# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
## 3 Proposed Method
### Network Architecture
#### 3.3.1 Multi-Layer Perceptron with a Data-Aware Entity-Embedding Layer
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer (Type)** & **Input Shape** & **Output Shape** & **Connected to** \\ \hline  & (1), & & dense\_1 \\  & (1), & & dense\_2 \\  & (1), & & dense\_3 \\  &... & &... \\ concatenate\_1 & (1) & (m + (n \(\times|D|\times\) & dense\_m \\ (Concatenate) & (\(|D|\times\) 50), & 50)), & reshape[1] \\  & (\(|D|\times\) 50), & reshape[2] \\  & (\(|D|\times\) 50), & reshape[3] \\  &... & &... \\  & (\(|D|\times\) 50) & & reshape[n] \\ dense\_NN\_1 & (m + (n \(\times|D|\times\) 50)) & (500) & concatenate\_1 \\ activation\_1 (Activation) & (500) & (500) & dense\_NN\_1 \\ dropout\_1 (Dropout) & (500) & (500) & activation\_1 \\ dense\_NN\_2 (Dense) & (500) & (250) & dropout\_1 \\ activation\_2 (Activation) & (250) & (250) & dense\_NN\_2 \\ dropout\_2 (Dropout) & (250) & (250) & activation\_2 \\ dense\_NN\_3 (Dense) & (250) & (100) & dropout\_2 \\ activation\_3 (Activation) & (100) & (100) & dense\_NN\_3 \\ dropout\_3 (Dropout) & (100) & (100) & activation\_3 \\ dense\_NN\_4 (Dense) & (100) & (50) & dropout\_3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Input and output shapes and connectivity of MLP with Data-Aware Entity-Embedding component. higher label number has a more significant influence than a lower label number. Therefore, we need an encoding method that assigns equal weight to categorical variables. Thus, some labels do not have a higher weight than other labels. This encoding is known as one-hot encoding. These result variables are also called dummy variables. One feature will be broken down into as many cardinals as the feature, with only one of them having a value of one, all of the others having a value of zero. The one-hot encoding technique has some disadvantages, such that if a feature has variables with a high cardinality level, the number of binary columns produced will be vast, and the dataset will become very sparse or have very high dimensions as a result. Not only that, but the values that are similar to each other also are not placed close together in dimensional space. The result is that the intrinsic properties of the feature are eliminated. One approach that can overcome these problems is a technique known as entity embedding [30]. By using the entity-embedding encoding technique, values that have similar intrinsic properties will be placed close together in space. Additionally, memory usage will be reduced since the dimensions of features are smaller than those of one-hot encoding. We propose a better encoding method that is suitable for event-log-type data. Instead of encoding each entity as one variable (entity embedding), we build up a fixed-size vector consisting of all different entities. We call this method Data-Aware Entity Embedding (DAEE). It is expected to decrease memory by \(n\) times, where \(n\) is the number of different types of entities. Based on our domain knowledge of the manufacturing process, a particular activity can experience differences in performance depending on the resources that run it. So, we can obtain a better encoding representation if we do not treat each entity as one variable but instead attach it to others (e.g., attach the resource to the activity). The expected outcome vectors of the embedding are shown in Figure 5. For example, in a typical embedding entity, Machine-1, Operator-ID0001, and Inspection-activity will each be represented as a single vector of its own. In DAEE, Inspection activity will be attached with any data associated with it before being represented as an embedding. For example, Inspection-activity performed by Operator-ID0001 on Machine-1 will be represented as a vector of its own. The process of attaching the data associated to certain activities is shown in Algorithm 1.