Defining Instance Spanning Constraint Patterns for Business Processes Based on Proclets

Winter, Karolin; Rinderle-Ma, Stefanie

doi:10.1007/978-3-030-62522-1_11

Cited by 4 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraints spanning multiple instances are referred to by CMF6 in the original CMF framework [64]. When looking into literature and the real-world constraints, CMF6 can be refined into constraints that reflect i) the simultaneous execution of events, ii) constrained execution, iii) order of events, iv) non-concurrent execution of events, and v) constrained start of following instances [114,113].…”

Section: Findings Based On Case Studiesmentioning

confidence: 99%

Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions

Rinderle-Ma¹,

Winter²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Business process compliance is a key area of business process management and aims at ensuring that processes obey to compliance constraints such as regulatory constraints or business rules imposed on them. Process compliance can be checked during process design time based on verification of process models and at runtime based on monitoring the compliance states of running process instances. For existing compliance monitoring approaches it remains unclear whether and how compliance violations can be predicted, although predictions are crucial in order to prepare and take countermeasures in time. This work, hence, analyzes existing literature from compliance and SLA monitoring as well as predictive process monitoring and provides an updated framework of compliance monitoring functionalities. For each compliance monitoring functionality we elicit prediction requirements and analyze their coverage by existing approaches. Based on this analysis, open challenges and research directions for predictive compliance and process monitoring are elaborated.

show abstract

Section: Findings Based On Case Studiesmentioning

confidence: 99%