Predictive Process Monitoring
22 papers with code • 0 benchmarks • 1 datasets
A branch of predictive analysis that attempts to predict some future state of a business process.
Benchmarks
These leaderboards are used to track progress in Predictive Process Monitoring
Ultra-processed Food Dataset
Latest papers
HOEG: A New Approach for Object-Centric Predictive Process Monitoring
To leverage this enriched data, we propose the Heterogeneous Object Event Graph encoding (HOEG), which integrates events and objects into a graph structure with diverse node types.
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime.
Structural Positional Encoding for knowledge integration in transformer-based medical process monitoring
christopher-irw/proformer_ce
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Predictive process monitoring is a process mining task aimed at forecasting information about a running process trace, such as the most correct next activity to be executed.
Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction
Predictive Process Monitoring (PPM) aims at leveraging historic process execution data to predict how ongoing executions will continue up to their completion.
Measuring the Stability of Process Outcome Predictions in Online Settings
This paper proposes an evaluation framework for assessing the stability of models for online predictive process monitoring.
Trace Encoding in Process Mining: a survey and benchmarking
Encoding methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, trace clustering, etc.
Can recurrent neural networks learn process model structure?
In this work, we investigate the capabilities of such an LSTM to actually learn the underlying process model structure of an event log.
Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring
The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use.
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models
In this paper, we define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction.
