## 1 Introduction  
Process mining combines traditional model-based process analysis and data-centric mining techniques [1]. It is a technology known to be useful for understanding business processes and constructing process models by using event logs captured in information systems [2]. Process mining includes process discovery, conformance checking, and enhancement [3]. Among them, process discovery is a paramount task that aims at automatically discovering process models from structured event logs to analyse and improve the internal business processes [4]. Process mining has shown promising potential in many aspects, such as discovering significant insights and improving process performances [5]. A typical event log refers to a collection of events, each with a timestamp that records the executed time. An event represents a unique execution of an activity, which is a well-defined step in the process, such as \"doctor appointment\". Cases group these events, also called process instances. For example, a case could be a patient who follows a treatment process in a hospital.  
In recent years, with an effort being made to discover accurate and comprehensible process models from structured and clean event logs, many advanced discovery algorithms have been proposed, such as the Heuristic Miner [6] and Split Miner [7]. However, like other data mining technologies, the quality of input data (event logs) has a great impact on the resulting model [2]. Moreover, real-life event logs can suffer various data-quality issues and lead to \"spaghetti-like\" business models, which refer to models that have numerous activities and intricate relations. Such spaghetti-like' models are too complex to comprehend easily even for domain experts [8]. The process mining manifesto [9] has emphasised the importance of event log quality in process discovery. The first guideline for process mining is to treat event data as first-class citizens. [PERSON] et al. [10] summarised 11 event log imperfection patterns and pointed out that data quality on activity labels in event logs is unique in process mining research, as the quality can be affected by integrating data sources or discrepancies of labelling in the same information system. One particular imperfection pattern is redundant activity labels, which refers to labels that have different syntax but share the same behaviours [11]. Two labels are recognised as sharing the same behaviours not only if they play identical roles in process models but also if they represent the same activity in reality. One contributing factor to such redundancy is data integration from separate systems, e.g., electronic medical records (EMR) from different hospitals, because multiple systems use different labels for the same activity. The other is the free-text input or human error in providing an initial suggestion in the same system [11]. Redundant activity labels are commonly observed in real-life event logs. For instance, nearly 30% of activity labels in observation tests are considered redundant in the publicly available MIMIC-III database [12]. Event logs with such redundant activity labels can suffer a significant quality loss in the discovered process model, because most existing process discovery algorithms assume that every activity label in the event log is meaningful and unique. Several methods are proposed in the field of process mining to detect redundant activity labels in event logs [11; 13; 14]. However, none of these methods can accurately detect redundant activity labels in event logs without domain knowledge, especially when the redundant activity labels are less frequent and contain numerical values as attributes.  
In this paper, we propose a multi-view framework that efficiently incorporates the control-flow relations, attribute values, and label semantic information of each activity label to detect redundancy in event logs. A consensus is then guided by a majority voting mechanism to integrate results produced from multiple views.