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Data Matching – a Matter of Belief

  • Ana-Maria Olteanu Raimond
  • Sébastien Mustière
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Nowadays, it is often that a geographic area is described by several independent geographic databases. Yet users need to fusion various information coming from these databases. In order to integrate databases, redundancy and inconsistency between data should be identified. Many steps are required to finalise the databases integration, in particular automatic data matching. In this paper, one approach of matching geographic data bearing on the belief theory is presented. This approach consists in combining criteria from knowledge such as geometry, orientation, nature of roads, names and topology. Then it is tested on heterogeneous network representing roads.

Keywords

data matching networks belief function fusion topology 

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References

  1. Abadie N, Olteanu A-M, Mustière S (2007) Comparaison de la nature d’objets géographiques. In : Colloque Ingénierie des Connaissances, 3 juillet, 2007, GrenobleGoogle Scholar
  2. Appriou A (1991) Probabilités et incertitudes en fusion de données multi-senseurs Revue Scientifique et Technique de la Défense 11, pp 27-40Google Scholar
  3. Beeri C, Kanza Y, Safra E, Sagiv Y (2004) Object fusion in Geographic Information System. Proceedings 30th VLDB conference, 2004, Toronto, Canada, pp 816-827Google Scholar
  4. Dempster A (1968) Upper and lower probabilities induced by multivalued mapping. Annals of Mathematical Statistics, (AMS-38), pp 325-339Google Scholar
  5. Devogele T (1997) Processus d’intégration et d’appariement des bases de données géographiques-Application àune base de données routière multi-échelles, PhD thesis, Universitéde Versailles, FranceGoogle Scholar
  6. Devogele T, Parent C, Spaccapietra S (2001) On spatial database integration. International Journal of Geographical Information Science, 12(4), pp 335–352CrossRefGoogle Scholar
  7. Hampe M, Sester M (2002) Real-time integration and generalization of spatial data for mobile applications. Geowissenschaftliche Mitteilungen, Maps and the Internet, Wien, Heft (60), pp 167-175.Google Scholar
  8. Kilpelaïen T (2000) Maintenance of Multiple Representation Databases of Topographic Data. The Cartographic Journal, 37 (2), pp 101-107Google Scholar
  9. Levenshtein VI (1965) Binary Codes Capable of Correcting Deletions, Insertions, and Reversals, Soviet Physics - Doklady, 10(8), 707-710. Translated from Doklady Akademii Nauk SSSR, 163(4), pp845-848, 1965Google Scholar
  10. Mustière S (2006) Results of experiments on automated matching of networks. Proceedings of the ISPRS Workshop on Multiple Representation and Interoperability of Spatial Data, 22-24 February 2006, Hanover, Germany, pp 92-100Google Scholar
  11. Mustière S, Devogele T (2008) Matching networks with different levels of detail. GeoInformatica, on press, to be published in 2008Google Scholar
  12. Mustière S, van Smaalen J (2007) Database Requirements for Generalisation and Multiple Representations. In: Mackaness W, Ruas A, Sarjakoski T (eds), The Generalisation of Geographic Information: Models and Applications, Elsevier, pp 113-136.Google Scholar
  13. Olteanu A-M (2007) Matching geographical data using the Theory of Evidence. Proceedings of 20th ICC, 5-9 August 2007, Moscow, RussieGoogle Scholar
  14. Rajabifard A, Binns A, Masser I, Williamson I (2006) The role of sub-national government and the private sector in future spatial data infrastructures. International Journal of Geographical Information Science, 20(7), pp 727-741CrossRefGoogle Scholar
  15. Rodriguez MA, Egenhofer MJ (2003) Determining semantic similarity among entity classes from different ontologies. IEEE Transactions on Knowledge and Data Engineering, 15(2), pp 442- 456CrossRefGoogle Scholar
  16. Royère C, Gruyer D, Cherfaoui V (2002) Data association with believe theory. Proceedings of International Conference of Information Fusion, Washington, pp 23-29Google Scholar
  17. Samal A, Seth SC, Cueto K (2004) A feature-based approach to conflation of geospatial sources. International Journal of Geographical Information Science, 18(5), pp 459-489.CrossRefGoogle Scholar
  18. Shafer G (1976) A Mathematical Theory of Evidence. Princeton University Press.Google Scholar
  19. Smets P, Kennes R (1994) The Transferable Belief Model. Artificial Intelligence, 66, pp 191-234.CrossRefGoogle Scholar
  20. Uitermark H (2001) Ontology-Based Geographic Data Set Integration. PhD thesis, Universiteit Twente, the Netherlands, 2001.Google Scholar
  21. Wu Z, Palmer M (1994) Verb Semantics and Lexical Selection. Proceedings of the 32nd Annual Meetings of the Associations for Computational Linguistics, pp 133-138Google Scholar
  22. Volz S (2006) An iterative approach for matching multiple representations of street data. Proceedings of the ISPRS workshop on Multiple Representation and Interoperability of Spatial Data, 22-24 February 2006, Hanover, Germany, pp 101-110Google Scholar
  23. Walter V, Fritsch D (1999) Matching Spatial Data Sets: a Statistical Approach. International Journal of Geographical Information Science, 13(5), pp445-473CrossRefGoogle Scholar
  24. Zhang M, Shi W, Meng L (2005) A generic matching algorithm for line networks of different resolutions. Proceedings 8th ICA workshop on Generalisation and multiple Representation, July, 2005, Coruña, SpainGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ana-Maria Olteanu Raimond
    • 1
  • Sébastien Mustière
    • 1
  1. 1.COGIT LaboratoryIGN94165 Saint-MandécedexFrance

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