Predicting Process Outcomes with Limited Data: A New Approach

Author: Denis Avetisyan

Researchers demonstrate that advanced AI models can accurately forecast process behavior even when data is scarce, offering a viable alternative to traditional methods.

This review explores how Large Language Models leverage embedded knowledge and complex reasoning for predictive process monitoring in small-scale event logs.

Predictive process monitoring often struggles with limited data availability, hindering accurate outcome forecasting. This paper, ‘Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs’, investigates the capacity of Large Language Models (LLMs) to overcome this challenge. Empirical results demonstrate that LLMs not only surpass benchmark methods in data-scarce settings-even with just 100 traces-but also leverage both prior knowledge and internal trace correlations for enhanced prediction across multiple Key Performance Indicators. Could this approach unlock a new era of proactive process management, even where historical data is minimal?

The Illusion of Control: From Reaction to Anticipation

Historically, process mining techniques largely concentrated on reconstructing and visualizing events after they occurred – a fundamentally descriptive approach. While valuable for understanding what happened, this retrospective analysis often leaves organizations positioned to react to problems only when they’ve already impacted operations. This reliance on past data creates a cycle of responding to incidents, rather than anticipating and preventing them. Consequently, businesses find themselves constantly playing catch-up, addressing bottlenecks, inefficiencies, and deviations as they emerge, instead of proactively shaping process performance and mitigating potential risks before they escalate into significant issues. The limitations of solely examining historical data highlight the need for a forward-looking methodology capable of predicting future process behavior.

Predictive Process Monitoring (PPM) represents a fundamental shift from simply understanding what happened in a business process to anticipating what will happen. Rather than reacting to deviations or bottlenecks after they occur, PPM leverages data-driven models to forecast potential issues – such as delays, failures, or compliance violations – before they impact operations. This proactive capability enables organizations to implement timely interventions, ranging from automated adjustments to resource allocation or process parameters, to mitigate risks and optimize performance. By forecasting future outcomes, PPM doesn’t just illuminate problems; it empowers preemptive action, fostering resilience and driving continuous improvement within complex operational systems.

The true power of Predictive Process Monitoring lies not just in forecasting future events, but in the sophistication of the prediction models themselves. Real-world processes are rarely static; they evolve with changing conditions, exhibit intricate dependencies, and often generate vast amounts of noisy data. Consequently, robust PPM demands models capable of adapting to these complexities – techniques like recurrent neural networks and long short-term memory networks are increasingly employed to capture temporal dependencies, while ensemble methods mitigate the risk of relying on a single, potentially flawed, prediction. Furthermore, these models must be regularly retrained and validated with incoming data to maintain accuracy and prevent ‘model drift’, ensuring that proactive insights remain reliable and actionable even as the underlying processes continue to change. The ability to effectively handle this dynamism is the defining characteristic of successful Predictive Process Monitoring implementations.

The Echo in the Machine: LLMs and the Mimicry of Process

Large Language Models (LLMs) excel at processing sequential data due to their underlying transformer architecture, which utilizes attention mechanisms to weigh the importance of different elements within a sequence. Event logs, representing sequences of activities within a process, are therefore a natural fit for LLM analysis. Specifically, LLMs can ingest event log data, where each event is a step in a process, and learn the patterns and dependencies between these events. This capability stems from the model’s ability to represent and understand the temporal relationships inherent in sequential data, allowing it to predict future events based on observed patterns without requiring explicit feature engineering traditionally used in process mining.

Successful implementation of Large Language Models (LLMs) for process prediction necessitates meticulous prompt engineering. LLMs do not inherently understand process mining terminology or the desired prediction format; therefore, prompts must explicitly define the task, specify the input data structure (e.g., event log trace representation), and instruct the model on the desired output format (e.g., next event prediction, remaining trace completion). The precision of these instructions directly correlates with the accuracy of the LLM’s predictions; ambiguous or poorly structured prompts will yield unreliable results. Techniques such as few-shot learning, where example input-output pairs are included in the prompt, and the use of chain-of-thought prompting to encourage reasoning, are critical for eliciting optimal performance from LLMs in a process prediction context.

Evaluations demonstrate that Large Language Models (LLMs) achieve notable predictive performance in process mining tasks with remarkably limited training data; specifically, accuracy is maintained even when trained on only 100 process traces. This capability stems from the embodied knowledge present within pre-trained LLMs, representing a broad understanding of language and sequential patterns acquired during general training. Consequently, LLMs require significantly less task-specific data compared to traditional machine learning methods to generalize effectively to process prediction, highlighting the transfer learning benefits inherent in leveraging these models for process mining applications.

Distilling the Ghost: Formalizing Reasoning with β-Learners

β-Learners are formalized representations of reasoning strategies identified within the explanations produced by Large Language Models (LLMs). These patterns are derived through abstraction, distilling the core logic LLMs utilize when arriving at predictions. Rather than relying on the full complexity of the LLM, β-Learners offer a condensed framework for understanding and potentially replicating the reasoning process. This abstraction allows for focused analysis of specific reasoning steps and facilitates the creation of a more interpretable and manageable predictive model, independent of the original LLM architecture.

β-Learners represent abstracted reasoning patterns derived from the explanation traces of Large Language Models (LLMs). These patterns function as a distilled representation of the strategies LLMs utilize during prediction tasks, moving beyond the complexity of the full model to focus on core reasoning steps. By extracting these patterns, a more concise and interpretable predictive framework is achieved; rather than relying on the entirety of the LLM’s parameters, predictions can be attributed to the presence and application of specific β-Learners. This abstraction facilitates analysis of how a prediction was made, increasing transparency and enabling focused refinement of reasoning capabilities.

Good-Turing Smoothing is implemented to improve the performance of β-Learners when encountering infrequent patterns in process data. This technique mitigates the impact of rare events, which would otherwise result in zero probability assignments and hinder accurate prediction. Analysis indicates that, given a single new data trace, the probability of discovering a completely novel β-Learner is 0%; the smoothing allows the model to generalize from existing patterns rather than requiring observation of every possible sequence to assign a non-zero probability.

Testing the Echo: KNN Models Augmented by β-Learners

The integration of β-Learners into k-Nearest Neighbors (KNN) models was performed using two distinct feature sets: activity-based (knn-act) and attribute-based (knn-att). The knn-act approach utilizes event logs focusing on sequences of activities to determine nearest neighbors, while knn-att leverages the attributes associated with each event, such as resource or cost, for neighbor identification. This dual implementation allows for comparative analysis of the β-Learner’s effectiveness in different feature spaces within the KNN framework, providing a more comprehensive assessment of its contribution to predictive performance. Both knn-act and knn-att models benefited from the β-Learner’s knowledge integration capabilities, enhancing the accuracy of KNN predictions.

Model validation employed a diverse set of datasets to ensure generalizability. The Bpi12 Dataset, a publicly available event log, was utilized alongside the Bac Dataset, another established benchmark in process mining. In addition to these public datasets, a private Hospital Dataset, containing real-world clinical process data, was included to evaluate performance in a practical, non-synthetic environment. This multi-dataset approach allowed for a robust assessment of the β-Learner enhanced KNN models across varying data characteristics and process complexities.

Evaluations demonstrate that k-Nearest Neighbors (KNN) models incorporating β-Learners achieve competitive performance in predicting process Key Performance Indicators (KPIs), specifically total time and activity occurrence. On the Bpi12 dataset, utilizing a limited sample of 100 traces, the models produced a Mean Absolute Error (MAE) of 6508. This result surpasses the performance of CatBoost, which yielded an MAE of 9394, and approaches the performance of PGTNet at 8856. Furthermore, an F1-Score of 0.77 was attained, indicating performance comparable to benchmark models, and a Nemenyi test confirmed statistical significance with a p-value of less than 0.01, suggesting the models effectively leverage semantic knowledge for prediction.

The Illusion of Control, Revisited: Towards Adaptive and Explainable Process Intelligence

Current Process Performance Monitoring (PPM) systems often struggle with the inherent dynamism of real-world processes, requiring frequent manual recalibration to maintain accuracy. This research establishes a foundation for a new generation of adaptive PPM systems capable of autonomously learning and refining predictive models as process behaviors evolve. By continuously analyzing incoming process data and adjusting internal parameters, these systems promise to overcome the limitations of static models and deliver consistently reliable performance, even in the face of changing conditions. This capability is crucial for industries where process shifts are frequent, such as manufacturing, logistics, and healthcare, and ultimately enables proactive identification of performance bottlenecks and opportunities for optimization without constant human intervention.

The utility of process intelligence extends beyond mere prediction; a crucial next step involves translating complex algorithmic outputs into readily understandable insights for those directly involved in process execution. β-Learners, by design, offer an inherent degree of explainability, allowing stakeholders to not simply see a predicted outcome, but to understand why that prediction was made. This transparency fosters trust in the system, moving beyond a “black box” approach and enabling informed interventions; for example, identifying specific process variables driving a predicted delay or pinpointing the root cause of a quality defect. Such actionable intelligence empowers process owners and operators to proactively address issues, optimize performance, and ultimately, make data-driven decisions with confidence.

Future investigations are directed toward broadening the applicability of this process intelligence framework to encompass increasingly intricate process landscapes, moving beyond simplified models to accommodate the nuances of real-world operations. This expansion includes tackling challenges posed by high-dimensionality, non-linearity, and the presence of multiple interacting processes. Crucially, research will explore seamless integration with automated process control systems, enabling a closed-loop system where predictive insights directly inform and optimize process execution. The ultimate goal is to move beyond mere process monitoring and prediction toward autonomous process management, where the system can proactively adapt to changing conditions and ensure optimal performance without constant human intervention, potentially revolutionizing industries reliant on complex operational workflows.

The pursuit of predictive accuracy from limited event logs reveals a fundamental truth: systems rarely conform to neat, pre-defined structures. This research demonstrates that Large Language Models excel not by imposing order, but by discovering patterns within inherent chaos. As Marvin Minsky observed, “Common sense is the collection of things everyone knows, but no one can explain.” Similarly, LLMs don’t require exhaustive training data; they leverage pre-existing knowledge and reasoning to navigate ambiguity. Stability, in this context, isn’t a guaranteed outcome, but an illusion that caches well – a fleeting sense of predictability gleaned from a constantly evolving system. The model’s capacity to reason with sparse data underscores that chaos isn’t failure-it’s nature’s syntax.

What Lies Ahead?

This exploration of Large Language Models within the confines of limited event data reveals, predictably, not a solution, but a shifting of the problem. The apparent success in predicting process outcomes isn’t a triumph of architecture, but a temporary reprieve purchased with embedded knowledge. Every model, no matter how cleverly prompted, will eventually encounter a process it hasn’t ‘seen’ in some form. Scalability is merely the word applied to justify increasing complexity, and this work only demonstrates that even with limited data, that complexity accrues rapidly.

The temptation will be to chase ever-larger models, to encode more ‘world knowledge’. But everything optimized will someday lose flexibility. The real challenge isn’t prediction accuracy-it’s building systems that gracefully degrade, that signal their uncertainty rather than offer confident falsehoods. The field should focus less on mimicking human reasoning and more on understanding the inherent limitations of algorithmic foresight.

The perfect architecture is a myth to keep one sane. Perhaps the most fruitful avenue lies not in refining the models themselves, but in developing methods for continuous adaptation, for acknowledging and incorporating the inevitable drift between prediction and reality. The goal isn’t to foresee the future, but to build systems that can respond intelligently when the future inevitably deviates from the forecast.

Original article: https://arxiv.org/pdf/2601.11468.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/