| Title |
Prognosing the Compliance of Declarative Business Processes Using Event Trace Robustness |
| Authors |
Teresa Gomez-Lopez, Maria , PARODY NÚÑEZ, MARÍA LUISA, Gasca, Rafael M. , Rinderle-Ma, Stefanie |
| External publication |
Si |
| Means |
Lect. Notes Comput. Sci. |
| Scope |
Proceedings Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.35400 |
| Publication date |
01/01/2014 |
| ISI |
000346246900019 |
| Abstract |
Several proposals have studied the compliance of execution of business process traces in accordance with a set of compliance rules. Unfortunately, the detection of a compliance violation (diagnosis) means that the observed events have already violated the compliance rules that describe the model. In turn, the detection of a compliance violation before its actual occurrence would prevent misbehaviour of the business processes. This functionality is referred to as proactive management of compliance violations in literature. However, existing approaches focus on the detection of inconsistencies between the compliance rules or monitoring process instances that are in a violable state. The notion of robustness could help us to prognosticate the occurrence of these inconsistent states in a premature way, and to detect, depending on the current execution state of the process instance, how "close" the execution is to a possible violation. On top of being able to possibly avoid violations, a robust trace is not sensitive to small changes. In this paper we propose the way to determine whether a process instance is robust against a set of compliance rules during its execution at runtime. Thanks to the use of constraint programming and the capacities of super solutions, a robust trace can be guaranteed. |
| Keywords |
Declarative Business Process; Compliance Rules; Model-based Prognosis; Robustness |
| Universidad Loyola members |
|
Manage cookies