# Detecting and Responding to Concept Drift in Business Processes
## 2 Related Work  
### Process Mining  
Concept drifts refer to data changes over time, which have been classified into four categories in the literature [20]. Sudden drift occurs abruptly at a specific time point with one model replaced by another one, while in a gradual drift scenario, the current model is taken over by a new model gradually. The drift type of recurring concept relates to situations in which previously seen concepts reoccur after some time. Incremental drift refers to a sequence of changes to reach a stable new model from the current model. In this paper, we consider recurring and incremental drift as special types of sudden and gradual drift, respectively [20; 21]. In the context of process mining (PM), concept drift detection techniques can be roughly classified into online and offline learning approaches [22]. The former concentrate on new incoming data batches, with the objective to quickly respond to changes [22]. The latter detectd concept drift in history data, aiming for accurate data modelling and understanding [16]. [PERSON] et al. [23] introduced an online approach aiming at detecting changes in its early stage. [PERSON] et al. [24] reviewed the current state of online process mining, focusing on evaluation goals and event logs for drift detection. In [25], [PERSON] et al. discussed the significance of understanding the root causes of concept drift. Sliding windows are commonly applied in offline scenarios to generate continuous populations [1; 5; 26] where changes are examined between consecutive pairs. The follows/precedes relationship, introduced by [PERSON] et al. [1], investigates whether process activity 'a' always, sometimes or never follows activity 'b'. Thereafter, Hotelling \(T^{2}\), KS and MW tests were employed to uncover process drift. [PERSON] et al. [26] restructured process instances into RUNs as the representation of customer pathways. The Chi-square test is then used to detect changes. Clustering methods such as AHC [27], Markov clustering [28] and DBSCAN [29] are also commonly employed for process drift detection. [PERSON] et al. [24] proposed the Visual Drift Detection (VDD) approach with the capability of users visually observing drift. In addition to drift detection, concept drift adaptation is essential to ensure that the learned model/customer profile is suitable for the dynamic environment [21]. [PERSON] et al. [30] proposed a framework for monitoring concept drift in process event streams. The method first represents process data into a trace histogram and a time histogram. Process cases can then be transformed into feature vectors to obtain the cluster boundary. New process cases that fall outside the boundary of any cluster are detected as anomalies. Finally, concept drift is considered to occur if there is a significant increase in the number of anomalies. The approach is then extended for real-time reaction and concept drift adaptation [31]. The method addresses drifts in the time difference/duration between activities but does not distinguish the difference between different activity transitions. [PERSON] and [PERSON] [32] proposed a concept drift adaption method with the goal of finding process bottlenecks in cases that have a long duration. The training process cases are partitioned into a set of groups to build an ensemble-based drift detection model. The approach can adapt to gradual and recurrent concept drift, but it only concentrates on the overall duration, neglecting the duration of specific transitions. In [21], [PERSON] et al. proposed a method for dealing with concept drift in predictive process monitoring. The approach uses incremental learning to adapt the forecast model to control flow and data concept drift, but there is a lack of duration perspective. The proposed HMDP approach can automatically adapt to concept drift, i.e., update customer profiles under changes in customer transition probabilities and their duration.