Advertisement

Matching Uncertain Identities Against Sparse Knowledge

  • Steven HornEmail author
  • Anthony Isenor
  • Moira MacNeil
  • Adrienne Turnbull
Conference paper
  • 394 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9310)

Abstract

This paper presents a method for fast matching of data attributes contained in a high-volume data stream against an incomplete database of known attribute values. The method is applied to vessel observational data and databases of known vessel characteristics, with emphasis on vessel identity attributes. Due to the large quantity of streaming observations, it is desirable to compute the best matching identity to a sufficient confidence level rather than include all possible identity information in the matching result. The question of which observed attributes to use in the calculation is addressed using information theory and the combination of the information conveyed by each attribute is addressed using evidence theory. An algorithm is developed which matches observations to known identities with a configurable level of desired confidence, represented as a \(\chi ^2\) value for statistical significance.

Keywords

Entropy Transferrable belief model Generalized Bayes theorem Database Intelligence Information Data errors 

References

  1. 1.
    Graybeal, J., Isenor, A.W., Reuda, C.: Semantic mediation of vocabularies for ocean observing systems. Comput. Geosci. 40, 120–131 (2012)CrossRefGoogle Scholar
  2. 2.
    ST-Hilaire, M.-O., Isenor, A.W.: Determining the consistency of information between multiple subsystems used in maritime domain awareness. NATO Science for Peace and Security Series-E: Human and Societal Dynamics, NATO Advanced Science Institutes Series (2011)Google Scholar
  3. 3.
    Jousselme, A.-L., Maupin, P.: Comparison of uncertainty representations for missing data in information retrieval. In: Proceedings of the 16th International Conference on Information Fusion, pp. 1902–1909 (2013)Google Scholar
  4. 4.
    Unger, E.A., Harn, L.: Entropy as a measure of database information. In: Proceedings of the Sixth Annual Computer Security Applications Conference, pp. 80–77 (1990)Google Scholar
  5. 5.
    Elouedi, Z., Mellouli, K., Smets, P.: Belief decision trees: theoretical foundations. Int. J. Approx. Reason. 28(2–3), 91–124 (2001)zbMATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Wolpert, D., Wolf, D.: Estimating functions of probability distributions from a finite set of samples; part i: Bayes estimators and the Shannon entropy. Santa Fe Institute 1993–07-046 (1993)Google Scholar
  7. 7.
    Smets, P.: Data fusion in the transferable belief model. In: Proceedings of the 3rd International Conference on Information Fusion, pp. 21–33 (2000)Google Scholar
  8. 8.
    Janez, F., Appriou, A.: Theory of evidence and non-exhaustive frames of discernment Plausibilities correction methods. Int. J. Approx. Reason. 18, 1–19 (1998)zbMATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    Smets, P.: Belief functions: the disjunctive rule of combination and the generalized Bayes theorem. Int. J. Approx. Reason. 9, 1–35 (1993)zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Denoeux, T.: Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. J. Artif. Intell. 172(2–3), 234–264 (2008)zbMATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Lefevre, E., Vannoorenberghe, P., Colot, O.: Using information criteria in Dempster-Shafer’s basic belief assignment. In: Proceedings of the 1999 IEEE International Fuzzy Systems Conference, pp. 173–178 (1999)Google Scholar
  12. 12.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  13. 13.
    Reineking, T.: A Python library for performing calculations in the Dempster-Shafer theory of evidence. https://github.com/reineking/pyds

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Steven Horn
    • 1
    Email author
  • Anthony Isenor
    • 2
  • Moira MacNeil
    • 1
  • Adrienne Turnbull
    • 1
  1. 1.Centre for Operational Research and AnalysisDefence Research and Development CanadaOttawaCanada
  2. 2.Atlantic Research CentreDefence Research and Development CanadaHalifaxCanada

Personalised recommendations