Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014)

A Method to Tackle Abnormal Event Logs Based on Process Mining

Authors
Zhanmin Yang, Liqun Zhang, Yuan Hu
Corresponding Author
Zhanmin Yang
Available Online March 2014.
DOI
10.2991/sekeie-14.2014.8How to use a DOI?
Keywords
Process Mining, Abnormal Event Logs; Petri-net, Re-execute
Abstract

One important function of process mining is to manage business process in order to detect the abnormal. A new process mining method is proposed based on process mining technology. The method will form a new process model that is more extensible relative to original process model. Then we use a effective method re-executing our event logs based on this new process model. Several kinds of abnormal are defined: task lost; task is excess; task replaced; tasks disordered. Due to the define of abnormal type, the re-executed results will show us the concrete mistakes about abnormal behaviors and help us understood and analysis the business process easily.

Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014)
Series
Advances in Intelligent Systems Research
Publication Date
March 2014
ISBN
10.2991/sekeie-14.2014.8
ISSN
1951-6851
DOI
10.2991/sekeie-14.2014.8How to use a DOI?
Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhanmin Yang
AU  - Liqun Zhang
AU  - Yuan Hu
PY  - 2014/03
DA  - 2014/03
TI  - A Method to Tackle Abnormal Event Logs Based on Process Mining
BT  - Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014)
PB  - Atlantis Press
SP  - 34
EP  - 38
SN  - 1951-6851
UR  - https://doi.org/10.2991/sekeie-14.2014.8
DO  - 10.2991/sekeie-14.2014.8
ID  - Yang2014/03
ER  -