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Software and Systems Modeling

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21.04.2016 | Theme Section Paper

A visual language for modeling multiple perspectives of business process compliance rules

verfasst von: David Knuplesch, Manfred Reichert

Erschienen in: Software and Systems Modeling | Ausgabe 3/2017

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Abstract

A fundamental challenge for enterprises is to ensure compliance of their business processes with imposed compliance rules stemming from various sources, e.g., corporate guidelines, best practices, standards, and laws. In general, a compliance rule may refer to multiple process perspectives including control flow, time, data, resources, and interactions with business partners. On one hand, compliance rules should be comprehensible for domain experts who must define, verify, and apply them. On the other, these rules should have a precise semantics to avoid ambiguities and enable their automated processing. Providing a visual language is advantageous in this context as it allows hiding formal details and offering an intuitive way of modeling the compliance rules. However, existing visual languages for compliance rule modeling have focused on the control flow perspective so far, but lack proper support for the other process perspectives. To remedy this drawback, this paper introduces the extended Compliance Rule Graph language, which enables the visual modeling of compliance rules with the support of multiple perspectives. Overall, this language will foster the modeling and verification of compliance rules in practice.

Vorheriger Artikel A participative end-user method for multi-perspective business process elicitation and improvement

Nächster Artikel Designing secure business processes with SecBPMN

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A Kolmogorov–Smirnov test (p value 0.006) suggested not accepting the assumption of a normal distribution.

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Titel: A visual language for modeling multiple perspectives of business process compliance rules
verfasst von: David Knuplesch
Manfred Reichert
Publikationsdatum: 21.04.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Software and Systems Modeling / Ausgabe 3/2017
Print ISSN: 1619-1366
Elektronische ISSN: 1619-1374
DOI: https://doi.org/10.1007/s10270-016-0526-0

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