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2018 | OriginalPaper | Chapter

Expressing Measurement Uncertainty in OCL/UML Datatypes

Authors : Manuel F. Bertoa, Nathalie Moreno, Gala Barquero, Loli Burgueño, Javier Troya, Antonio Vallecillo

Published in: Modelling Foundations and Applications

Publisher: Springer International Publishing

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Abstract

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.

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Operations on basic datatypes normally use infix notation (e.g., \(x+y\), \(a<b\), \(P \ {\texttt {and}}\ Q\)). This is the notation that we already support in our USE implementation for the newly defined types (UReal, UBoolean, etc.), see Sect. 3.7. However, other languages that we have used to implement these new types (e.g., Java) do not support infix notation. Therefore, in the following we will use either an infix or prefix notation (x.add(y), a.lt(b), P.and(Q)) for the operations of these types, depending on the context and on the particular language used.

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Title: Expressing Measurement Uncertainty in OCL/UML Datatypes
Authors: Manuel F. Bertoa
Nathalie Moreno
Gala Barquero
Loli Burgueño
Javier Troya
Antonio Vallecillo
Publisher: Springer International Publishing
Book: Modelling Foundations and Applications
Print ISBN: 978-3-319-92996-5

Electronic ISBN: 978-3-319-92997-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-92997-2_4

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