A Framework for the Management of Deformable Moving Objects

Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

There is an emergence of a growing number of applications and services based on spatiotemporal data in the most diverse areas of knowledge and human activity. The representation of the continuous evolution of moving regions, i.e., entities (or objects) whose position, shape and extent change continuously over time, is particularly challenging and the methods proposed in the literature to obtain such representation still present some issues. In this paper we present a framework for moving objects, in particular, moving regions, that uses the concept of mesh, i.e., a triangulated polygon, compatible triangulation and rigid interpolation methods to represent the continuous evolution of moving regions over time. We also present a spatiotemporal database extension for PostgreSQL that uses this framework and that allows to store moving objects data in a PostgreSQL database and to analyze and manipulate them using SQL. This extension can be smoothly integrated with PostGIS. Experiments show that our framework works with real data and provides a base for further work and investigation in this area.

Keywords

Moving objects Spatiotemporal data management Morphing 

Notes

Acknowledgements

We thank the anonymous reviewers for their comments and suggestions. This work is partially funded by National Funds through the FCT—Foundation for Science and Technology, in the context of the project UID/CEC/00127/2013.

References

  1. Alexa M, Cohen-or D, Levin D (2000) As-rigid-as-possible shape interpolation. In: SIGGRAPH ’00 Proceedings of the 27th annual conference on computer graphics and interactive techniques, pp 157–164Google Scholar
  2. Amaral A (2015) Representation of spatio-temporal data using compatible triangulation and morphing techniques. Aveiro UniversityGoogle Scholar
  3. Baxter W, Barla P, Anjyo K (2008) Rigid shape interpolation using normal equations. In: NPAR ’08 Proceedings of the 6th international symposium on Non-photorealistic animation and rendering, pp 59–64. http://dl.acm.org/citation.cfm?id=1377993
  4. Cotelo Lema J et al (2003) Algorithms for moving objects databases. Comput J 46(6):680–712CrossRefGoogle Scholar
  5. Forlizzi L et al (2000) A data model and data structures for moving objects databases. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, pp 319–330Google Scholar
  6. Gotsman C, Surazhsky V (2004) High quality compatible triangulations. Eng Comput 20(2):147–156Google Scholar
  7. Güting RH et al (2000) A foundation for representing and querying moving objects. ACM Trans Database Syst 25(1):1–42. http://doi.acm.org/10.1145/352958.352963
  8. Güting RH, Behr T, Düntgen C (2010) SECONDO: a platform for moving objects database research and for publishing and integrating research implementations. Bull IEEE Comput Soc Tech Comm Data Eng 33(2):56–63Google Scholar
  9. Heinz F, Güting RH (2016) Robust high-quality interpolation of regions to moving regions. GeoInformatica 20(3):385–413. http://link.springer.com/10.1007/s10707-015-0240-z
  10. Liu Z et al (2015) High quality compatible triangulations for 2D shape morphing. In: VRST ’15 Proceedings of the 21st ACM symposium on virtual reality software and technology, Beijing, China, pp 85–94Google Scholar
  11. Matos L, Moreira J, Carvalho A (2012) Representation and management of spatiotemporal data in object-relational databases. In: Proceedings of the 27th annual ACM symposium on applied computing, SAC ’12. ACM, New York, NY, USA, pp 13–20. http://doi.acm.org/10.1145/2245276.2245280
  12. Mckenney M, Webb J (2010) Extracting moving regions from spatial data. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, San Jose, California, pp 438–441. http://doi.acm.org/10.1145/1869790.1869856
  13. Mckennney M, Frye R (2015) Generating moving regions from snapshots of complex regions. ACM Trans Spat Algorithms Syst 1(1):1–30. http://doi.acm.org/10.1145/2774220
  14. Mesquita P (2013) Morphing techniques for representation of geographical moving objects. Universidade de AveiroGoogle Scholar
  15. Moreira J, Dias P, Amaral P (2016) Representation of continuously changing data over time and space. In: 2016 IEEE 12th international conference on e-science, Baltimore, MD, USA. IEEE, pp 111–119Google Scholar
  16. Pelekis N et al (2006) Hermes—a framework for location-based data management. In: EDBT’06 Proceedings of the 10th international conference on advances in database technology, Munich, Germany, pp 1130–1134.  https://doi.org/10.1007/11687238_75
  17. Sanderson C, Curtin R (2016) Armadillo: a template-based C++ library for linear algebra. J Open Source Softw 1:26CrossRefGoogle Scholar
  18. Tøssebro E, Güting R (2001) Creating representations for continuously moving regions from observations. In: Proceedings of the 7th international symposium on advances in spatial and temporal databases. Springer, Berlin, Heidelberg, pp 321–344.  https://doi.org/10.1007/3-540-47724-1_17
  19. Zhao L et al (2011) STOC: extending oracle to support spatiotemporal data management. Springer, Berlin, Heidelberg, pp 393–397. http://link.springer.com/10.1007/978-3-642-20291-9_43. Accessed 30 Sept 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.DETI/IEETA, Universidade de AveiroAveiroPortugal

Personalised recommendations