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SILVA, Gabriel Lucas Cantanhede da. Application of genetic algorithms on mining non structured processes. 2018. 106 p. Dissertation (Master of Science) – School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 2018. Process mining is a new field of research that links data mining and business process management. Process mining follows the premise that there is an unknown process in a given context, and by analyzing the traces of it’s behavior, with the help of data mining, the process model can be discovered. However, realistic business processes are difficult to mine because of excessive behavior recorded in the logs. These unstructured processes, despite being complex, hold great potential for improvement, and the current process mining approaches for that context yet provide little support for management. This master’s research project aims to apply evolutionary computational techniques in process mining, using genetic algorithms to automatically discover unstructured process models in order to support process management in organizations. A literature review was carried out to support the proposition of a new approach focused on the discovery of unstructured process models. The proposed approach introduces new formulas for calculating completeness and precision metrics, based on the information of transitions between activities that are reorganized through a matrix structure created in this work. The approach also introduces the use of genetic operators and evolutionary flow strategies not yet implemented in the literature regarding genetic algorithms in process discovery. Analyzes of the parameterization of the proposed approach, as well as the resulting process models, indicate that the approach is effective in mining better process models from samples of a unstructured log.

Aplicação de algoritmos genéticos em mineração de processos não estruturados