Predictive Process Mining

• Predictive Process Mining is an analytical discipline that uses historical event data to forecast future process behavior, outcomes, and key performance indicators.
• It integrates statistical analysis, machine learning, and process-aware modeling to predict next events, remaining time, and compliance statuses with rigorous benchmarking.
• PPM supports proactive operational decisions by enabling adaptive monitoring, explainability, and responsiveness to evolving process dynamics.

Predictive Process Mining (PPM) is an advanced analytical discipline within process mining that leverages historical event log data to anticipate the future evolution, behavior, or performance of ongoing business process instances. PPM unifies statistical, machine learning, and process-aware modeling frameworks to forecast control-flow activities, temporal KPIs, outcomes, and compliance states during process execution. As a result, PPM facilitates proactive operational support, dynamic decision-making, and prescriptive interventions in both intra- and inter-organizational contexts.

1. Formal Foundations and Taxonomy of Predictive Tasks

PPM models map partial execution traces (prefixes) of business processes to predicted future properties via formally defined functions:

• Next-Event Prediction: Given an observed prefix $\sigma_k = \langle e_1,\ldots,e_k \rangle$, predict the label of the next activity $a_{k+1} = f_\text{evt}(\sigma_k)$ and optionally $t_{k+1} = f_\text{time}(\sigma_k)$ (Fioretto et al., 7 Jan 2025).
• Remaining-Time Estimation: For case prefix $\sigma_k$, estimate time-to-completion $$\tau(\sigma_k) = T_\text{end} - t_k = f_\text{rem}(\sigma_k)$$.
• Outcome Prediction: Classification of a running case as compliant/non-compliant, accepted/rejected, etc., $y = f_\text{out}(\sigma_k)$, $y \in \{0, 1, ..., C\}$.
• KPI/Performance Indicator Forecasting: Regression of process-level quantities such as throughput time, costs, utilization, $KPI: \sigma_k \rightarrow \mathbb{R}$ (Leribaux et al., 13 Oct 2025).
• Suffix/Sequence Prediction: Estimation of the most likely remaining activities $Seq: \sigma_k \rightarrow \sigma_{k+1...n}$ (Fioretto et al., 7 Jan 2025, Stritzel et al., 18 Dec 2025).
• Resource Assignment: Forecasting the next actor or resource involved, $Res: \sigma_k \rightarrow \mathcal{R}$.

These tasks are increasingly formulated as multi-output architectures (e.g., simultaneous next-activity and timestamp prediction in ProcessTransformer) and can be adapted for collaborative, object-centric, or compliance-critical business scenarios (Calegari et al., 13 Sep 2024, Rinderle-Ma et al., 2022).

2. Data Representation, Encoding, and Sampling Methodologies

PPM pipelines commence with meticulous event log preprocessing and trace encoding:

• Classical event logs: Each event tuple consists of $$(\text{caseID}, \text{activity}, \text{timestamp}, \text{attributes})$$; object-centric logs (OCEL) link events to multiple objects creating relational graphs (Fioretto et al., 7 Jan 2025).
• Encoding techniques: One-hot, count vectors, n-grams, word2vec/GloVe embeddings, graph walks (node2vec, DeepWalk), conformance-based (token-replay, alignment), and log-skeleton encodings. Higher-order and graph-based encodings (GraphWave, BoostNE) generally yield superior label correlation and expressivity; naive one-hot encodings show distinctly inferior F1 (Jr. et al., 2023).
• Sampling procedures: Variant-preserving instance selection (division, logarithmic, unique sampling per control-flow variant) sharply reduces training time while preserving predictive performance. For instance, division sampling ($k=2..10$) maintains $R_\text{Acc} \approx 1.00$ at up to $9\times$ speedup; over-pruning via unique selection risks blindness to rare behaviors (Sani et al., 2023, Sani et al., 2022).

Benchmark dataset construction mandates leakage-free splitting and temporal de-biasing. Strict protocols ensure train/test separation by case IDs and debias both start/end distributions (Weytjens et al., 2021).

3. Predictive Modeling Architectures and Learning Paradigms

PPM models span a spectrum of algorithmic methodologies, from classical machine learning to deep sequence and graph learning:

• Classical (DT, RF, SVM, boosting): Suited to tabular, static features; gradient boosting (CatBoost, XGBoost) matches or outperforms graph methods when abundant features exist (Fioretto et al., 7 Jan 2025).
• Sequence models (LSTM, GRU, CNN, Transformer): Activity and temporal context encoded as one-hot or embedding sequences. LSTM/GRU excel for event prediction and time regression; Transformer-based self-attention solutions (ProcessTransformer) achieve state-of-the-art (SOTA) results but with increased training time (Stritzel et al., 18 Dec 2025, Ansari et al., 21 Sep 2025).
• Graph-based (GNN, DGCNN): Essential for object-centric logs, capturing multi-object synchronization via message-passing and convolutional architectures (Fioretto et al., 7 Jan 2025).
• Hybrid and self-supervised paradigms: Data augmentation (SiamSA-PPM) and self-supervised Siamese networks leverage statistically-informed transformations and unlabeled traces to bolster representation learning and SOTA next-activity/outcome prediction accuracy (Straten et al., 24 Jul 2025).
• Transfer learning approaches: Pretrained model transfer (LSTM, embedding models) enables outcome prediction even under severe data scarcity, outperforming traditional methods (AUC improvements up to ~2–3%) for cross-organizational adaptation (Weinzierl et al., 11 Aug 2025).
• Parameter-efficient fine-tuning of LLMs: LoRA adapters and partial unfreezing democratize LLM deployment for PPM, matching LSTM/Transformer accuracy in multi-task settings with reduced computation and tuning requirements (Oyamada et al., 3 Sep 2025).
• Model simplification studies: Reduction of layer count, embedding dimensions, and attention heads in architecture (e.g. $>$85% parameter shrinkage) leads to only marginal (2–3%) precision loss for both Transformer and LSTM models (Ansari et al., 21 Sep 2025).

4. Explainability, Trust, and Stakeholder Integration

PPM systems rely on Explainable AI (XAI) to foster stakeholder trust and regulatory acceptance:

• Model-specific explanations: Coefficient inspection (LR), built-in feature importance (tree ensembles).
• Model-agnostic explanations: SHAP (Shapley values), LIME, Permutation Feature Importance, Accumulated Local Effects. SHAP provides deterministic, interaction-aware attributions and is the most reliable for both tree and linear models (Elkhawaga et al., 2022, Elkhawaga et al., 2022).
• Local post-hoc explanations: Latent-space clustering with surrogate trees yields stable, interpretable rules, enhancing user trust in black-box predictions (average AUROC 0.94, local surrogate fit $R^2 \approx 0.90$) (Mehdiyev et al., 2020).
• Frameworks for explanation stability: Systemic checks of explanation quality under different encodings and bucketing reveal that data sparsity, collinearity, and class imbalance can undermine both model learning and explanation reliability (Elkhawaga et al., 2022, Elkhawaga et al., 2022).
• Prescriptive compliance monitoring: PPM outputs are mapped to compliance predicates, allowing for early risk detection, mitigation action suggestion, and transparent “root-cause” analysis in compliance-critical contexts (Rinderle-Ma et al., 2022).

5. Evaluation, Reproducibility, and Benchmarking Protocols

Rigorous benchmarking is essential for reproducible and fair advancement in PPM:

• Metrics: Next-activity and outcome prediction (accuracy, precision, recall, F1, AUC); timestamp and remaining time (MAE, RMSE, MAPE); sequence/suffix prediction (Damerau-Levenshtein similarity, BLEU/jaccard indices); stability and reliability for model explanations.
• SPICE library: Re-implements canonical neural architectures (LSTM, ProcessTransformer) with robust configuration, leakage-free splitting, and strict random seed controls; empirically, re-implementation either matches or improves previously reported metrics, particularly due to debiased splits and preprocessing (Stritzel et al., 18 Dec 2025).
• Bias quantification: Case duration and running-case metrics inform the representativeness of splits; Jensen–Shannon divergence and running-case deviation capture start/end bias (Weytjens et al., 2021).
• Best practices: Publish train/test splits, configuration files, and all code; use only train-set statistics for preprocessing; report per-class, balanced metrics (Stritzel et al., 18 Dec 2025, Weytjens et al., 2021).

6. Handling Concept Drift and Online Adaptation

PPM must continuously adapt to evolving process semantics and data distributions:

• Drift detection and retraining: Page-Hinkley and ADWIN detectors trigger retraining on recent batches; the “last” (sliding window) batch (typically $B=500$ cases) delivers most effective adaptation, raising accuracy from 0.54 to $\approx0.70$ (Baier et al., 2020).
• Incremental learning: Combining single-instance updates with batch retraining enhances performance by additional 1.6 pp.
• Strategy selection: Small batch sizes and retraining on most recent labeled data speed recovery and preserve prediction accuracy during abrupt or gradual drift (Baier et al., 2020).

7. Extension to Collaborative, Object-Centric, and Performance-Driven Scenarios

Recent frameworks generalize PPM to new process domains:

• Collaborative process monitoring: By merging participant logs and extending event attributes, standard sequence models (Transformer) predict not only next activities but also next participant or inter-organizational message in real-world healthcare and e-government scenarios (Calegari et al., 13 Sep 2024).
• Object-centric event logs: Graph-based encoding and GNNs tackle synchronization and concurrency among multiple interacting objects, outperforming flattened encodings in accuracy and expressivity (Fioretto et al., 7 Jan 2025).
• Actor-enriched KPI forecasting: Time-aligned actor signals (e.g., involvement, handover, interruption frequencies/durations) augment TT regression models, delivering consistent RMSE and $R^2$ gains across all datasets; tree-based approaches and LSTM/attention hybrids integrate these signals for more robust process performance prediction (Leribaux et al., 13 Oct 2025).
• Declarative constraint prediction: PAM with ConvLSTM architectures predicts the presence of LTL/Declare constraints over sliding windows (“processes as movies”), outperforming next-event baselines ($\text{AP}\geq0.96$, $F_1\geq0.84$ for binary constraints) and enabling strategic model forecasting (Smedt et al., 2020).

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Predictive Process Mining, as an integrated field, now encompasses advanced encoding, scalable learning architectures, explainability at both global and local scales, robust benchmarking, adaptive model maintenance, and support for collaborative, graph-structured, and compliance-critical business environments. Current research emphasizes efficiency, transparency, extension to object-centric and actor-driven process signals, and rigorous adaptation protocols to preserve predictive performance under evolving process realities.