当前位置: X-MOL 学术J. Big Data › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Anomaly detection in business processes using process mining and fuzzy association rule learning
Journal of Big Data ( IF 8.1 ) Pub Date : 2020-01-09 , DOI: 10.1186/s40537-019-0277-1
Riyanarto Sarno , Fernandes Sinaga , Kelly Rossa Sungkono

Much corporate organization nowadays implement enterprise resource planning (ERP) to manage their business processes. Because the processes run continuously, ERP produces a massive log of processes. Manual observation will have difficulty monitoring the enormous log, especially detecting anomalies. It needs the method that can detect anomalies in the large log. This paper proposes the integration of process mining, fuzzy multi-attribute decision making and fuzzy association rule learning to detect anomalies. Process mining analyses the conformance between recorded event logs and standard operating procedures. The fuzzy multi-attribute decision making is applied to determine the anomaly rates. Finally, the fuzzy association rule learning develops association rules that will be employed to detect anomalies. The results of our experiment showed that the accuracy of the association rule learning method was 0.975 with a minimum confidence level of 0.9 and that the accuracy of the fuzzy association rule learning method was 0.925 with a minimum confidence level of 0.3. Therefore, the fuzzy association rule learning method can detect fraud at low confidence levels.

中文翻译:

使用流程挖掘和模糊关联规则学习的业务流程异常检测

如今,许多公司组织都实施企业资源计划(ERP)来管理其业务流程。由于流程连续运行,因此ERP会生成大量的流程日志。手动观察将难以监视巨大的日志,尤其是检测异常。它需要能够在大日志中检测异常的方法。本文提出了过程挖掘,模糊多属性决策和模糊关联规则学习相结合的异常检测方法。流程挖掘分析记录的事件日志和标准操作程序之间的一致性。应用模糊多属性决策方法确定异常率。最后,模糊关联规则学习会开发出关联规则,以用于检测异常。我们的实验结果表明,关联规则学习方法的准确性为0.975,最小置信度为0.9,而模糊关联规则学习方法的准确性为0.925,最小置信度为0.3。因此,模糊关联规则学习方法可以在低置信度下检测欺诈。
更新日期:2020-01-09
down
wechat
bug