Title: Log automaton under conditions of infrequent behaviour mining

Authors: Xian-wen Fang; Juan Li; Li-li Wang; Huan Fang

Addresses: School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, China ' School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, China ' School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, China ' School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, China

Abstract: In the existing process mining methods, infrequent behaviours are often considered as noise is ignored, but some infrequent behaviours have an important role in business process management. Firstly, the knowledge of log automaton is applied to the low-frequency log to delete infrequent behaviour in the logs; secondly, the processed logs are added into attributes. Then, the condition-dependent value of the communication characteristics of different module networks is compared with the threshold, and the effective infrequent log is retained to optimise the model. Finally, a practical case is applied, which indicates the effectiveness and validation of the proposed method.

Keywords: process mining; log automaton; infrequent behaviour; conditional dependency measure.

DOI: 10.1504/IJITM.2020.110239

International Journal of Information Technology and Management, 2020 Vol.19 No.4, pp.292 - 304

Received: 18 Jul 2018
Accepted: 26 Mar 2019
Published online: 12 Oct 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article