# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 3 Concept Drift in Process Mining
### Categories of Concept Drift Analysis
#### 3.2.1 Concept Drift Types  
An important component of concept drift analysis process is realising that concept drift can take different forms. As will be illustrated through the SLR and CONDA-PM, choosing the right concept drift analysis technique is correlated with the type of concept drift addressed. Choosing unsuitable technique can result in ignoring important patterns in the data that may result in detecting a different type of the drift other than the one present in the data under analysis. Figure 1 illustrates these different concept drift patterns. Regarding _sudden concept drift_, a change takes place dynamically during the execution of a process instance. A sudden drift may affect the way or the point in time an activity may be executed, the resources performing the activity, or any other process perspective concerned by a change. As example on a sudden drift consider the change of the process of uploading a video to any media streaming website to ensure the ownership of this media as a consequence of the European copyright legislation. Gradual concept drift, in turn, is characterised by the co-existence of two or more versions of the process; that is, the old process version exists along with the new one, over a certain period of time, and hence, process instances may be executed according to the old or the new version. Afterwards, the new version operates solely, that is, the process instances may be only created from the new version. For example, an organisation O1 from the field of consultation services is acquired by an  
Figure 1: Different types of concept drifts (adopted from Reference [13]). organisation O2 adopting another customer service process model M2. The process model of the acquiring organisation M2 will be applicable only to new customers of O1, whereas the current consultation traces that have not been completed before the acquisition follow the customer service model of O1. After completing all current traces, process model M1 of O1 is no longer used. _Recurring concept drift_ can be defined as a drift taking place over a certain period of time. Afterwards, the process switches back to its former version. This drift is caused by the process context or environment. As example, consider occasional changes of a marketing process during a certain period of the year, for example, to offer reductions for certain products during public holidays based on the country or the state of the customer. Note that the duration of the offer and the period it is applied are both subject to external factors. _Incremental concept drift_ takes place when a change is introduced incrementally into the running process until the process reaches the desired version. An example of incremental drift is the incremental introduction of self-service terminals for paying fines at toll stations [8]. Figure 2 illustrates the fifth type of concept drift, as introduced in Reference [21], namely _multi-order drift_. The latter involves changes on two granularity levels, that is, micro and macro level. As illustrated in Figure 2, M1 and M2 represent the macro level of the change to be introduced to a business process; that is, M1 represents the current process an organisation adopts and M2 corresponds to a new process to be adopted over a time period of, for example, 12 months. M1 is further divided into M11 and M12, and M2 into M21 and M22, respectively. The resulting divisions of M1 and M2 represent the micro level of the change; they allow introducing the total change as chunks of different periods of time, rather than introducing M1/M2 suddenly. When M1 becomes fully adopted, M11 and M12 will recur, each through a period of 6 weeks. After 6 months, M2 will be adopted and its variants (i.e., M21 and M22) will recur every 6 weeks. Regarding this type of concept drift, different population sizes (i.e., volumes of traces) are considered as well as different time scales.