# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 7 CONDA-PM Application and Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Publi-Cation**} & \multirow{2}{*}{**Tool Availability**} & \multicolumn{2}{c}{**Evaluation Input**} & \multirow{2}{*}{**Evaluation (What/How)**} \\ \cline{4-4} \cline{6-6}  & & **Artificial** & **Real-life** & \\ \hline \multirow{4}{*}{S12} & \multirow{4}{*}{ConceptDiff plugin in ProM} & Insurance claim (sudden \& multi-order changes: & **What**: measuring the ability to detect \& localise changes with a lag window of different traces no. \\  & & & \& multi-order changes: & **How**: adaptive windowing using the \\  & & 600 cases -15 activities & Dutch municipality\(-\)184 & Kolmogorov test over the data stream then \\  & & -5873 events -15 activities & cases –38 activities\(-\)4391 & classic data mining metrics are derived. -For the multi-order drifts \& gradual drifts: different \\  & & drifts: 200 cases–15 activities -19346 events & configurations using different population & sizes, step sizes, \(\rho\)-value threshold \& gap sizes \\  & & & -5 examples of linear \& exponential gradual drifts \\ \hline \multirow{4}{*}{S14} & \multirow{4}{*}{Concept Drift plugin in ProM} & Insurance claim (600 cases -15 activities -235 events) & municipality (116 cases -235 events) & The ability to detect (using RC feature) –influence of population size –Time complexity for feature extraction \& hypothesis test analysis \\ \hline \multirow{4}{*}{S15} & \multirow{4}{*}{Comparison framework package in ProM} & CoSeLeG project –5 municipalities & What: Comparison visualization understandability & \\  & & & municipalities & **How**: Meetings with representatives of municipalities- comparison of replay fitness scores \& visualization \\ \hline \multirow{4}{*}{S16} & \multirow{4}{*}{(NA)} & Illustrative examples & & (NA) \\ \hline \multirow{4}{*}{S17} & \multirow{4}{*}{(NA)} & \multirow{4}{*}{13 models are used to derive logs with drifts} & \multirow{4}{*}{1050 cases–35 activities} & Concatenation of two logs with different distributions \& measuring the approach’s ability to detect the change, with estimation of the number of points needed to sample for change detection. & \\  & & & & **What**: the accuracy at which each \\  & & & & approach is able to classify different traces. \\  & & & & **How**: approaches (without time factor –with \\  & & cases–over 1 year & time is weighted against the no. of elements in the feature set) were applied on the 3 logs –an & accuracy metric is calculated. \\ \hline \multirow{4}{*}{S19} & \multirow{4}{*}{Trace Clustering plugin in ProM} & AMC hospital in Amsterdam (619 cases–52 activities–3574 events) & Different combinations of distance measures \& clustering techniques–the understandability of resulting models with the aid of domain experts \\  & & & & \\ \cline{1-1}  & & & & \\ \end{tabular}
\end{table} 
Table 10: \(Cont.\)From Table 9 we may conclude that structural changes are not comprehensively studied, whereas focus is put on studying behavioural changes of a process. This can be explained with the challenges facing process model similarity. Representing same behaviour with different structures, differences in labeling styles, and terminology differences all illustrate these challenges [50]. A study considering behavioural and structural changes together may provide a comprehensive approach to explain the semantics of a change and might allow studying the contextual influence on a changing business process. Concerning the techniques used by each approach, we find that these techniques primarily depend on the kind of input, considered process perspectives, and the change form studied. Note that existing techniques mostly address drift analysis rather than reasoning about the drift. This supports our conclusion that there is not enough research on the reasons of drifts and their context. However, note that studies on the evolution of drifts and the way these drifts contribute to the change of the business process use sliding windows as a technique to partition the event log.