# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
## 5 Discussion and Conclusions  
In this study, we derived and evaluated a new approach, namely a parallel-structure deep-learning model named CNN + DAEE, for application in the PBPM field, particularly for predicting the remaining time of an ongoing process (case) using chart images. Our proposed method produced more precise accuracy relative to the baseline method (DA-LSTM), as indicated by the MAE metric and proven and demonstrated by empirical experiments using real-world datasets. We showed that the model's performance indeed proves to be better with the addition of another representation type of data. In terms of the performance duration of the training, our proposed method is far more practical; in fact, seven times better than the baseline method (DA-LSTM). In our method, we use only an MLP and SOP as input to capture sequence dependencies from data and handle categorical variables very well using embedding rather than the usual and proven model in the sequence problem, namely LSTM. One major consideration is the length of time required for training. MLP proved to be reasonably reliable compared with LSTM in this study, and its performance was improved by combining it with another model (CNN). By adding another representation type of data and using a combination of the two models, namely CNN + DAEE, the training duration was prolonged, but with respect to the use of only one model, MLP + EE; however, at the cost of longer training duration, prediction accuracy was increased. To date, deep-learning solutions used in PBPM research have focused only on the use of architectural RNN, especially LSTM. That approach is considered reasonable because the event log can be considered as a sequence of events. We proposed a different method, using another type of NN architecture, a parallel-structure deep-learning system using CNN and MLP, and it achieved better results (as indicated by our experimental data). Thus, we have added a variety of reasonable alternative solutions to the PBPM research field.  
In the utilization of chart image representation, there are still limitations. For example, we currently display the process only from the beginning to the event for which we want to predict the remaining time. In the future, it might be possible to apply the sliding window method so that the process resembles real chart monitoring. Another limitation is that in determining hyperparameters, such as the number of layers or the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **DA-LSTM** & **MLP + EE** & **CNN + EE** & **CNN + DAEE** \\ \hline MAE & 6104.678 & 5333.294 & 4773.060 & 3517.002 \\ Training duration & 861.400 & 83.600 & 155.400 & 123.000 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Mean training duration and MAE in units of minutes across five iterations of CV.  
Figure 12: MAE in units of minutes (**left**) and training duration in units of hours (**right**).  
number of neurons in the network, we use only the trial-and-error method, which is considered the most straightforward. We plan to improve our approach by applying a more sophisticated optimization method in future work, such as [PERSON] et al. [17]. Besides, our model can also potentially be extended to provide multi-objective predictions that predict not only the remaining time (as is the case now) but also other objectives such as minimization of deadline violations [13].  
Conceptualization, writing--original draft preparation, N.A.W.; supervision and review, H.B.; writing--review and editing, T.N.A., Y.A.I. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C110229411). This work was supported by the BK21 FOUR of the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 5199990914451). The authors declare no conflict of interest.