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Representing Internal Varying Characteristics of Moving Objects

  • Ahmed Ibrahim
  • Ulanbek Turdukulov
  • Menno-Jan Kraak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8716)

Abstract

Recent data acquisition tools have resulted in huge amounts of data that have spatial and temporal components. The movement represents an important category of such data. Some phenomena may have attributes that vary continuously over space, such as wildfires and storms. Nevertheless, for simplification purpose, most applications represent such phenomena as objects by neglecting their internal continuous structure. Moreover, little consideration has been given to such characteristics in moving objects database. At this end, this paper presents a data model for managing raster data and internal heterogenous attributes in moving objects. The data model utilizes the abstract data types. We add two abstractions (moving raster, and combined type) to describe the change of the raster data and internal varying characteristics of the moving objects along with specific operations that permit to analyse them. Query examples are provided to demonstrate the application of these operations.

Keywords

Spatiotemporal data model moving objects internal varying characteristics moving objects databases 

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References

  1. 1.
    Goodchild, M.F., Yuan, M., Cova, T.J.: Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science 21(3), 239–260 (2007)CrossRefGoogle Scholar
  2. 2.
    Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases. SIGMOD Rec. 29(2), 319–330 (2000)CrossRefGoogle Scholar
  3. 3.
    Cova, T.J., Goodchild, M.F.: Extending geographical representation to include fields of spatial objects. International Journal of Geographical Information Science 16(6), 509–532 (2002)CrossRefGoogle Scholar
  4. 4.
    McIntosh, J., Yuan, M.: A framework to enhance semantic flexibility for analysis of distributed phenomena. International Journal of Geographical Information Science 19(10), 999–1018 (2005)CrossRefGoogle Scholar
  5. 5.
    Yuan, M.: Representing Complex Geographic Phenomena in GIS. Cartography and Geographic Information Science 28(2), 83–96 (2001)CrossRefGoogle Scholar
  6. 6.
    Galton, A.: Space, Time, and the Representation of Geographical Reality. Topoi 20(2), 173–187 (2001)CrossRefGoogle Scholar
  7. 7.
    Galton, A.: Fields and Objects in Space, Time, and Space-time. Spatial Cognition & Computation: An Interdisciplinary Journal 4(1), 39–68 (2004)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Yuan, M.: Wildfire conceptual modeling for building GIS space-time models. In: GIS/LIS, pp. 860–869 (1994)Google Scholar
  9. 9.
    Yuan, M.: Use of a Three-Domain Repesentation to Enhance GIS Support for Complex Spatiotemporal Queries. Transactions in GIS 3(2), 137–159 (1999)CrossRefGoogle Scholar
  10. 10.
    Pultar, E., Cova, T.J., Yuan, M., Goodchild, M.F.: EDGIS: a dynamic GIS based on space time points. International Journal of Geographical Information Science 24(3), 329–346 (2010)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Güting, R.H., Bhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Transactions on Database Systems 25(1), 1–42 (2000)CrossRefGoogle Scholar
  13. 13.
    Kim, K.-S., Kiyoki, Y.: An Object-Field Perspective Data Model for Moving Geographic Phenomena. In: Yoshikawa, M., Meng, X., Yumoto, T., Ma, Q., Sun, L., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 6193, pp. 410–421. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Lema, C., Antonio, J., Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: Algorithms for Moving Objects Databases. The Computer Journal 46(6), 680–712 (2003)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ahmed Ibrahim
    • 1
  • Ulanbek Turdukulov
    • 2
  • Menno-Jan Kraak
    • 1
  1. 1.Faculty of Geoinformation Science and Earth Observation (ITC)University of TwenteThe Netherlands
  2. 2.Western Australian School of MinesCurtin UniversityAustralia

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