Collection and Elicitation of Business Process Compliance Patterns with Focus on Data Aspects
Abstract
Business process compliance is one of the prevalent challenges for companies. Despite an abundance of research proposals, companies still struggle with manual compliance checks and the understanding of compliance violations in the light of missing root-cause explanations. Moreover, approaches have merely focused on the control flow perspective in compliance checking, neglecting other aspects such as the data perspective. This paper aims at analyzing the gap between existing academic work and compliance demands from practice with a focus on the data aspects. The latter emerges from a small set of regulatory documents from different domains. Patterns are assumed as the right level of abstraction for compliance specification due to their independence of (technical) implementation in (process-aware) information systems, potential for reuse, and understandability. A systematic literature review collects and assesses existing compliance patterns. A first analysis of ten regulatory documents from different domains specifically reveals data-oriented compliance constraints that are not yet reflected by existing compliance patterns. Accordingly, data-related compliance patterns are specified.
- Citation
- BibTeX
Voglhofer, T. & Rinderle-Ma, S.,
(2020).
Collection and Elicitation of Business Process Compliance Patterns with Focus on Data Aspects.
Business & Information Systems Engineering: Vol. 62, No. 4.
Springer.
(S. 361-377).
DOI: 10.1007/s12599-019-00594-3
@article{mci/Voglhofer2020,
author = {Voglhofer, Thomas AND Rinderle-Ma, Stefanie},
title = {Collection and Elicitation of Business Process Compliance Patterns with Focus on Data Aspects},
journal = {Business & Information Systems Engineering},
volume = {62},
number = {4},
year = {2020},
,
pages = { 361-377 } ,
doi = { 10.1007/s12599-019-00594-3 }
}
author = {Voglhofer, Thomas AND Rinderle-Ma, Stefanie},
title = {Collection and Elicitation of Business Process Compliance Patterns with Focus on Data Aspects},
journal = {Business & Information Systems Engineering},
volume = {62},
number = {4},
year = {2020},
,
pages = { 361-377 } ,
doi = { 10.1007/s12599-019-00594-3 }
}
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ISSN: 1867-0202
xmlui.MetaDataDisplay.field.date: 2020
Content Type: Text/Journal Article