# Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis
## 8 Conclusions  
Process mining techniques enable organisations to gain insights into their processes through the use of event data collected during the execution of those processes. Process mining techniques are focused on three main areas, (i) _process discovery_, dealing with the discovery of the process models based on the behaviour of events, (ii) _conformance analysis_, identifying inconsistencies between actual process executions and behaviour of the process observed through the discovered process model, and (iii) _process enhancement_, establishing a feedback mechanism for the improvement of process model through learnings gained from the conformance analysis. Conformance analysis gives a 360-degree view of the retrieved event log for analysing anomalous business process behaviour. This paper presents novel techniques of conformance analysis for a deep dive analysis of the real-world complex business processes for service-oriented organisations. Individual fitness trends for process instances in the event log are examined for early detection of abnormalities in real-world business processes. The fitness trend enables failure forecasting as well as root cause investigation of issues that cause failure in a business process. Three conformance analysis techniques from the literature are used to find similarities and relevant discrepancies between simulated and observed behaviour in process logs. Using a real-world business process, the proposed framework and conformance techniques are validated. This work focuses on service-oriented business processes whose execution traces are challenging to mine. The results of 'case-level' and 'event-level' conformance testing are presented to illustrate the variances in patterns across various types of process instances. A conformance analysis solution is augmented with root cause analysis for abnormality prediction based on a decreasing fitness score. The concept of a 'conformance lifeline' is introduced for the early prediction of process instance execution anomalies. As the proposed techniques are based on historic event logs with a labelled outcome, yet in the future, we will explore the opportunities to include autonomous techniques for the identification of the success or failure of an execution business process.  
Conceptualisation, Z.T.; methodology, Z.T.; software, Z.T.; validation, Z.T., D.C. and S.M.; formal analysis, Z.T. and S.M.; investigation, Z.T.; resources, Z.T.; data curation, Z.T. and P.T.; writing--original draft preparation, Z.T.; writing--review and editing, Z.T., D.C., S.M., I.M. and P.T.; visualisation, Z.T.; supervision, D.C., S.M., I.M. and P.T.; project administration, S.M. and P.T.; funding acquisition, S.M. and P.T. All authors have read and agreed to the published version of the manuscript.  
This research is sponsored by BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland.  
Not applicable.  
This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland.  
Conflicts of Interest: [PERSON] and [PERSON] are guest editors of the Special Issue \"Process Mining and Its Applications\" and are also co-authors in this research.