Beyond the Black Box: Standardizing Predictive Process Mining

Author: Denis Avetisyan

A new framework aims to address the critical lack of reproducibility in predictive process mining, offering a path toward more reliable and comparable model evaluations.

The SPICE workflow establishes a systematic process for analyzing data.

This paper introduces SPICE, a deep learning library designed to standardize data splitting, evaluation metrics, and benchmarking practices in predictive process mining.

Despite the increasing sophistication of predictive process mining (PPM) techniques, a lack of standardized implementation and evaluation hinders meaningful comparison and robust advancement of the field. This paper, ‘Towards Reproducibility in Predictive Process Mining: SPICE – A Deep Learning Library’, addresses this critical gap by introducing SPICE, a PyTorch-based framework for reimplementing and rigorously benchmarking popular deep learning approaches to PPM. Through SPICE, we reveal inconsistencies in previously reported results and propose improved data splitting and evaluation metrics for more reliable performance assessment. Will this standardized approach finally unlock the full potential of data-driven process intelligence and accelerate innovation in PPM?

The Predictive Imperative: Beyond Understanding to Anticipation

Predictive Process Mining (PPM) endeavors to move beyond simply understanding how a process unfolded to actively anticipating its future trajectory, a shift fraught with inherent difficulties. While conventional process mining reconstructs past events from event logs, PPM seeks to forecast upcoming activities, resource allocation, and potential bottlenecks. However, achieving reliable predictions proves challenging due to the dynamic and often unpredictable nature of real-world processes. Factors such as evolving business rules, external disruptions, and the sheer volume of data contribute to inaccuracies. Furthermore, the effectiveness of PPM models is heavily reliant on the quality and representativeness of the training data; insufficient or biased data can lead to models that fail to generalize to novel situations, undermining their practical utility and necessitating continuous refinement and validation.

Contemporary process systems, characterized by interconnectedness and dynamic change, are rapidly exceeding the capabilities of conventional predictive models. This escalating complexity necessitates a shift towards more nuanced approaches – incorporating techniques like machine learning and deep neural networks – to capture intricate patterns and anticipate future behavior. However, this pursuit of increased sophistication is shadowed by the growing “reproducibility crisis” within scientific research; inconsistent reporting, limited data availability, and algorithmic opacity threaten the reliability of predictive outcomes. Without rigorous validation, transparent methodologies, and standardized datasets, even the most advanced models risk delivering inaccurate or misleading forecasts, hindering effective process optimization and potentially leading to flawed decision-making in critical applications.

Conventional predictive models, frequently trained on historical process data, often falter when confronted with novel situations or unforeseen variations. This limitation stems from an inability to effectively extrapolate learned patterns beyond the specific conditions of the training dataset, a phenomenon known as poor generalization. Consequently, decisions informed by these models can prove suboptimal, or even detrimental, when applied to previously unencountered process instances. The ramifications extend beyond mere inaccuracy; a lack of robustness hinders proactive process optimization, as the models fail to reliably anticipate and adapt to evolving operational realities, ultimately diminishing the potential for improved efficiency and reduced costs. This challenge underscores the need for predictive methodologies capable of handling the inherent dynamism and uncertainty present in real-world process environments.

Preprocessing prepares input traces for multi-step prediction by appending start/end tokens, padding to ensure equal input/output sizes, and optionally incorporating time/resource features, enabling the training of both single and multi-task models.

Data Foundations: Quality and Rigorous Partitioning

Process mining (PPM) model accuracy is directly dependent on the quality of event logs used for analysis and prediction; these logs must contain accurate, complete, and consistently formatted records of process instances and their associated events. Key attributes typically include a case ID, activity name, and timestamp, but may also include resource information or event-specific data. Data quality issues, such as missing timestamps, inconsistent activity naming, or inaccurate case assignments, can introduce significant bias and reduce the reliability of derived insights and predictive models. Consequently, thorough data cleaning, preprocessing, and validation are essential initial steps in any PPM project to ensure the integrity of the analysis and the robustness of the resulting predictions.

Data splitting is a fundamental practice in predictive process mining (PPM) model development, involving the partitioning of event log data into three distinct subsets: training, validation, and testing. The training set, typically comprising 70-80% of the data, is used to train the predictive model, allowing it to learn patterns and relationships within the process. The validation set, around 10-15%, is used to tune model hyperparameters and prevent overfitting to the training data. Finally, the testing set, comprising the remaining 10-15%, provides an unbiased evaluation of the model’s generalization performance on unseen data. Proper data splitting ensures that the model’s ability to accurately predict future process behavior is assessed realistically and not simply a memorization of the training data, which is crucial for reliable deployment and performance in real-world scenarios.

Process Prediction Mining (PPM) addresses several distinct predictive tasks, each necessitating specific modeling techniques. Next Activity Prediction focuses on determining the most likely subsequent event given the current process state. Remaining Time Prediction estimates the duration until a case completes, or until a specific activity is finished. Next Timestamp Prediction forecasts when the next event will occur, considering temporal information. Finally, Suffix Prediction aims to predict the remaining sequence of activities in a process instance. These tasks differ in their input data requirements, evaluation metrics, and optimal algorithmic approaches; for example, remaining time prediction often utilizes regression models, while next activity prediction commonly employs classification techniques.

Architectural Refinements: Harnessing the Power of Deep Networks

Long Short-Term Memory (LSTM) networks are a prevalent choice for Predictive Process Monitoring (PPM) due to their capacity to model temporal dependencies within sequential process data. However, training deep LSTM networks can be susceptible to issues like vanishing or exploding gradients, leading to instability and suboptimal performance. Normalization techniques, such as Batch Normalization and Layer Normalization, address these challenges by normalizing the activations of each layer. Batch Normalization normalizes activations across a batch of samples, while Layer Normalization normalizes across the features within a single sample. Both methods help stabilize the learning process, accelerate convergence, and often improve the generalization capability of the LSTM network when applied to PPM tasks. These techniques allow for higher learning rates and can reduce the need for careful weight initialization.

Batch Normalization (BN) and Layer Normalization (LN) are techniques employed to improve the training process and overall performance of Long Short-Term Memory (LSTM) networks. BN normalizes the activations of a layer across a batch of training examples, reducing internal covariate shift and allowing for higher learning rates. This is achieved by calculating the mean and variance for each feature across the batch and normalizing the activations accordingly. Layer Normalization, conversely, normalizes activations across the features within a single training example, making it particularly effective for recurrent neural networks like LSTMs where batch sizes may be small or variable. Both techniques help to stabilize the learning process by preventing activations from becoming too large or too small, leading to faster convergence and improved generalization performance. The application of either BN or LN typically involves scaling and shifting the normalized activations using learnable parameters, allowing the network to adapt the normalization to the specific characteristics of the data.

Transformer architectures address limitations in recurrent models like LSTMs when processing extended sequences of process data. Utilizing self-attention mechanisms, these models weigh the importance of different data points within the sequence, enabling the capture of long-range dependencies without the sequential processing constraints of RNNs. This parallelization capability significantly reduces training time and allows the model to directly relate any two data points, regardless of their distance in the sequence. The self-attention calculation involves three learned weight matrices – Query, Key, and Value – to compute attention scores that determine the contribution of each input element to the representation of other elements. This differs from RNNs which process data sequentially, potentially losing information over long sequences due to the vanishing or exploding gradient problem.

Validating Predictive Power: Metrics and Standardization

Evaluating the effectiveness of Next Activity Prediction relies heavily on metrics like Accuracy and Balanced Accuracy, which quantify how well a model correctly forecasts subsequent actions. Recent studies demonstrate significant progress in this area, with certain datasets now yielding prediction accuracies exceeding 92%. While standard Accuracy calculates the ratio of correct predictions to total predictions, Balanced Accuracy addresses potential class imbalances – a common issue in activity recognition – by averaging the recall across all activities, thus providing a more robust evaluation. These metrics are not merely numerical scores; they represent a model’s ability to understand and anticipate user behavior, which is crucial for applications ranging from personalized assistance to proactive system optimization.

Evaluating the accuracy of predicted timestamps requires specific metrics beyond simple correctness; therefore, researchers commonly employ Mean Absolute Error (MAE) and Log-Cosh loss to assess Next Timestamp Prediction performance. MAE calculates the average magnitude of the errors between predicted and actual timestamps, providing an easily interpretable measure of prediction inaccuracy. However, MAE can be sensitive to outliers. Log-Cosh loss, defined as $log(cosh(y – \hat{y}))$ where $y$ is the actual value and $\hat{y}$ is the predicted value, offers a smoother, less sensitive alternative, effectively mitigating the impact of extreme errors and promoting more robust evaluation of timestamp prediction models. Both metrics contribute to a comprehensive understanding of a model’s ability to accurately forecast temporal events.

The scientific community increasingly grapples with a reproducibility crisis, where repeating studies often yields inconsistent results. To address this within the field of Process Prediction Models (PPMs), researchers have developed SPICE – a standardized framework designed to ensure consistent evaluation and comparison of different PPM approaches. This framework isn’t simply about reporting metrics; it defines a common language and methodology for assessing performance, encompassing dataset preparation, model training, and evaluation procedures. By mandating transparent reporting and a shared experimental setup, SPICE facilitates rigorous scrutiny of new PPMs and allows for meaningful comparisons between them. The presented work highlights the critical role of SPICE in moving the field beyond isolated successes and toward a more robust and cumulative body of knowledge, ultimately accelerating progress in process prediction.

Recent investigations into next-event prediction reveal a substantial performance gain through a standardized approach to remaining time estimation. Specifically, models designed to directly predict the time until a forthcoming event outperformed iterative methods – those that predict events sequentially – by a margin of 20%. This improvement underscores the efficacy of establishing consistent evaluation frameworks, as demonstrated by the SPICE methodology. By focusing on direct prediction rather than step-by-step approximation, researchers were able to leverage a more holistic understanding of temporal patterns, leading to significantly enhanced accuracy and a move towards more reliable predictive modeling in process mining and related fields. This direct approach not only boosts performance but also simplifies model complexity and improves interpretability.

Refining Predictions: The Power of Diverse Sampling

Suffix prediction, a critical component of process mining and sequence modeling, greatly benefits from the implementation of diverse sampling techniques. Methods like Random Sampling and Greedy Sampling enable the generation of multiple potential process suffixes, moving beyond deterministic, single-path predictions. Random Sampling introduces stochasticity, exploring a broader range of possibilities and fostering diversity in the generated sequences, while Greedy Sampling prioritizes locally optimal choices for efficient suffix construction. This combination allows systems to not only predict likely continuations of a process but also to efficiently generate a variety of plausible outcomes, which is crucial for applications requiring adaptability and resilience in dynamic environments. The ability to produce diverse and relevant suffixes directly impacts the accuracy and usefulness of predicted process sequences, improving the overall effectiveness of process discovery and monitoring.

Investigations into suffix prediction demonstrate the efficacy of random sampling techniques, achieving a similarity score, denoted as símDL, of 0.391. This metric quantifies the degree to which predicted process sequences align with expected outcomes, suggesting a substantial level of accuracy in forecasting subsequent steps. The result indicates that, by randomly selecting potential suffixes during prediction, the model can generate plausible continuations with a considerable degree of fidelity. While further refinement is ongoing, this figure establishes a baseline for evaluating the performance of various sampling strategies and their impact on the overall predictive capability of process mining algorithms.

The efficacy of process mining and prediction relies heavily on the ability to generate plausible and accurate sequences of events; advanced sampling methods demonstrably enhance this capability. By moving beyond simple, deterministic predictions, techniques like random and greedy sampling introduce controlled variation, allowing models to explore a wider range of potential process pathways. This exploration isn’t simply about increasing diversity, however; it’s about identifying sequences that are both statistically likely and contextually relevant to the specific process being modeled. Consequently, predicted sequences become more attuned to real-world complexity, reducing the incidence of improbable or illogical outcomes and ultimately improving the reliability of process analysis and optimization efforts. The resulting improvements translate into more actionable insights and a greater capacity to proactively manage and refine operational workflows.

Further advancements in Predictive Process Monitoring (PPM) hinge on a concerted effort to synthesize these refined sampling techniques – like Random and Greedy Sampling – with contemporary machine learning architectures. While initial results demonstrate improvements in suffix prediction, realizing the full potential of PPM requires moving beyond isolated implementations and embracing standardized evaluation frameworks. This will enable rigorous comparison of different approaches, facilitate reproducibility, and accelerate the development of more robust and reliable predictive models. Ultimately, a cohesive integration of advanced sampling with modern tools promises to unlock new levels of accuracy and efficiency in process anomaly detection and control, pushing the boundaries of what’s achievable in industrial settings.

The pursuit of robust evaluation in Predictive Process Mining, as detailed in this work, mirrors a fundamental human condition. Blaise Pascal observed, “The eloquence of angels is not heard, only the sighing of the wind.” Similarly, the true strength of a model isn’t demonstrated through elaborate metrics alone, but through the clarity of its consistent performance across varied conditions. SPICE attempts to distill evaluation to its essential components – standardized data splitting and rigorous benchmarking – removing extraneous variables to reveal genuine predictive capability. The library’s focus on reproducibility isn’t merely about technical correctness; it’s a quest for an underlying truth obscured by complexity.

What’s Next?

The provision of SPICE is not, of course, an arrival. It is merely a sharpened chisel, revealing further strata of complication within Predictive Process Mining. The framework addresses readily apparent deficiencies in experimental design – the capricious splitting of data, the inconsistent application of metrics – but these are symptoms, not causes. The true impediment lies in the field’s eagerness to embrace complexity without first establishing a baseline of demonstrable simplicity.

Future work must resist the gravitational pull of ever-more-elaborate models. The focus should shift toward rigorous error analysis, not merely performance gains. A model that explains why it fails is, paradoxically, more valuable than one that succeeds without illumination. The pursuit of reproducibility demands a brutal honesty regarding the limitations of current techniques, and an acknowledgement that, frequently, the signal is lost in a deluge of parameters.

Ultimately, the value of SPICE will be measured not by the number of models it benchmarks, but by the number it discards. The art of process intelligence lies not in predicting every possible outcome, but in identifying – and then elegantly removing – the unnecessary ones. A leaner model, stripped of superfluous features, is not a compromise, but a refinement.

Original article: https://arxiv.org/pdf/2512.16715.pdf

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