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 |
|---|---|
| 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 |
|---|---|
| Country | Brazil |
| City | Rio de Janeiro |
| Period | 8/07/18 → 13/07/18 |
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Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision. / Da Silva, Gabriel L.C.; Fantinato, Marcelo; Peres, Sarajane M.; Reijers, Hajo A.
2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8477795.Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision
AU - Da Silva, Gabriel L.C.
AU - Fantinato, Marcelo
AU - Peres, Sarajane M.
AU - Reijers, Hajo A.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85056280658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056280658&partnerID=8YFLogxK
U2 - 10.1109/CEC.2018.8477795
DO - 10.1109/CEC.2018.8477795
M3 - Conference contribution
BT - 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -