Abstract
Process model discovery can be approached as an optimization problem, for which genetic algorithms have been used previously. However, the fitness functions used, which consider full log traces, have not been found adequate to discover unstructured processes. We propose a solution based on a local analysis of activity transitions, which proves effective for unstructured processes, most common in organizations. Our solution considers completeness and accuracy calculation for the fitness function.
Original language | English |
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Title of host publication | 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509060177 |
DOIs | |
Publication status | Published - 2018 |
Event | 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 |
Conference
Conference | 2018 IEEE Congress on Evolutionary Computation, CEC 2018 |
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Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 8/07/18 → 13/07/18 |