Skip to main content

Regions Trajectories Data: Evolution of Modeling and Construction Methods

  • Conference paper
  • First Online:
Intelligent Interactive Multimedia Systems and Services 2017 (KES-IIMSS-18 2018)

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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) http://ceur-ws.org/Vol-1075/00.pdf

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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. Schneider, M.: Design and implementation of finite resolution crisp and fuzzy spatial objects. Data Knowl. Eng. 44(1), 81–108 (2003)

    Article  MATH  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. Yan, Z., Macedo, J., Parent, C., Spaccapietra, S.: Trajectory ontologies and queries. Trans. GIS 12(s1), 75–91 (2008)

    Article  Google Scholar 

  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. Yu, F., Ip, H.H.: Semantic content analysis and annotation of histological images. Comput. Biol. Med. 38(6), 635–649 (2008)

    Article  Google Scholar 

  34. Zhang, A., Song, S., Wang, J.: Sequential data cleaning: A statistical approach (2016)

    Google Scholar 

  35. Zheng, Y.: Trajectory data mining: An overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marwa Massaâbi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Massaâbi, M., Layouni, O., Zekri, A., Aljeaid, M., Akaichi, J. (2018). Regions Trajectories Data: Evolution of Modeling and Construction Methods. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59480-4_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59479-8

  • Online ISBN: 978-3-319-59480-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics