Predictive Process Monitoring (PPM)

A branch of process mining concerned with forecasting the future of running process instances from partial event-log traces.

Where traditional process mining is retrospective (discovery, conformance, performance on completed cases), PPM operates on running cases — at any point k within an in-progress trace, it answers questions such as:

Prediction targets

concepts/next-activity-prediction — which activity comes next?
concepts/remaining-time-prediction — how long until completion?
concepts/outcome-prediction — will this case end normally / violate an SLA / result in a positive outcome?
Full-suffix prediction — the entire remaining activity sequence.
Event attribute prediction — next resource, next data value.

Typical pipeline

Event log (XES / OCEL) as input.
Trace prefix extraction — for each completed case, generate all prefixes of length k ≥ 1 to use as training instances.
concepts/trace-encoding — convert variable-length prefixes into fixed-size feature vectors (or tensors), or — for LLM-based PPM — into human-readable strings (ρ_seq).
Model training — classical ML (random forest, XGBoost), RNN/LSTM, Transformer, GNN, or — increasingly — pre-trained LLMs invoked via in-context learning.
Online inference — apply the model to live running cases.

Design dimensions

Target — next activity / remaining time / outcome / activity occurrence.
Encoding — aggregate, index-based, one-hot, learned embeddings, LLM string (ρ_seq).
Data perspective — control-flow-only vs data-aware (attributes, resources, timestamps).
Inter-case vs intra-case — does the model consider contemporaneous cases?
Model family — classical ML, sequence models (RNN/LSTM/GRU/Transformer), graph models (GNN), pre-trained LLMs.
Training-data regime — full event log (typical for classical/deep methods) vs small-scale / 100-trace regime (Padella, de Leoni & Dumas 2026 show LLM-PPM excels at the latter).

Why it matters

Operational — drive interventions (alert, reroute, reallocate).
APM-relevant — feeds the "Recommend" enactment role of concepts/conversational-actionability; mentioned in sources/2026-calvanese-agentic-bpm-manifesto as a prototypical capability an APM agent invokes for time-to-completion reasoning.
Feeds PrPM — predictions only become actionable when combined with interventions; the runtime recommender built on top is prescriptive process monitoring (see Kubrak et al. 2022).

Landscape

The raw/Predictive process monitoring/ corpus spans a decade of PPM research — see syntheses/ppm-landscape for a cross-cutting synthesis and overview for domain positioning. For the authored research line of the wiki owner, see syntheses/riess-research-arc and entities/mike-riess.

Evaluation rigour

Beyond point accuracy, PPM models should be evaluated on earliness, temporal consistency (sources/2023-riess-temporal-loss-remaining-cycle-time), and their robustness across data-generating-process characteristics — the latter motivating controlled synthetic benchmarks via parametric simulation (SynBPS). Deployed models are subject to concept drift: Baier, Reimold & Kühl 2020 document an industrial P2P case where ignoring drift halves accuracy over a 70 k-transaction window, recoverable only by Page-Hinkley detection + incremental retraining on the most recent pre-alarm batch. Survey: Sato et al. 2021 (45-paper SLR; flags lack of common evaluation protocol). Adaptation via metaheuristics: sources/2022-riess-metaheuristics-concept-drift-survey.

For LLM-PPM, an additional concern is benchmark contamination — public event logs may be in LLM pre-training data. The semantic-hashing probe from Padella et al. 2026 is a concrete protocol to test for this.

Philosophy-of-science framing

A PPM prediction is a probabilistic causal claim and inherits the commitments of its chosen interpretation of concepts/probabilistic-causation. A tendency/disposition framing (sources/2023-anjum-rocca-phi403-lecture-18-risky-predictions) fits the case-level nature of PPM better than a frequentist reading; applying aggregate benchmark metrics directly to an individual running case is the ecological fallacy.

methods/process-mining-basics · concepts/self-modification · concepts/conversational-actionability · concepts/prescriptive-process-monitoring · concepts/intervention-policy · concepts/business-process-simulation · concepts/concept-drift · concepts/llm-based-ppm · concepts/beta-learner-distillation · concepts/semantic-hashing-probe · concepts/probabilistic-causation · concepts/rct-limitations