# 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 c c c c c c} \hline \hline \multirow{2}{*}{**Publication**} & \multicolumn{2}{c}{**Input**} & \multicolumn{2}{c}{**Regarded Perspectives**} & \multicolumn{4}{c}{**Patterns of Change**} \\ \cline{2-10}  & **Event log** & **Process Model** & **Control-Flow** & **Data** & **Resources** & **Time** & **Sudden** & **Gradual** & **Recurring** & **Incremental** & **Multi-Order** \\ \hline S1 & Event Streams & & ✓ & ✓ & & & ✓ & ✓ & ✓ & ✓ & \\ \hline S2 & ✓ & ✓ & & & & ✓ & ✓ & ✓ & ✓ & \\ \hline S3 & Event Streams & & ✓ & & & ✓ & ✓ & ✓ & ✓ & \\ \hline S4 & ✓ & & ✓ & ✓ & ✓ & ✓ & & & (NA) & \\ \hline S5 & ✓ & & ✓ & & & & ✓ & ✓ & & \\ \hline S6 \& S13 & ✓ & & ✓ & ✓ & ✓ & & (NA) & \\ \hline S7 & ✓ & & ✓ & ✓ & ✓ & ✓ & & (NA) & \\ \hline S8 & ✓ & & ✓ & & & & ✓ & & \\ \hline S9 & ✓ & ✓ & ✓ & & & & & (NA) & \\ \hline S10 & ✓ & & ✓ & & & & & (NA) & \\ \hline S11 & ✓ & & ✓ & & & & & (NA) & \\ \hline S12 & ✓ & & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline S14 & ✓ & & ✓ & & & ✓ & ✓ & ✓ & \\ \hline S15 & ✓ & ✓ & ✓ & & & & (NA) & \\ \hline S16 & ✓ & ✓ & ✓ & & & & & (NA) & \\ \hline S17 & ✓ & & ✓ & & & ✓ & & \\ \hline S18 & ✓ & & ✓ & & ✓ & & (NA) & \\ \hline S19 & ✓ & & ✓ & & & & (NA) & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Applying the dimensions of the input investigation sub-phase to the selected studies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Publication**} & \multicolumn{3}{c}{**Study Focus**} & \multicolumn{2}{c}{**Change Form**} & \multirow{2}{*}{**Used Techniques**} \\ \cline{2-2} \cline{4-6}  & **Detection** & **Localisation** & **Evolution** & **Behavioral** & **Structural** & \\ \hline S1 & ✓ & ✓ & & ✓ & & Sliding window, Statistical Analysis for outlier detection \& Serialisation in YAML \\ \hline  & & & & & & Sliding window, Hierarchical Clustering, Change point detection using PELT \\ S2 & ✓ & ✓ & ✓ & ✓ & ✓ & \& Declarative constraints discovery using MINERFUL \\ \hline  & & & & & Sliding window, Stream-Based Abstract Representation (SBAR), Hashables, \\ S3 & ✓ & & & & ✓ & Inductive Miner \\ \hline  & & & & & & Perspective graphs \& finding statistical differences between them to result in differential perspective graphs \\ S4 & & (NA) & & & ✓ & Adaptive windowing, statistical testing (Chi-Square test) \& oscillation filter \\ \hline S5 & ✓ & ✓ & ✓ & ✓ & ✓ & Adaptive windowing, statistical testing (Chi-Square test) \& oscillation filter \\ \hline  & & & & & ✓ & MCL \& cosine similarity \\ \hline  & & & & & & Recursive Partitioning by Conditional Inference (RFCI), \\ S7 & & (NA) & & ✓ & & points of interest \& event augmentation \\ \hline S8 & ✓ & & & ✓ & DBSCAN clustering \\ S9 & ✓ & ✓ & & & ✓ & Adaptive windowing, Heuristic mining \& C-Tests \\ S10 & ✓ & ✓ & & ✓ & ✓ & Backtracking, heuristic function, a greedy algorithm, cost function \& RECs \\ \hline S11 & ✓ & ✓ & & ✓ & ✓ & Adaptive windowing, statistical testing (G-test of independence) \\ \hline S12 & ✓ & ✓ & ✓ & ✓ & ✓ & Adaptation of ADWIN approach \\ \hline  & & & & & Statistical hypotheses testing using Kolmogorov-Smirnov (KS) test, Mann-Whitney \\ S14 & ✓ & ✓ & ✓ & ✓ & & test, Hotelling T2 test \& windowing \\ \hline S15 & ✓ & ✓ & & ✓ & Alignment matrix \\ \hline S16 & & ✓ & & ✓ & Vertical \& horizontal partitioning \\ \hline S17 & ✓ & & Proposed Ideas & ✓ & Abstract interpretation, Parikh vectors \& Adaptive windowing \\ \hline S18 & ✓ & & & ✓ & Euclidean Distance \& Agglomerative Hierarchical Clustering (AHC) \\ \hline S19 & ✓ & & & ✓ & Trace Clustering / divide \& conquer (trace profiles) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Applying the dimensions of the processing sub-phase to the selected studies.