# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 2 Background
### Predictive Process Monitoring
#### 2.1.1 PPM Workflow
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline
**Case, Id** & **Application Id** & **Act.Craw.\_appr** & **Act.\_visid.\_appr** & **Act.\_decide.\_appr** & **Act.\_disne.\_appr** & **Res.\_john** & **Res.\_john** & **Res.\_john** & **Res.\_john** & **Sem.\_monthly\_out** & **Regmented\_amount** & **Cometed\_amount** \\ \hline A150 & C1803 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 1281 & 38,000 & 30,000 \\ A150 & C1803 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 1512 & 38,000 & 30,000 \\ A150 & C1803 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1854 & 38,000 & 30,000 \\ A150 & C1603 & 1 & 1 & 1 & 1 & 1 & 2 & 1 & 1 & 3067 & 38,000 & 30,000 \\ \hline \end{tabular}
\end{table} 
Table 2: Feature vector created for case with id = A150 from the log in Table 1 using _aggregated_ encoding. Note that the difference between the two examples is represented by the differences in how dynamic attributes are encoded. All encoding techniques applied to dynamic attributes are accompanied by _static encoding_ used for encoding static attributes. * **Predictive model construction and operation. ** Depending on the PPM task, the respective predictive model is chosen. In this context, the prediction task type is not the only factor guiding the process of selecting the predictive model. Other relevant factors include the scalability of the predictive model when facing larger event logs, its simplicity, and the interpretability of the results. According to [2], Decision Trees (DT) are most often selected in current PPM research thanks to their simplicity. XGBoost represents another type of high-performing predictive model used in the context of PPM tasks [2]. The assignment of parameter values follows the model selection step. The values of model parameters are learned by the model during the training phase, whereas the values of hyperparameters are set prior to training the model. In the next step, the predictive model is trained based on encoded prefixes that represent completed process instances. Note that for each bucket, a dedicated predictive model needs to be trained, i.e., the number of predictive models depends on the chosen bucketing technique. After generating predictions for the training event log, the performance of a predictive model needs to be evaluated. Generally, the choice of the evaluation technique depends on the prediction task, e.g., classification tasks have different evaluation metrics than regression tasks. * **PPM online stage. ** This stage starts with an incomplete process instance, i.e., a running process instance. Buckets formed in the offline stage are recalled to determine the suitable bucket for the running process instance. Finding the relevant bucket is based on the similarity between the running process instance and the prefixes in a bucket according to the criteria defined by the bucketing method. For example, in the case of state bucketing, a running process instance is assigned to a bucket in which all process instances have the same state as the running process instance. Afterwards, the running process instance is encoded according to the encoding method chosen for the PPM task. The encoded form of the running process instance then qualifies as an input to the most relevant predictive model from those created in the offline stage. Finally, this stage is completed by the predictive model, which generates a prediction for the running process instance according to the pre-specified goal of the PPM task.