Controlled experiments in Process Mining primarily rely on synthetic event logs generated from declarative or procedural process modeling languages, which often lack the flexibility needed for precise experimental setups. In this paper, we introduce a novel log generator designed to address this gap by enabling fine-tuned customization of synthetic logs. The log generator, implemented using the declarative language Answer Set Programming (ASP), allows researchers to define sophisticated scenarios that are impossible to express with standard log generators to create specific experimental conditions. • Fine-grained and flexible event log generation with a new position-based modeling language ( PosLan ) and Answer-Set Programming. • Support for synthetic logs with characteristics that mimic complex real-world patterns. • Variability control of the generated event log via Answer-Set Programming.

This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitoring (PPM) approach that integrates data-driven learning with temporal logic-based prior knowledge. While recent approaches leverage deep learning models for suffix prediction, they often fail to satisfy even basic logical constraints due to the absence of explicit integration of domain knowledge during training. We propose a novel method to incorporate Linear Temporal Logic over finite traces (LTLf) into the training process of autoregressive sequence predictors. Our approach introduces a differentiable logical loss function, defined using a soft approximation of LTLf semantics and the Gumbel-Softmax trick, which can be combined with standard predictive losses. This ensures the model learns to generate suffixes that are both accurate and logically consistent. Experimental evaluation on three real-world datasets shows that our method improves suffix prediction accuracy and compliance with temporal constraints. We also introduce two variants of the logic loss (local and global) and demonstrate their effectiveness under noisy and realistic settings. While developed in the context of BPM, our framework is applicable to any symbolic sequence generation task and contributes toward advancing Neuro-Symbolic AI.

Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools.

So far, approaches for business process modeling, enactment and monitoring have mainly based on process specifications consisting of a single process model. This setting aptly captures monolithic scenarios from domains in which all possible behaviors can be folded into a single model. However, this strategy cannot be applied to domains where multiple interacting (procedural) processes simultaneously work over the same objects, in the presence of additional (declarative) constraints relating activities from the same or different processes. A relevant example for this setting is that of healthcare, where co-morbid patients may be subject to multiple clinical pathways at once, in the presence of additional, general constraints capturing basic medical knowledge. To fill this gap, we have previously presented the M3 Framework and an accompanying monitoring technique, which allows for a hybrid representation of a process using both procedural and declarative models, and supports the modular creation of multi-process specifications where domain experts can focus on specific procedures and domain constraints without being forced to merge them into one single specification. In this paper, we make significant extensions to this framework, allowing us to go from simple toy examples towards addressing practical real-life scenarios. We achieve this by introducing a richer form of integration between the interacting process components, in particular supporting asynchronous and synchronous activities that may operate over local and global (shared) data variables. This is framed by a discussion of the business meaning of these concepts, the introduction of the corresponding modeling patterns, and the application of our approach to real-life business processes, the latter being the driving-force behind this paper.

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this article, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely, log generation, conformance checking, and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT.

Declarative business process (BP) models define the behavior of BPs as a set of temporal constraints, which can be summarized as a deterministic finite state automaton (DFA). Declarative BP discovery aims at inferring such constraints from event logs. To this aim, it requires as additional input the set of candidate constraints to be verified with respect to the event log. Intuitively, this restricts the discovery task to a conformance checking activity between a predefined set of constraint templates and an event log, preventing to learn any observed behavior that is not captured by those templates. In this paper, we investigate how to leverage Model Learning (ML) for the automated discovery of the DFA underlying the behavior of a declarative BP model, without using any further a-priori information in addition to the event log. To assess the quality of the discovered DFA, we introduce a novel definition of the standard process mining quality metrics, i.e., precision, generalization and simplicity, tailored to DFAs. Finally, a preliminary evaluation performed with real-life logs shows that ML enables to generate extremely simpler DFAs than state-of-the-art BP declarative discovery techniques, keeping similar values of precision and generalization.

We formalize and study business process systems that are centered around "business artifacts", or simply "artifacts". Artifacts are used to represent (real or conceptual) key business entities, including both their data schema and lifecycles. The lifecycle of an artifact type specifies the possible sequencings of services that can be applied to an artifact of this type as it progresses through the business process. The artifact-centric approach was introduced by IBM, and has been used to achieve substantial savings when performing business transformations.