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Automaton-based comparison of Declare process models

Title data

Schützenmeier, Nicolai ; Käppel, Martin ; Ackermann, Lars ; Jablonski, Stefan ; Petter, Sebastian:
Automaton-based comparison of Declare process models.
In: Software and Systems Modeling. (5 December 2022) .
ISSN 1619-1374
DOI: https://doi.org/10.1007/s10270-022-01069-y

Abstract in another language

The Declare process modeling language has been established within the research community for modeling so-called flexible processes. Declare follows the declarative modeling paradigm and therefore guarantees flexible process execution. For several reasons, declarative process models turned out to be hard to read and comprehend. Thus, it is also hard to decide whether two process models are equal with respect to their semantic meaning, whether one model is completely contained in another one or how far two models overlap. In this paper, we follow an automaton-based approach by transforming Declare process models into finite state automatons and applying automata theory for solving this issue.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Business process modeling; Declare; Model comparison; Declarative process management; Automata theory
Institutions of the University: Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
000 Computer Science, information, general works > 050 General serials and their indexes
Date Deposited: 06 Dec 2022 06:11
Last Modified: 06 Dec 2022 06:11
URI: https://eref.uni-bayreuth.de/id/eprint/72961