conference on Hybrid Intelligent Systems, pp. 209–218. Springer (2016)Google Scholar
  5. 5.
    Damiani, M.L., Valdes, F., Issa, H.: Moving objects beyond raw and semantic trajectories. In: Proceedings of the 3rd lnternational workshop on Information Management for Mobile Applications (IMMoA 2013), Riva del Garda, Italy. Citeseer (2013)
  6. 6.
    Erwig, M., Güting, R.H., Schneider, M., Vazirgiannis, M.: Spatio-temporal data types: An approach to modeling and querying moving objects in databases. Geoinformatica 3(3), 269–296 (1999)CrossRefGoogle Scholar
  7. 7.
    Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases, vol. 29. ACM (2000)Google Scholar
  8. 8.
    Gong, L., Sato, H., Yamamoto, T., Miwa, T., Morikawa, T.: Identification of activity stop locations in gps trajectories by density-based clustering method combined with support vector machines. J. Mod. Transp. 23(3), 202–213 (2015)CrossRefGoogle Scholar
  9. 9.
    Gudmundsson, J., van Kreveld, M.: Computing longest duration flocks in trajectory data. In: Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems, pp. 35–42. ACM (2006)Google Scholar
  10. 10.
    Güting, R.H., Böhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Trans. Database Syst. (TODS) 25(1), 1–42 (2000)CrossRefGoogle Scholar
  11. 11.
    Güting, R.H., De Ridder, T., Schneider, M.: Implementation of the rose algebra: Efficient algorithms for realm-based spatial data types. In: Advances in Spatial Databases, pp. 216–239. Springer (1995)Google Scholar
  12. 12.
    Güting, R.H., Schneider, M.: Realm-based spatial data types: The rose algebra. VLDB J.-Int. J. Very Large Data Bases 4(2), 243–286 (1995)CrossRefGoogle Scholar
  13. 13.
    Hu, Y., Janowicz, K., Carral, D., Scheider, S., Kuhn, W., Berg-Cross, G., Hitzler, P., Dean, M., Kolas, D.: A geo-ontology design pattern for semantic trajectories. In: Spatial Information Theory, pp. 438–456. Springer (2013)Google Scholar
  14. 14.
    Huang, Y., Chen, C., Dong, P.: Modeling herds and their evolvements from trajectory data. In: Geographic Information Science, pp. 90–105. Springer (2008)Google Scholar
  15. 15.
    Junghans, C., Gertz, M.: Modeling and prediction of moving region trajectories. In: Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming, pp. 23–30. ACM (2010)Google Scholar
  16. 16.
    Lema, J.A.C., Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: Algorithms for moving objects databases. Comput. J. 46(6), 680–712 (2003)CrossRefzbMATHGoogle Scholar
  17. 17.
    Ma, Z., Zhang, F., Yan, L.: Fuzzy information modeling in uml class diagram and relational database models. Appl. Soft Comput. 11(6), 4236–4245 (2011)CrossRefGoogle Scholar
  18. 18.
    Massaâbi, M., Akaichi, J.: Modeling moving regions: Colorectal cancer case study. In: Intelligent Interactive Multimedia Systems and Services 2016, pp. 417–426. Springer (2016)Google Scholar
  19. 19.
    Olsen, B., McKenney, M.: Storm system database: A big data approach to moving object databases. In: 2013 Fourth International Conference on Computing for Geospatial Research and Application (COM. Geo), pp. 142–143. IEEE (2013)Google Scholar
  20. 20.
    Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., Bogorny, V., Damiani, M.L., Gkoulalas-Divanis, A., Macedo, J., Pelekis, N., et al.: Semantic trajectories modeling and analysis. ACM Comput. Surv. (CSUR) 45(4), 42 (2013)CrossRefGoogle Scholar
  21. 21.
    Schneider, M.: Uncertainty management for spatial datain databases: Fuzzy spatial data types. In: Advances in Spatial Databases, pp. 330–351. Springer (1999)Google Scholar
  22. 22.
    Schneider, M.: Metric operations on fuzzy spatial objects in databases. In: Proceedings of the 8th ACM International Symposium on Advances in Geographic Information Systems, pp. 21–26. ACM (2000)Google Scholar
  23. 23.
    Schneider, M.: Design and implementation of finite resolution crisp and fuzzy spatial objects. Data Knowl. Eng. 44(1), 81–108 (2003)CrossRefzbMATHGoogle Scholar
  24. 24.
    Schneider, M.: Fuzzy spatial data types for spatial uncertainty management in databases. In: Handbook of Research on Fuzzy Information Processing in Databases, vol. 2, pp. 490–515 (2008)Google Scholar
  25. 25.
    Singh, S., Agarwal, K., Ahmad, J.: Conceptual modeling in fuzzy object-oriented databases using unified modeling language. Int. J. Latest Res. Sci. Technol. 3, 174–178 (2014)Google Scholar
  26. 26.
    Spaccapietra, S., Parent, C., Damiani, M.L., de Macedo, J.A., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)CrossRefGoogle Scholar
  27. 27.
    Tøssebro, E., Güting, R.H.: Creating representations for continuously moving regions from observations. In: Advances in Spatial and Temporal Databases, pp. 321–344. Springer (2001)Google Scholar
  28. 28.
    Wang, Z., Zlatanova, S., Moreno, A., van Oosterom, P., Toro, C.: A data model for route planning in the case of forest fires. Comput. Geosci. 68, 1–10 (2014)CrossRefGoogle Scholar
  29. 29.
    Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semantic trajectories: Mobility data computation and annotation. ACM Trans. Intell. Syst. Technol. (TIST) 4(3), 49 (2013)Google Scholar
  30. 30.
    Yan, Z., Giatrakos, N., Katsikaros, V., Pelekis, N., Theodoridis, Y.: Setrastream: Semantic-aware trajectory construction over streaming movement data. In: Advances in Spatial and Temporal Databases, pp. 367–385. Springer (2011)Google Scholar
  31. 31.
    Yan, Z., Macedo, J., Parent, C., Spaccapietra, S.: Trajectory ontologies and queries. Trans. GIS 12(s1), 75–91 (2008)CrossRefGoogle Scholar
  32. 32.
    Yan, Z., Spaccapietra, S.: Towards semantic trajectory data analysis: A conceptual and computational approach. In: VLDB Ph.D. Workshop. Citeseer (2009)Google Scholar
  33. 33.
    Yu, F., Ip, H.H.: Semantic content analysis and annotation of histological images. Comput. Biol. Med. 38(6), 635–649 (2008)CrossRefGoogle Scholar
  34. 34.
    Zhang, A., Song, S., Wang, J.: Sequential data cleaning: A statistical approach (2016)Google Scholar
  35. 35.
    Zheng, Y.: Trajectory data mining: An overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Marwa Massaâbi
    • 1
    Email author
  • Olfa Layouni
    • 1
  • Assawer Zekri
    • 1
  • Mohammad Aljeaid
    • 2
  • Jalel Akaichi
    • 2
  1. 1.BESTMOD LaboratoryInstitut Supérieur de Gestion de TunisTunisTunisia
  2. 2.College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia

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