Anomaly detection in business processes using process mining and fuzzy association rule learning 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 multiattribute 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 multiattribute 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.

Ontologydriven extraction of event logs from relational databases Process mining is an emerging discipline whose aim is to discover, monitor and improve real processes by extracting knowledge from event logs representing actual process executions in a given organizational setting. In this light, it can be applied only if faithful event logs, adhering to accepted standards (such as XES), are available. In many realworld settings, though, such event logs are not explicitly given, but are instead implicitly represented inside legacy information systems of organizations, which are typically managed through relational technology. In this work, we devise a novel framework that supports domain experts in the extraction of XES event log information from legacy relational databases, and consequently enables the application of standard process mining tools on such data. Differently from previous work, the extraction is driven by a conceptual representation of the domain of interest in terms of an ontology. On the one hand, this ontology is linked to the underlying legacy data leveraging the wellestablished ontologybased data access (OBDA) paradigm. On the other hand, our framework allows one to enrich the ontology through useroriented log extraction annotations, which can be flexibly used to provide different logoriented views over the data. Different data access modes are then devised so as to view the legacy data through the lens of XES.