Predictive Process Monitoring
PulseAugur coverage of Predictive Process Monitoring — every cluster mentioning Predictive Process Monitoring across labs, papers, and developer communities, ranked by signal.
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New GNN4PPM approach enhances predictive process monitoring with R-GCNs
Researchers have developed GNN4PPM, a novel approach for Predictive Process Monitoring (PPM) that utilizes Relational Graph Convolutional Networks (R-GCNs). This method represents event log data as a heterogeneous knowl…
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Multi-task learning shows promise for predictive process monitoring
A new study explores the potential of multi-task learning (MTL) for Predictive Process Monitoring (PPM), a field that forecasts the unfolding of organizational processes. While deep learning has advanced PPM, most exist…
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New framework enables continual fine-tuning of foundation models for predictive process monitoring
Researchers have introduced COMPASS, a novel framework designed for the online continual fine-tuning of foundation models (FMs) specifically for Predictive Process Monitoring (PPM). This approach addresses the cold-star…
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New method enhances deep learning explainability for process monitoring
Researchers have developed a new method to improve the explainability of deep learning models used in predictive process monitoring. The proposed technique addresses the computational complexity and loss of detail assoc…
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New GGATN model generates complete event sequences for predictive process monitoring
Researchers have developed a new neural network architecture called the Graph Grounded Cross Attention Transformer Neural Network (GGATN) for predictive process monitoring. This model aims to generate complete event seq…
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Simple argmax baseline matches LLMs in Next Activity Prediction benchmark
A new paper on arXiv benchmarks the performance of various models for Next Activity Prediction (NAP), a key component of predictive process monitoring. The study compares large language models (LLMs), Transformers, LSTM…