## 7 Conclusions and Future Work  
Existing process discovery algorithms assume that each activity label in the event log is unique and meaningful. The existence of redundant activity labels introduces unnecessary  
Figure 11: The process model discovered from the preprocessed event log.  
Figure 10: A snapshot of the partial process model discovered from the original event log.  
complexities to the discovered process models, which leads to spaghetti-like models. Hence, this paper proposes a multi-view framework to accurately detect redundant activity labels and produce more representative event logs for process mining. Our framework considers information from different views (i.e., control-flow relations, attribute values, and label semantic information) when detecting redundant activity labels. A consensus is made through the majority voting mechanism. The results are superior to those of the existing method in terms of detecting redundant activity labels. The usability of our framework is further demonstrated by using a real-life case study based on Speed-Extract EMR dataset.  
Of course, the study described in this paper also comes with its very own limitations. It has to be noted that, like other data preprocessing approaches, the detection results still vary under different parameter settings in different logs. The adopted NLP model is not designed explicitly for process mining tasks, which may impact the performance of the framework.  
In future work, first, we plan to investigate how different parameter settings can impact the framework and to develop a method to automatically determine thresholds for different views. Secondly, we also aim to incorporate the NLP technique to automatically repair redundant activity labels by preserving the same contexts and by categorising differences according to their closest synonyms. Finally, we plan to investigate the feasibility of applying our framework in other domains.  
Conceptualisation and methodology, Q.C., Y.L., C.S.T., and S.K.P.; development, Q.C.; validation, Q.C., Y.L., C.S.T., and S.K.P.; writing--original draft preparation, Q.C.; writing--review and editing, Q.C., Y.L., C.S.T., and S.K.P.; supervision, C.S.T. and S.K.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethics approval for the study (2019/ETH09692) was provided by the Northern Sydney Local Health District (NSLHD) Human Research Ethics Committee (HREC). Governance approvals were provided by the NSLHD and Central Coast Local Health District HRECs.  
We received a waiver of consent for the patients in this study, which was approved by the NSLHD Human Research Ethics Committee. Only de-identified data were used by the researchers for this analysis, so a waiver of consent was appropriate.  
All datasets used in Section 5 to evaluate the proposed framework are publicly available. Please refer to notes for links to access the datasets. The datasets generated and/or analysed in Section 6 are not publicly available, as they are owned by the Chief Executives of the LHDs and not by the researchers who performed this study.  
The authors thank the members of the Speed-Extract research team and the technical assistance provided by the Sydney Informatics Hub, a Core Research Facility of the University of Sydney. The authors also acknowledge ICT services at NSLHD. The authors also thank the Agency for Clinical Innovation, the NSW Ministry of Health, the Sydney Health Partners, and Health Innovation Funding for funding the Speed-Extract study.  
The authors declare that there are no conflict of interest.