# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 4 Research Methodology
### Highlights after Data Extraction
Figure 8: Number of publications classified by different types of concept drift. Figure 6: Distribution of publications over regarded perspectives. Figure 7: Number of publications classified by whether or not concept drift type is stated. The core of any approach is represented by the algorithm or techniques it provides to process its inputs such that the desired outcomes can be obtained. When tracing back the relation between inputs, processing, and outcomes,therefore, we visualise the concept drift analysis tasks being of interest to the selected studies and show which change forms are mostly addressed by the studies. We plot the distribution of selected studies over change forms (RQ6), indicating concept drift tasks. As shown in Figure 9, 17 studies address behavioural changes, whereas only one deals with structural changes. One study addresses both forms. This observation complies with the previous observation on the types of inputs used by the various approaches (16 studies take only event logs as input, whereas, three studies take process models into account as well). S9 is one of the three studies that take both an event log and a process model as input, and use the available process model for analysing structural changes. Figure 10 allows for several observations. _First_, the majority of studies (i.e., nine studies) dedicate efforts for detecting and localising concept drifts. This can be explained with the fact that the use of machine learning algorithms and probabilistic techniques facilitates these tasks if the input (i.e., event log) is rich with information about the state of an activity on a vertical spectrum (i.e., at the current point in time there is enough payload enabling inference about the activity) as well as on a horizontal spectrum (i.e., there are enough process instances that can be used to study the change in the state of an activity). _Second_, four studies either directly or indirectly aim at investigating the evolution of drifts and putting a detected drift into the context of the whole process. This trend allows studying the effect of a drift on the process and enables different other tasks to be accomplished accurately, for example, making more accurate predictions about the outcome or next activity of a running process instance. From a practical perspective, it is also important to have insights into the platforms used for implementing concept drift analysis approaches (RQ3) as well as the artefacts used for evaluating the latter (RQ7). Figure 11 plots the selected studies over different platforms. As can be observed, 47% of the studies (i.e., nine studies) use ProM [38] as a platform. This can be explained with the fact that ProM is an open source testbed for all process mining-related approaches. Moreover, some of the approaches rely on other ProM packages for preprocessing the used event logs. A big share of the studies either implement their approach as proof-of-concept prototypes (five studies) or do not provide any implementation of the approach (three studies). Figure 12 plots the artefacts used for  
Figure 10: Number of publications classified by study focus. Figure 9: Number of publications classified by studied change form. evaluating the approach. 52% of them (i.e., ten studies) use both an artificial event log for preliminary experiments and real-life logs for demonstrating the capabilities of the proposed approaches. Real-life logs [39; 40; 41; 42; 43; 44; 45; 46] are possibly available from Business Process Intelligence Challenges (BPIC) in the years 2011 to 2019. Furthermore, among the factors contributing to real-life logs availability is the increased awareness of business entities about the importance of process mining and the inherent benefits of its application, for example, gaining more accurate insights into real-life business processes and tuning these processes accordingly.