Name
Abstract | ||
|---|---|---|
Process mining is the automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them focus on mining models from the prospective of control flow while ignoring the structure of mined models. This directly impacts the understandability and quality of mined models. To address the problem, we have proposed a genetic programming (GP) approach to mining simplified process models. Herein, genetic programming is applied to simplify the complex structure of process models using a tree-based individual representation. In addition, the fitness function derived from process complexity metric provides a guideline for discovering low complexity process models. Finally, initial experiments are performed to evaluate the effectiveness of the method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CIS.2011.303 | CIS |
Keywords | Field | DocType |
process model,complex structure,process mining,simplified business process model,automated acquisition,process mining technique,mining model,mined model,process complexity metric,genetic programming,structuredness metric,low complexity process model,genetic algorithm,measurement,genetic algorithms,fitness function,business process,data mining,process control,electronic countermeasures,business process model,control flow,business | Data mining,Computer science,Genetic programming,Artificial intelligence,Business process modeling,Genetic algorithm,Process mining,Mathematical optimization,Control flow,Process modeling,Fitness function,Process control,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weidong Zhao | 1 | 45 | 9.83 |
| Xi Liu | 2 | 36 | 10.08 |
| Anhua Wang | 3 | 4 | 2.44 |