# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## Appendix A
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Users** & **Link** \\ \hline Electricity & [143] & [[https://www.openml.org/d/151](https://www.openml.org/d/151)]([https://www.openml.org/d/151](https://www.openml.org/d/151)) (accessed on 29 May 2025) \\ California & [143] & [[https://www.dcc.fc.up.pt/](https://www.dcc.fc.up.pt/)]([https://www.dcc.fc.up.pt/](https://www.dcc.fc.up.pt/))\textasciasciasciitilde{}loving/cal_housing.html (accessed on 29 May 2025) \\ CICMalDroid2020 & [45] & [[https://www.kaggle.com/datasets/hasanccr92/cicmaldroid-2020](https://www.kaggle.com/datasets/hasanccr92/cicmaldroid-2020)]([https://www.kaggle.com/datasets/hasanccr92/cicmaldroid-2020](https://www.kaggle.com/datasets/hasanccr92/cicmaldroid-2020)) (accessed on 29 May 2025) \\ CICMalMem2022 & [45] & [[https://www.kaggle.com/datasets/lucagodoy/obfuscated-malware-memo-ry-2022-cic](https://www.kaggle.com/datasets/lucagodoy/obfuscated-malware-memo-ry-2022-cic)]([https://www.kaggle.com/datasets/lucagodoy/obfuscated-malware-memo-ry-2022-cic](https://www.kaggle.com/datasets/lucagodoy/obfuscated-malware-memo-ry-2022-cic)) (accessed on 29 May 2025) \\ Customer & [141] & [[https://www.consumerfinance.gov/data-research/consumer-complaints/](https://www.consumerfinance.gov/data-research/consumer-complaints/)]([https://www.consumerfinance.gov/data-research/consumer-complaints/](https://www.consumerfinance.gov/data-research/consumer-complaints/)) (accessed on 29 May 2025) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: _Cont._ \begin{table}
\begin{tabular}{l l l} \hline
**Dimension** & **Value** & **Papers** \\ \hline Drift Type & Sudden Concept Drift & [101,114] \\ \cline{2-3}  & Incremental Concept Drift & [101] \\ \cline{2-3}  & Gradual Concept Drift & [101,114], [120] \({}^{\star}\) \\ \cline{2-3}  & Recurrent Concept Drift & [101] \\ \cline{2-3}  & Concept Drift (General) & [125], [123] \({}^{\star}\) \\ \cline{2-3}  & Data drift & [49,58], [32] \({}^{\star}\) \\ \hline Application Context & IoT & [101] \\ \cline{2-3}  & Marine Science & [114] \\ \cline{2-3}  & Energy & [49] \\ \cline{2-3}  & Finance & [58] \\ \cline{2-3}  & Anomaly Detection & [125] \\ \cline{2-3}  & Process Mining & [123] \({}^{\star}\) \\ \cline{2-3}  & Video Surveillance & [32] \({}^{\star}\) \\ \cline{2-3}  & Industrial Processes & [120] \({}^{\star}\) \\ \hline Scope & Global & [49,101,114], [32] \({}^{\star}\), [120] \({}^{\star}\) \\ \cline{2-3}  & Local & [58,125], [123] \({}^{\star}\) \\ \hline Audience & Data Scientists & [49,58,101,114,125], [123] \({}^{\star}\), [32] \({}^{\star}\), [120] \({}^{\star}\) \\ \hline Metrics & — & — \\ \hline Learning Mode & Online & [101,125], [123] \({}^{\star}\), [32] \({}^{\star}\), [120] \({}^{\star}\) \\ \cline{2-3}  & Hybrid & [49,114] \\ \cline{2-3}  & Offline & [58] \\ \hline \end{tabular}
\end{table} 
Table 11: Classification of Causal and Temporal Dependency literature based on key dimensions. \begin{table}
\begin{tabular}{l l l} \hline
**Dimension** & **Value** & **Papers** \\ \hline \multirow{2}{*}{Scope} & Global & [40,48,116], [94] \({}^{\star}\), [37] \({}^{\star}\) \\ \cline{2-3}  & Local & [48,104,116,119,123], \({}^{\star}\), [122] \({}^{\star}\), [59] \({}^{\star}\), [133] \({}^{\star}\) \\ \hline Audience & Data Scientists & [40,48,104,116,119,123], \({}^{\star}\), [122] \({}^{\star}\), [59] \({}^{\star}\), [94] \({}^{\star}\), [37] \({}^{\star}\), [133] \({}^{\star}\) \\ \hline Metrics & – & – \\ \hline Learning Mode & Offline & [40], [94] \({}^{\star}\) \\ \cline{2-3}  & Hybrid & [116], [122] \({}^{\star}\), [59] \({}^{\star}\) \\ \cline{2-3}  & Online & [48,104,119,123], \({}^{\star}\), [133] \({}^{\star}\) \\ \hline \end{tabular}
\end{table} 
Table 12: Classification of Bayesian and Uncertainty Modeling literature based on key dimensions.