An In-Context Foundation Model for Predictive Process Monitoring on Event Logs

IEEE Access, vol. 14, pp. 16959–16983

Abstract

Event logs record the execution of business processes as sequences of timestamped events. Most predictive process monitoring methods still learn a separate model per log: when the process, the activity vocabulary, or the time scale changes, the model must be retrained and revalidated. This paper takes a different route and argues for a foundation model for process mining: a single reusable model trained across heterogeneous event logs, designed to generalize to new logs and to be adapted with only a small set of in-log examples. To the best of our knowledge, we present the first event-log-native foundation model specifically tailored to process mining. The model consumes prefixes of cases directly (as ordered event sequences with timestamps and attributes), produces a compact representation of the running case, and supports two core monitoring tasks: next-activity prediction and remaining-time estimation, without any per-log parameter updates. At use time, it adapts in context from a support set sampled from the target log, which aligns the model to the local activity set and temporal scale; string attributes can optionally provide additional semantic hints. We study both balanced few-shot contexts and retrieval-based contexts built by nearest-neighbor selection with same-case masking, and we report extensive ablations over context size and design choices. Results on held-out logs and a real-life case study show that a single pretrained model can transfer to unseen event logs and improve with a handful of contextual examples. Overall, this work is a first step toward general-purpose, reusable foundation models for process mining that can be trained once and applied broadly across organizations and processes.

Authors 2

1. Alessandro Berti Aachen Process and Data Science (PADS)

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany

2. Wil M. P. van der Aalst Aachen Process and Data Science (PADS)

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany