# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
## 4 Experimentation and Results
### Experimental Setup  
To demonstrate the reliability and robustness of our proposed method, we used another deep-learning method, proposed by [PERSON] et al. [15], as the baseline method. This method, called Data-Aware LSTM (DA-LSTM), utilizes the LSTM architecture. Unlike the previous research, which also utilizes LSTM, it considers the attribute data of events. With this method, to determine the number of layers and neurons, we also used trial and error (as we did in our proposed method). If the layers are more than two, the training process will be very long, but the performance will not improve. For that reason, we chose to use two layers. Unlike our model, which uses Adam as its optimizer, [PERSON] et al. [15] chose to use Nadam as the optimizer of their model. To demonstrate the effect without using a chart image in our model, we also compared it with a model without a CNN component (MLP + EE), which does not receive a chart image as its input. Additionally, to demonstrate the superiority of our method that uses DAEE, we compared it with a model that uses only ordinary embedding (Entity Embedding) to encode each discrete variable unit (CNN + EE). Note that all of the hyperparameters we used in our complete model are the same as this model's, except for the batch size, which has memory limitations due to the length of the input vector and image input. We summarize the four methods used in the experiment below:  
1. CNN + DAEE, our full proposed method, consists of a combination of two NN models, CNN and MLP with DAEE;
2. CNN + EE, the same model as CNN + DAEE, except that it uses entity embedding as its encoder;
3. MLP + EE, which is one part of our proposed method without the CNN model, to show the impact of not utilizing chart images;
4. DA-LSTM, the latest method based on deep learning for the prediction of the remaining time of an ongoing process problem. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Case ID** & **Activity** & **Resource** & **Timestamp** & \begin{tabular}{c} **Work** \\ **Order Qty** \\ \end{tabular} & **Part Des.** & **Worker ID** & \begin{tabular}{c} **Report** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Qty** \\ **Completed** \\ \end{tabular} & \
\begin{tabular}{c} **Qty** \\ **Rejected** \\ \end{tabular} &
\begin{tabular}{c} **Qty for** \\ **MRB** \\ \end{tabular} \\ \hline Case 1 & Turning \& & & Machine \(\leftarrow\)Turning \& 1/29/12 22.324 & 10 & Cable Head & ID@932 & S & 1 & 0 & 0 \\ Case 1 & Milling–Machine 4 & Milling & & & & & & & & & \\ Case 1 & Turning \& & Machine \(\leftarrow\)Turning \& 1/30/12 5.44 & 10 & Cable Head & ID@932 & D & 1 & 0 & 0 \\ Case 1 & Turning \& & Machine \(\leftarrow\)Turning \& 1/30/12 659 & 10 & Cable Head & ID@167 & S & 0 & 0 & 0 \\ Case 1 & Turning \& & Machine \(\leftarrow\)Turning \& 1/30/12 721 & 10 & Cable Head & ID@167 & D & 8 & 0 & 0 \\ Case 1 & Turning \& Milling \& 0.5 & Quality Check 1 & 1/31/12 13.20 & 10 & Cable Head & ID@163 & D & 9 & 1 & 0 \\ Case 1 & Laser Marking–Machine 7 & Machine \(\leftarrow\)laser Marking & 2/1/12 86.18 & 10 & Cable Head & ID@998 & D & 9 & 0 & 0 \\ Case 1 & Lapping–Machine 1 & Machine \(\leftarrow\)Lapping & 2/1/12 00 & 10 & Cable Head & ID@852 & D & 0 & 0 & 0 \\ Case 1 & Lapping–Machine 1 & Machine 1—Lapping & 2/14/12 9.00 & 10 & Cable Head & ID@882 & D & 1 & 0 & 0 \\ Case 1 & Lapping–Machine 1 & Machine 1—Lapping & 2/14/12 9.05 & 10 & Cable Head & ID@882 & D & 1 & 0 & 0 \\ Case 1 & Round Grinding & Machine 3—Round & 2/14/12 9.13 & 10 & Cable Head & ID@1445 & S & 0 & 0 & 0 \\  & –Machine 3 & Grinding & 2/14/12 13.37 & 10 & Cable Head & ID@1445 & D & 9 & 0 & 0 \\ Case 1 & Final Inspection QC. & Quality Check 1 & 2/16/12 6.59 & 10 & Cable Head & ID@1483 & D & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fragment of event log from manufacturing process. We adopted the k-fold cross-validation (CV) technique [37] to validate and evaluate the reliability of our model.