Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading to more favourable outcomes. Existing counterfactual methods do not readily apply to temporal domains, such as that of process mining, where data take the form of traces of activities that must obey to temporal background knowledge expressing which dynamics are possible and which not. Specifically, counterfactuals generated off-the-shelf may violate the background knowledge, leading to inconsistent explanations. This work tackles this challenge by introducing a novel approach for generating temporally constrained counterfactuals, guaranteed to comply by design with background knowledge expressed in Linear Temporal Logic on process traces (LTLp). We do so by infusing automata-theoretic techniques for LTLp inside a genetic algorithm for counterfactual generation. The empirical evaluation shows that the generated counterfactuals are temporally meaningful and more interpretable for applications involving temporal dependencies.

Generative Artificial Intelligence (GenAI) techniques have recently emerged as promising tools for challenges in Process Mining such as missing data, small datasets, and the generation of complex what-if scenarios. A major limitation of current approaches is their inability to produce sets of process traces in a mutually dependent manner. Traces are generated independently, failing to reflect crucial process-level phenomena such as concurrency, resource contention, and workload distribution. This limitation significantly reduces the realism and utility of generated logs, yet has received limited attention in the literature. In contrast, Business Process Simulation (BPS) excels at preserving process constraints and interdependencies. However, in the presence of specific or complex what-if scenarios, this requires the adjustment of several simulation parameters, which can be challenging to achieve. Motivated by these issues, where generative methods lack consistency and BPS methods lack flexibility, we propose a novel data generation framework combining GenAI with BPS. The proposed approach enables generating diverse and complex whatif scenarios via data-driven generative models, which are then validated and refined through BPS to ensure consistency and feasibility at the log level. We call the approach What-If Scenarios via Data-driven generative mOdels and siMulation (WISDOM). We compare several existing methods for process data generation and assess their outputs using an established evaluation framework. Results show that while generative methods fail to capture the full intricacies of process behaviour, their integration with BPS produces traces that are both diverse and compliant with process constraints. This hybrid approach addresses a critical gap in the literature and opens new avenues for robust, simulation-grounded generation methods in process mining.

Proactive scheduling creates robust offline schedules that optimize resource utilization and minimize job flow times. This work addresses scheduling challenges in business processes, often encountered in service systems, which differ from traditional applications like manufacturing due to inherent uncertainties in activity durations, and human resource availability. We model the business process scheduling problem (BPSP) as a variation of stochastic resource-constrained multi-project scheduling (RCMPSP), and apply process mining to infer unknown parameter values from historical event data. To overcome the randomness in activity durations, we transform the problem into its deterministic counterpart, and prove that the latter provides a lower bound on the Makespan of the stochastic problem. Our approach integrates data-driven Monte Carlo simulation with constraint programming to generate proactive schedules that guarantee, with high probability, that the Makespan remains below a predefined threshold. We evaluate our approach using synthetic datasets with varying levels of uncertainty and size. In addition, we apply the approach to a real-world dataset from an outpatient cancer hospital, demonstrating its effectiveness in optimizing the process Makespan by an average of 5% to 14%.

State-of-the-art approaches for integrating symbolic knowledge with deep learning architectures have demonstrated promising results in static domains. However, methods to handle temporal logic specifications remain underexplored. The only existing approach relies on an explicit representation of a finite-state automaton corresponding to the temporal specification. Instead, we aim at directly injecting the temporal knowledge into the neural model without having to rely on a separate symbolic structure. Specifically, we propose a neurosymbolic framework designed to incorporate temporal logic specifications, expressed in Linear Temporal Logic over finite traces (LTLf), directly into deep learning architectures for sequence-based tasks. We extend the Iterative Local Refinement (ILR) neurosymbolic algorithm, leveraging the recent introduction of fuzzy LTLf interpretations. We name this proposed method Temporal Iterative Local Refinement (T-ILR). We assess T-ILR on an existing benchmark for temporal neurosymbolic architectures, consisting of the classification of image sequences in the presence of temporal knowledge. The results demonstrate improved accuracy and computational efficiency compared to the state-of-the-art method.

Predictive Process Monitoring leverages machine learning models to predict the future states of business processes. Most Predictive Process Monitoring approaches rely on black-box models which, while powerful, lack interpretability, limiting their applicability in critical decision-making scenarios. Explainable Predictive Process Monitoring has emerged to address this gap, focusing on delivering actionable and transparent insights into predictions. Current approaches, however, often fail to incorporate multiple process perspectives and granularity levels in their explanations, overlooking crucial factors that influence process outcomes. This paper proposes a novel Explainable Predictive Process Monitoring approach to deliver explanations, integrating multiple process perspectives at various levels of granularity. The proposed approach addresses the limitations of existing methods, providing comprehensive, process context-aware explanations. The effectiveness of the proposed method is assessed through experimental evaluations on real-life event logs, showing how the integration of diverse process perspectives improves the interpretability and predictive insights of local explanations with improvements ranging from 2%–3% to 15%–20% depending upon the event log under analysis.

Recent research in Computer Science has investigated the use of Deep Learning (DL) techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) model. The main idea of this combination is to maintain a white box simulation model complement it with information provided by DL models to overcome the unrealistic or oversimplified assumptions of traditional DESs. State-of-the-art techniques in BPM combine Deep Learning and Discrete Event Simulation in a post-integration fashion: first an entire simulation is performed, and then a DL model is used to add waiting times and processing times to the events produced by the simulation model. In this paper, we aim at taking a step further by introducing RIMS (Runtime Integration of Machine Learning and Simulation). Instead of complementing the outcome of a complete simulation with the results of predictions a posteriori, RIMS provides a tight integration of the predictions of the DL model at runtime during the simulation. This runtime-integration enables us to fully exploit the specific predictions while respecting simulation execution, thus enhancing the performance of the overall system both w.r.t. the single techniques (Business Process Simulation and DL) separately and the post-integration approach. In particular, the runtime integration ensures the accuracy of intercase features for time prediction, such as the number of ongoing traces at a given time, by calculating them during directly the simulation, where all traces are executed in parallel. Additionally, it allows for the incorporation of online queue information in the DL model and enables the integration of other predictive models into the simulator to enhance decision point management within the process model. These enhancements improve the performance of RIMS in accurately simulating the real process in terms of control flow, as well as in terms of time and congestion dimensions. Especially in process scenarios with significant congestion – when a limited availability of resources leads to significant event queues for their allocation – the ability of RIMS to use queue features to predict waiting times allows it to surpass the state-of-the-art. We evaluated our approach with real-world and synthetic event logs, using various metrics to assess the simulation model's quality in terms of control-flow, time, and congestion dimensions.

In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing the features of the selected processes. However, current DL generative models are limited in their ability to adapt the learned distributions to generate data samples based on specific conditions or attributes. This limitation is particularly significant because the ability to control the type of generated data can be beneficial in various contexts, enabling a focus on specific behaviours, exploration of infrequent patterns, or simulation of alternative "what-if" scenarios.In this work, we address this challenge by introducing a conditional model for process data generation based on a conditional variational autoencoder (CVAE). Conditional models offer control over the generation process by tuning input conditional variables, enabling more targeted and controlled data generation. Unlike other domains, CVAE for process mining faces specific challenges due to the multiperspective nature of the data and the need to adhere to control-flow rules while ensuring data variability. Specifically, we focus on generating process executions conditioned on control flow and temporal features of the trace, allowing us to produce traces for specific, identified sub-processes. The generated traces are then evaluated using common metrics for generative model assessment, along with additional metrics to evaluate the quality of the conditional generation.