Expressing Measurement Uncertainty in OCL/UML Datatypes

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10890)


Uncertainty is an inherent property of any measure or estimation performed in any physical setting, and therefore it needs to be considered when modeling systems that manage real data. Although several modeling languages permit the representation of measurement uncertainty for describing certain system attributes, these aspects are not normally incorporated into their type systems. Thus, operating with uncertain values and propagating uncertainty are normally cumbersome processes, difficult to achieve at the model level. This paper proposes an extension of OCL and UML datatypes to incorporate data uncertainty coming from physical measurements or user estimations into the models, along with the set of operations defined for the values of these types.



This work has been partially supported by the Spanish Government under Grant TIN2014-52034-R. We would like to thank Martin Gogolla for his help and support during the development of the USE tool extension, and to the reviewers for their constructive comments and very valuable suggestions.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad de MálagaMálagaSpain
  2. 2.Universidad de SevillaSevillaSpain

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