NDLI: A new model for discovering process trees from event logs

Content Provider	Springer Nature Link
Author	Vahedian Khezerlou, Amin Alizadeh, Somayeh
Copyright Year	2014
Abstract	Process mining techniques aim at extracting knowledge from event logs. One of the most important tasks in process mining is process model discovery. In discovering process models, an algorithm is designed to build a process model from a given event log. In this paper, a new model to discover process models has been proposed. A combination of Genetic Algorithm and Simulated Annealing has been used in this model. Genetic Algorithms has previously been used in this context. Previous approaches had drawbacks in fitness evaluation that misguided the algorithm. Another problem was that the quality of the candidates, in the population, was low such that it reduced the chance of finding a perfect answer. In this paper, a new fitness measure has been proposed to evaluate process models based on event logs. Moreover SA has been used to improve the quality of candidates in the population. It has been demonstrated that the proposed model outperformed in terms of rediscovering process models, compared to other approaches which are proposed in the literature, which was the result of better fitness evaluation and increased quality of individuals,. It came to conclusion that using GA and SA in combination with each other can be effective in this context.
Starting Page	725
Ending Page	735
Page Count	11
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	41
Issue Number	3
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2014-07-04
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Process mining Process modeling Process tree Business process management Genetic algorithm Simulated annealing Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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