# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## Appendix A
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dimension** & **Value** & **Papers** \\ \hline Drift type & Concept Drift (General) & [31,33,36,50,60,61,98,100,107,108,111,118,128,133,144], \\  & \multicolumn{1}{c}{} & [48] \({}^{\star}\), [38] \({}^{\star}\), [141] \({}^{\star}\), [130] \({}^{\star}\), [104] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Sudden Concept Drift} & [117], [114] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Gradual Concept Drift} & [117], [114] \({}^{\star}\), [127] \\ \cline{2-3}  & \multicolumn{1}{c}{Data Drift} & [59,105] \\ \hline Application Context & \multicolumn{1}{c}{Online Community} & [107,128] \\ \cline{2-3}  & \multicolumn{1}{c}{Cybersecurity} & [31,33,60,117,127] \\ \cline{2-3}  & \multicolumn{1}{c}{Generic Data Stream} & [50,100,108,111,144] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Marine Science} & [114] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Process Mining} & [104] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Energy} & [59,98] \\ \cline{2-3}  & \multicolumn{1}{c}{Education} & [118], [48] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Human-Activity Recognition} & [36] \\ \hline IoT & [61], [38] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Time-Series Forecasting} & [144] \\ \cline{2-3}  & \multicolumn{1}{c}{Retail} & [133], [48] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Healthcare} & [48] \({}^{\star}\), [105] \\ \cline{2-3}  & \multicolumn{1}{c}{Financial} & [48] \({}^{\star}\), [141] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Server Monitoring} & [130] \({}^{\star}\) \\ \hline \multirow{2}{*}{Scope} & \multirow{2}{*}{Local} & [33,36,59,60,61,98,107,111,117,127,128,133], [48] \({}^{\star}\), [38] \({}^{\star}\), [38] \({}^{\star}\), [104] \({}^{\star}\), [141] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Global} & [31,50,60,98,100,108,111,1118,133,144], [114] \({}^{\star}\), [38] \({}^{\star}\), [48] \({}^{\star}\), [130] \({}^{\star}\) \\ \hline Audience & \multicolumn{1}{c}{End-Users} & [107,118,128] \\ \cline{2-3}  & \multicolumn{1}{c}{Data Scientists} & [31,33,36,50,59,61,98,100,105,108,111,117,127,133,144], \\ \cline{2-3}  & \multicolumn{1}{c}{[114] \({}^{\star}\), [48] \({}^{\star}\), [38] \({}^{\star}\), [141] \({}^{\star}\), [130] \({}^{\star}\), [104] \({}^{\star}\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Classification of Rule/Logical Pattern literature based on key dimensions. \begin{table}
\begin{tabular}{l l l} \hline
**Dimension** & **Value** & **Papers** \\ \hline \multirow{2}{*}{Metrics} & Feature Agreement & [118] \\ \cline{2-3}  & Fidelity–Sparsity & [38]\({}^{\star}\) \\ \hline \multirow{2}{*}{Learning Mode} & Online & [50,61,98,108,111,117,133,144], [114]\({}^{\star}\), [141]\({}^{\star}\), [104]\({}^{\star}\) \\ \cline{2-3}  & Hybrid & [36,59,107,127,128], [38]\({}^{\star}\), [130]\({}^{\star}\) \\ \cline{2-3}  & Offline & [33,60,100,105,118], [48]\({}^{\star}\) \\ \hline \end{tabular}
\end{table} 
Table 13: Classification of Fuzzy Transparency literature based on key dimensions.