# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 7 CONDA-PM Application and Evaluation
Further, they then apply a statistical method to study the differences in distributions between multiple windows of the event log. Table 10 attempts to provide more insights into the contributions and outputs of the selected studies. We checked whether there exist implementations of the proposed approaches, which we consider as both an indicator of the technical feasibility of an approach and its further usage. Most implementations are made available as plugins or packages in ProM. Despite the availability of Apromore as an open source business process analytics platform, only the implementations of approaches in S5 and S11 are integrated into Apromore. Other important dimensions include the evaluation inputs and criteria of each proposed approach. As stated before, most of the collected studies use real-life logs for evaluating different aspects of the approaches. However, most studies fail in demonstrating the performance costs of applying the proposed approaches, for example, the time consumed for running the tool as well as the effects of log size or studied drift patterns or handling modes on different performance measurements. There is a lack of studies on time, performance, memory and other costs related to tasks associated with concept drift analysis, for example, detection, localisation, and evolution analysis. In order to evaluate the maturity of the selected studies along the scales defined for each dimension of CONDA-PM, we constructed Table 11. This table indicates maturity in terms of numbers and categorises the SLR criteria along the four phases of CONDA-PM. Concerning the _goal design_ phase, we notice that S1, S3, S11, S12 and S17 have the highest scores regarding the defined maturity scales, whereas for the _input investigation_ sub-phase, S1 and S12 show the highest maturity in providing approaches that reflect a higher understanding of the inputs in hand. Regarding the _processing_ sub-phase, S2, S5, S10, S12 and S14 show a high maturity level regarding the comprehensiveness of manipulating inputs to reach the defined goals. Note that this maturity scale is complemented by the analysis provided in Section 5 and the description of techniques available in Table 9. Finally, S2, S5, S12, and S14 satisfy the study focus dimension. We observed a relation between the absence of clearly specifying a study focus and regarding variability as a theme of the study in S14 and S15. Concerning the _implementation_ phase, S5, S11, and S12 are dropped down on the maturity scale as no implementation is available for these approaches. Regarding the _evaluation_ phase, approaches that use artificial logs side-by-side with real-life logs have the highest scores. These findings are complemented with the descriptions of the evaluation process (cf. Table 10). Although the number of studies is rather low, each one is providing a novel approach, meaning that the topic deserves more in-depth exploration. Still, there is potential to develop complementary approaches to a solution of concept drift analysis in process mining.