## 2 Related Work
### Event Log Quality  
Event log quality has been identified as a critical issue that affects process mining results from many domains in both process discovery and enhancement [1; 5]. The process mining manifesto [9] has emphasised the importance of event log quality. The first guideline for process mining is to treat event data as first-class citizens. Later on, [PERSON] et al. [10] outlined 11 common event log imperfection patterns, such as incorrect timestamps and redundant labels. [PERSON] et al. [24] and [PERSON] et al. [25] suggested quality frameworks for assessing EMR data in the healthcare domain. Additionally, [PERSON] et al. [26] and [PERSON] [27] raised concerns for event data quality in process mining. Therefore, apart from proposing advanced process discovery, it is also essential to address data quality as early as the event log level. However, compared with process discovery, less effort has been made to improve event data quality. Event log quality can be improved by detecting erroneous data and potentially repairing it by relying on a reference model or observed correct data [28]. [PERSON] et al. [28] proposed to identify and repair events with the same timestamp by using information from correctly ordered events in the log. [PERSON] et al. designed an approach to identify and restore missing events by using a reference process model annotated with execution times. Similarly, [PERSON] et al. [29] proposed likelihood-based multiple imputations by event chains to repair missing events in logs. [PERSON] et al. [30] proposed an interval-based event selection method to reduce variations in event logs by targeting the behaviour of repeated activities. In order to detect redundant activity labels, relevant works [11; 13; 14] suggest ways to address this issue at the event log level. [PERSON] et al. [11] proposed a contextual approach that takes control-flow relations, resources, time, and data attributes into consideration. From the control-flow perspective, the method reports the similarity between rows of the footprint matrix, which may not well distinguish the frequency difference between two activity labels and suffers from noisy or infrequent relations. Thus, the method achieves relatively low accuracy with low-occurrence-frequency activity labels. Other methods largely adopt a probability density function to assess the value distributions between activity labels. This approach reports relatively poor results if there are numerical values as activity attributes, and this is a common phenomenon in a real-life event log. For instance, healthcare logs contain various laboratory tests and medications as activity labels. It relies on a weighted clustering method to combine the final results, which requires domain knowledge or ground truth to determine the best weight setting. The other method [13; 14] collaboratively and interactively detects problematic activity labels by adopting a gamified crowdsourcing approach, which utilises gamification elements (e.g., badges) to encourage a large group of domain experts to identify and repair redundant activity labels. The issue of activity labels has also been studied in process matching areas at the model level [31; 32; 33; 34]. These approaches match two process models from different data sources with the aim to find similar structures and activity labels. It is difficult to address redundant labels within the same log because separated logs may have incomplete processes. Hence, they are more widely used in a process similarity comparison instead of solving problematic event logs. Other approaches [35; 36] look at activity labels themselves while ignoring other information from logs, which may cause erroneous results. Though previous studies have made efforts to address redundant activity labels [11; 13; 14], many of them have difficulties identifying activity labels with low-occurrence frequencies, or if invalid labels have been used. However, redundant labels usually occupy a small portion of event logs.