Deep learning models are now being increasingly used for predictive process mining tasksin business processes. Modern approaches have been successful in achieving better performance for different predictive tasks , as compared to traditional approaches. In this work, five different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next activity in a given process instance. We have introduced a new method for representing feature vectors for any individual event in a given process instance, taking into consideration the structure of Directly-follows process graphs generated from the corresponding datasets. The adjacency matrix of the process graphs generated has been used as input to a Graph Convolutional Network (GCN). Different model variants make use of variations in the representation of the adjacency matrix. The performance of all the model variants have been tested at different stages of a process, determined by quartiles estimated based on the number of events and  the case duration. The results obtained from the experiments, significantly improves over the previously reported results for most of the individual tasks . Interestingly, it was observed that a linear Multi-Layer Perceptron (MLP) with dropout was able to outperform the GCN variants in both the  prediction tasks. Using a quartile-based analysis, it was further observed that the other variants were able to perform better than MLP at individual quartiles in some of the tasks where the MLP had the best overall performance .
Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved better performance for different predictive tasks than traditional approaches. We extend the existing body of research by testing four different variants of Graph Neural Networks (GNN) and a fully connected Multi-layer Perceptron (MLP) with dropout for the tasks of predicting the nature and timestamp of the next process activity. In contrast to existing studies, we evaluate our models' performance at different stages of a process, determined by quartiles of the number of events and normalized quarters of the case duration. This provides new insights into the performance of a prediction model, as they behave differently at different stages of a business-process . Interestingly, our experiments show that the simple MLP often outperforms more sophisticated deep-learning models in both prediction tasks. We argue that care needs to be taken when applying automated process-prediction techniques at different stages of a process. We further argue that researchers should reflect their results with strong baselines methods like simple MLPs .
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Deep learning models are now being increasingly used for predictive process mining tasksin business processes. Modern approaches have been successful in achieving
Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved
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, as compared to
than
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In this work, five
We extend the existing body of research by testing four
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a model involving a Graph Convolutional Layer and linear layers have been tested for the task
Graph Neural Networks (GNN) and a fully connected Multi-layer Perceptron (MLP) with dropout for the tasks
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activity in a given process instance. We have introduced a new method for representing feature vectors for any individual event in a given process instance, taking into consideration the structure of Directly-follows process graphs generated from the corresponding datasets. The adjacency matrix of the process graphs generated has been used as input to a Graph Convolutional Network (GCN). Different model variants make use of variations in the representation of the adjacency matrix. The performance of all the model variants have been tested
process activity. In contrast to existing studies, we evaluate our models' performance
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estimated based on
of
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normalized quarters of
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The results obtained from the experiments, significantly improves over the previously reported results for most of the individual tasks
This provides new insights into the performance of a prediction model, as they behave differently at different stages of a business-process
R
it was observed that a linear Multi-Layer Perceptron (MLP) with dropout was able to outperform the GCN variants in both
our experiments show that
A
simple MLP often outperforms more sophisticated deep-learning models in both
R
Using a quartile-based analysis, it was further observed that the other variants were able to perform better than MLP at individual quartiles in some of the tasks where the MLP had the best overall performance
We argue that care needs to be taken when applying automated process-prediction techniques at different stages of a process. We further argue that researchers should reflect their results with strong baselines methods like simple MLPs