5 papers
DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining
Johannes De Smedt, Jari Peeperkorn, Artem Polyvyanyy +1
Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring. However, standard objectives such as cross-entropy optimi…
Actor-Enriched Time Series Forecasting of Process Performance
Aurelie Leribaux, Rafael Oyamada, Johannes De Smedt +3
Predictive Process Monitoring (PPM) is a key task in Process Mining that aims to predict future behavior, outcomes, or performance indicators. Accurate prediction of the latter is…
Linking Actor Behavior to Process Performance Over Time
Aurélie Leribaux, Rafael Oyamada, Johannes De Smedt +3
Understanding how actor behavior influences process outcomes is a critical aspect of process mining. Traditional approaches often use aggregate and static process data, overlooking…
PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks
Wenjun Zhou, Artem Polyvyanyy, James Bailey
Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business p…
Stochastic Directly-Follows Process Discovery Using Grammatical Inference
Hanan Alkhammash, Artem Polyvyanyy, Alistair Moffat
Starting with a collection of traces generated by process executions, process discovery is the task of constructing a simple model that describes the process, where simplicity is o…