# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 5 Concept Drift Analysis Approaches in Process Mining
### Graph-Based Analysis Approaches  
As a common theme of approaches in this category, visualisations are created as an intermediate step in analysing drifts. The authors of [5] propose an approach for computing mappings between data and time-based partially ordered traces. Time-based partially ordered traces are used to identify dependencies between events. Note that data-based partially ordered traces enable partial similarity rather than complete similarity of traces and can enable localising drift detection to deviating events. Furthermore, partially ordered traces enable a more precise expression of causal dependencies, uncertainty, and co-occurrence of traces [48]. First, the data-based partial ordering over events means that two events are dependent if they access the same data attributes [5]. Time-based partial ordering over events, in turn, means that two events are dependent if they have different timestamps, that is, the event with the earlier timestamp is supposed to lead to the occurrence of the event with later timestamp, as they are considered to be concurrent [48].  
To compute mappings, the authors of [5] introduce two algorithms. One algorithm uses backtracking with a heuristic function, whereas the other one is a greedy algorithm. Such mapping depends on both local and global similarity measures. On one hand, local similarity between two events is concerned with their properties and local execution contexts which can be measured based on the similarity of neighbours of these events. On the other side, global similarity concerns the similarity of the events' positions in the execution graphs. Defining local and global similarity measures for each event allows providing contextual information about the events, rather than just considering the similarity of an event with another one as in the case of sequential alignment. These mappings are then used to compute _REGs_ for traces with similar behaviour as well as relations between the activities. The REGs can then be used to locate and visualise dissimilar behaviour, while giving insights into strongly supported behaviour. The authors of [5] argue that the accuracy of the results obtained from the backtracking mapping approach with data-based partially ordered traces is better than the one of the results obtained when sequential ordering is applied. However, when detecting an increasing number of deviations, the inaccuracy of the presented approach is clear. Finally, when applying the proposed approaches to real-life logs, existing approaches perform better with respect to deviation detection accuracy.  
Finally, the authors of [27] introduce an approach dicing an event log based on process perspectives other than control flow. Different versions of the same process can be compared along any perspective by constructing _perspective graphs_. The resulting perspective graphs are then compared leading to _differential perspective graphs_. Although flexible ways for exploring event logs, belonging to the same process are provided, the approach presumes that an event log follows a basic structure that enables the proposed exploration.