# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 9 Related Work  
In this paper, we are concerned with characteristing concept drift analysis approaches, that is, the input an approach needs, the analysis task, change form, and drift type the approach is concerned with, and the scale and timing at which the drift is analysed. We are further concerned with providing a framework that defines the basic requirements of a concept drift analysis approach. To the best of our knowledge, no other SLR conducted on concept drift analysis in process mining exits. However, there are some literature reviews that were conducted on related topics in process mining as well as related research areas. In addition, there are related fields that we consider complementary to gain an understanding on how changes and variances in a business process are studied. In the field of change mining, corresponding approaches are more concerned with change discovery and cartography, rather than detecting or predicting a change. A change log generated by a PAIS like ADEPT or CBRFlow [18], is taken as input to be analysed along with the execution log. The former log [18] captures the process changes performed on some process instances. A change log instance is a sequence of process changes performed on a process instance. Ideally, change logs keep information about high-level changes (i.e., adding, inserting, deleting, or moving events) performed, instead of low-level change primitives (e.g., addNode, deleteEdge). As a remarkable research effort in this direction, Reference [51] evolves the idea of change mining processes to produce a change tree including the number of occurrences of a certain change sequence. This helps predicting how a change sequence would evolve and whether a change deserves to be leveraged to the level of the process type rather than the process instance level. The authors of [52] provide an approach using change trees with the possibility to base the analysis of change sequences not only on the similarity between labels, but also on contextual similarities, like the usage of the same resources or comparable execution times. A related review is presented in [16], which surveys deviance mining approaches based on sequence mining. The survey summarizes 48 papers and classifies them. From this, five basic approaches are identified. The selected approaches address event log abstraction based on the occurrence count of individual activities or frequent set of trace attributes or features of sequence of events occurring multiple times in a process instance or across different instances. The authors of [16] study seven feature sets derived from the five selected approaches, and evaluates them using five real-life event logs. Fisher score is used for computing weights of statistical correlation of a feature with the outcome classes. Afterwards, a model is constructed using a variety of methods including decision trees, neural networks, and K-Nearest Neighbours (KNN). Finally, classification accuracy and the interestingness of rules extracted to explain the reasons of the deviance are assessed. In turn, the authors of [53] provide a comprehensive survey of business process variability and provides a comprehensive framework for evaluating process variability approaches. The former is derived based on analysing 63 collected studies from literature. The purpose of the resulting _VIVACE_ framework is to analyse how different process variability approaches support process variability through a business process lifecycle. This evaluation is based on the extent to which each evaluated approach realizes a set of features in each BPM stage. _VIVACE_ captures the business process perspectives covered by a process variability approach, the variability-specific language constructs are provided for specifying variability, the existence of a tool implementing the approach, the type of empirical evaluation performed to test the approach, and the domain in which the analysed approach has been applied. _VIVACE_ is applied to three process variability approaches.