Journal
INFORMATION SYSTEMS
Volume 89, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.is.2019.101484
Keywords
Digitalized compliance management; Constraint mining; Instance spanning constraints; Process mining
Categories
Funding
- Vienna Science and Technology Fund (WWTF) [ICTI5-072]
- Austrian Research Promotion Agency (FFG) via the Austrian Competence Center for Digital Production (CDP) [854187]
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Instance spanning constraints (ISC) are the instrument to establish controls across multiple instances of one or several processes. A multitude of applications crave for ISC support. Consider, for example, the bundling and unbundling of cargo across several instances of a logistics process or dependencies between examinations in different medical treatment processes. Non-compliance with ISC can lead to severe consequences and penalties, e.g., dangerous effects due to undesired drug interactions. ISC might stem from regulatory documents, extracted by domain experts. Another source for ISC are process execution logs. Process execution logs store execution information for process instances, and hence, inherently, the effects of ISC. Discovering ISC from process execution logs can support ISC design and implementation (if the ISC was not known beforehand) and the validation of the ISC during its life time. This work contributes a categorization of ISC as well as four discovery algorithms for ISC candidates from process execution logs. The discovered ISC candidates are put into context of the associated processes and can be further validated with domain experts. The algorithms are prototypically implemented and evaluated based on artificial and real-world process execution logs. The results facilitate ISC design as well as validation and hence contribute to a digitalized ISC and compliance management. (C) 2019 The Authors. Published by Elsevier Ltd.
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