Hybrid Particle Swarm Optimization method for process mining

Author

Chifu, Viorica Rozina ; Pop, Cristina Bianca ; Salomie, Ioan ; Balla, Izabella ; Paven, Ramona

Author_Institution

Dept. of Comput. Sci., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania

fYear

2012

fDate

Aug. 30 2012-Sept. 1 2012

Firstpage

273

Lastpage

279

Abstract

This paper presents a bio-inspired hybrid method that extracts the optimal or a near-optimal business process model from an event log. The proposed method combines Particle Swarm Optimization with Simulated Annealing to optimize the mining process in terms of execution time and model quality. To evaluate a candidate business process model we use a fitness function that considers as evaluation criteria the model completeness and preciseness according to the cases in the event log. The bio-inspired hybrid method has been integrated in the PROM framework and evaluated on a set of event logs.

Keywords

business data processing; data mining; particle swarm optimisation; simulated annealing; PROM framework; bio-inspired hybrid method; evaluation criteria; event log; execution time; fitness function; hybrid particle swarm optimization method; model completeness; model preciseness; model quality; near-optimal business process model; process mining; simulated annealing; Adaptation models; Biological system modeling; Birds; Business; Particle swarm optimization; Simulated annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computer Communication and Processing (ICCP), 2012 IEEE International Conference on

Conference_Location

Cluj-Napoca

Print_ISBN

978-1-4673-2953-8

Type

conf

DOI

10.1109/ICCP.2012.6356199

Filename

6356199