1. Translating Process Mining Results into Intelligible Business Information
- Author
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Ernesto Damiani, Paolo Ceravolo, Angelo Corallo, Manuela Marra, Antonia Azzini, Mariangela Lazoi, Ceravolo, P, Azzini, A, Damiani, E, Lazoi, M, Marra, M, and Corallo, A
- Subjects
Business rule ,Business process ,Computer science ,Artifact-centric business process model ,business.industry ,Process mining ,02 engineering and technology ,Business process modeling ,Business Process Assess-ment ,Data science ,Human-Computer Interaction ,Business process discovery ,Business Process Model and Notation ,Business process management ,Process Mining ,Computer Networks and Communication ,020204 information systems ,Business Rule ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,business ,Software - Abstract
Most business processes are today rooted into an informa-tion system recording operational events in log files. Process Mining algorithms exploit this information to discover and qualify differences between observed and modelled process. However, the output of these algorithms are not clearly con-nected with business properties. Our work faces these lim-itations by proposing an approach for calibrating Process Mining results based on the Business Rules adopted by an organisation. The general idea relates on applying Process Mining algorithms on subsequent refinements of the event log, flltering process executions based on Business Rules. This way we are able to associate these results with specific characterisations of the process, as entailed by the corre-sponding Business Rules. This approach is confronted to a real world scenario using data provided by an Italian man-ufacturing company.
- Published
- 2016