Anomaly detection in business processes using process mining and fuzzy association rule learning
Abstract 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 an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-01-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-019-0277-1 |
| _version_ | 1818965146440040448 |
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| author | Riyanarto Sarno Fernandes Sinaga Kelly Rossa Sungkono |
| author_facet | Riyanarto Sarno Fernandes Sinaga Kelly Rossa Sungkono |
| author_sort | Riyanarto Sarno |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-20T13:12:22Z |
| format | Article |
| id | doaj.art-80f2a0b075bd44a7b328a950b8d80ab1 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-20T13:12:22Z |
| publishDate | 2020-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-80f2a0b075bd44a7b328a950b8d80ab12022-12-21T19:39:38ZengSpringerOpenJournal of Big Data2196-11152020-01-017111910.1186/s40537-019-0277-1Anomaly detection in business processes using process mining and fuzzy association rule learningRiyanarto Sarno0Fernandes Sinaga1Kelly Rossa Sungkono2Department of Informatics, Institut Teknologi Sepuluh NopemberDepartment of Informatics, Institut Teknologi Sepuluh NopemberDepartment of Informatics, Institut Teknologi Sepuluh NopemberAbstract 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.https://doi.org/10.1186/s40537-019-0277-1Process miningAnomaly detectionFuzzy association rule learning |
| spellingShingle | Riyanarto Sarno Fernandes Sinaga Kelly Rossa Sungkono Anomaly detection in business processes using process mining and fuzzy association rule learning Journal of Big Data Process mining Anomaly detection Fuzzy association rule learning |
| title | Anomaly detection in business processes using process mining and fuzzy association rule learning |
| title_full | Anomaly detection in business processes using process mining and fuzzy association rule learning |
| title_fullStr | Anomaly detection in business processes using process mining and fuzzy association rule learning |
| title_full_unstemmed | Anomaly detection in business processes using process mining and fuzzy association rule learning |
| title_short | Anomaly detection in business processes using process mining and fuzzy association rule learning |
| title_sort | anomaly detection in business processes using process mining and fuzzy association rule learning |
| topic | Process mining Anomaly detection Fuzzy association rule learning |
| url | https://doi.org/10.1186/s40537-019-0277-1 |
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