Dynamic systems in AI are often complex and heterogeneous, so that an internal specification is not accessible and hence verification techniques like model checking are not applicable. Monitoring is in such cases an attractive alternative, as it deals with the observation of desirable properties along traces generated by an unknown dynamic system. In this work, we consider anticipatory monitoring of linear-time properties enriched with an arbitrary SMT theory over finite traces (data-LTLf). Anticipatory monitoring in this setting is a highly challenging problem and undecidable in general, as the monitoring state depends on both the trace prefix seen so far and all its possible finite continuations. Under reasonable assumptions on the background theory, we present and formally prove the correctness of a novel foundational framework for monitoring data-LTLf properties. The framework combines automata-theoretic methods to handle the temporal aspects of reasoning with automated reasoning techniques to address the first-order dimension. Moreover, we identify for the first time decidable fragments of this monitoring problem that are practically relevant as they combine linear arithmetic with uninterpreted functions, which covers e.g. data-aware business processes and dynamic systems operating over a database. Feasibility is witnessed by a prototype implementation and preliminary evaluation.

An object-centric behavioral constraint (OCBC) model provides an integrated view on entities handled in a process-oriented information system. It defines behavioral constraints between activities operating on data objects and cardinality constraints for relations over activities and objects. Existing techniques for OCBC conformance checking evaluate conditions from the model on an event log. As such, conformance cannot be evaluated directly over the relational databases that typically store the event data. Instead, log construction is required, which induces computational overhead and decouples data management and process analysis. In this paper, we show how to incorporate OCBC conformance checking directly within relational database systems. To this end, we introduce conformance queries for two representations of process data that differ in how they capture the relationships between events and objects, i.e., explicitly by dedicated tables or implicitly through foreign keys. Moreover, we present strategies for continuous conformance monitoring under database updates. We demonstrate the feasibility of our approach using event data generated based on the TPC-H benchmark.

Neurosymbolic (NeSy) Artificial Intelligence aims to integrate Deep Learning (DL) architectures with symbolic reasoning. While initial NeSy approaches have targeted mainly symbolic reasoning in propositional and first-order logics, recent works have started to address the construction of neurosymbolic frameworks for Temporal Logics, and in particular for LTLf. These approaches have established temporal NeSy as a promising research direction, laying the foundations for learning under temporal constraints. Nonetheless, they leave many questions unanswered. From a theoretical perspective, several differentiable semantics for interpreting LTLf have been proposed but have not yet been formally and systematically defined within a unified framework. Moreover, existing approaches commonly rely on automata to represent temporal knowledge, resulting in limited scalability. Motivated by this research gap, this paper provides the following contributions: (i) formally defining different fuzzy semantics for LTLf, and systematically analysing theoretical properties regarding equivalences and dualities of temporal operators; (ii) showing how these semantics can be directly integrated within a novel NeSy framework, called DiffLTLf, enabling flexible and scalable learning without relying on the usage of automata; and (iii) introducing a novel evaluation protocol of increased complexity of learning tasks w.r.t. existing benchmarks. Our results show that the choice of fuzzy semantics has a significant impact on predictive performance. Moreover, DiffLTLf achieves performance on par with, and sometimes superior to, state-of-the-art probabilistic approaches while substantially improving scalability. Taken together, these results establish direct fuzzy interpretations as a competitive and scalable alternative to existing temporal NeSy frameworks.

Most of the existing neuro-symbolic AI methods focus on the scenario of static knowledge where objects do not change according to a temporal dimension. Temporal neuro-symbolic works are still under explored and are mainly developed for time-interval logic or propositional linear temporal logic. There is a lack of models studying linear temporal logics with predicates that deal with objects whose properties and relations change through the time. We present First-Order Temporal Logic Tensor Networks (FOT-LTN) that is an extension of Logic Tensor Networks (LTN) that fills this gap by considering a linear-temporal dimension. In particular, FOT-LTN joins the syntax of First-Order Linear Temporal Logic with the fuzzy (and real-valued) semantics of LTN obtaining a framework that supports both temporal operators and quantifiers and is totally differentiable. A first evaluation regards a temporal knowledge graph completion task on two synthetic datasets showing better performance of FOT-LTN with respect to dedicated (purely neural) methods.

Object-centric process mining extends traditional approaches by associating events with multiple objects and capturing their interactions, which are essential to understanding process dynamics. However, existing case-centric models and current Object-Centric Event Data (OCED) approaches struggle with ambiguity and limited support for time and dynamic relations. In this work, we address these limitations through a three-step approach. First, we analyze key issues in existing OCED metamodels, highlighting their ambiguity and expressiveness gaps. Second, we enhance the OCED Core Model by grounding it in gUFO, a lightweight version of the UFO-B foundational ontology for representing objects, events, time, and their relations, resulting in a new metamodel called gOCED. Third, we show that gOCED preserves the simplicity of existing models while extending them to resolve the identified limitations.

Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

Evaluating process discovery algorithms requires ground truth models against which discovered models can be compared. A critical but underexplored dimension of such evaluations concerns how algorithms perform when the underlying process model is not static but evolving. For example, processes can become more or less restrictive over time due to regulatory, organizational, or operational changes. Addressing this requires the ability to systematically construct sets of process models that are hierarchically related through specialization and generalization. While procedural ground truth model generation is well established, declarative process mining lacks comparable support, and the ability to systematically specialize or generalize generated models is largely absent. This gap is consequential given the growing importance of declarative models in dynamic business environments, where behavior is defined through constraints rather than explicit execution paths. This paper addresses this gap by presenting algorithms for the synthetic generation and specialization of Declare ground truth models, following the methodology of algorithm engineering. The proposed specialization algorithm is grounded in a theorem that characterizes when one declarative model constitutes a specialization of another, enabling controlled reduction of allowable behavior through constraint modification. Together, these techniques support the construction of hierarchically related model sets for use in process discovery and conformance checking evaluations across a spectrum of behavioral flexibility. We provide knowledge about the design of the algorithms by evaluating effectiveness and efficiency. Results show that model generation succeeds in over 80% of cases when models contain between 1 and 25 constraints and involve 11 to 35 activities. Increasing the number of constraints negatively impacts success rates and execution time, with the generator exhibiting exponential and the specializer linear time trends. Template choice also affects performance, with Response and Precedence improving success rates while Exclusive Choice reduces them. These findings offer practical guidance for generating declarative ground truth models that are both realistic and systematically varied, supporting more rigorous evaluation of declarative process mining algorithms.

Object-centric process mining examines how processes interact with multiple co-evolving objects, and has gained great interest in recent years. However, object-centric event logs (OCELs) leave object relationships underspecified in several respects, especially if relationships are dynamic, i.e., they change over time. In this paper, we identify and formally define for the first time assumptions that allow to represent and manipulate dynamic relationships in OCELs in a semantically unambiguous way. We evaluate existing logs to show that our assumptions are often satisfied, ensuring full transparency of relationship semantics.

Just like traditional BPM systems, agentic BPM systems are built around a specification of the process under consideration. Their distinguishing feature, however, is that the execution of the process is driven by multiple autonomous decision-makers, referred to as agents. Since such agents cannot be fully controlled, the process specification is augmented with explicit objectives, or goals, assigned to the participating agents. Agents then pursue these goals, at least to the best of their efforts, under suitable assumptions on the behavior of others, by adopting appropriate strategies. Centrally, the organization enacting the process can use these specifications to provide guardrails on the decision-making capabilities of agents at the strategy level. This paper sets up the mathematical foundations of such systems in three key settings and analyzes four foundational problems of agentic BPM.

Process mining aims to obtain insights from event logs through the automated analyses of recorded process data in information systems, with the ultimate aim to improve business processes running in organisations. However, real-life event logs are often incomplete, noisy, or ambiguous, such as missing timestamps or having ambiguous event labels, which traditional deterministic models cannot capture. Recent process mining developments have considered uncertainty in process mining artifacts more explicitly: in logs of recorded process behaviour, uncertainty may implicitly or explicitly influence process mining outcomes, while in process models, explicit uncertainty allows analysts to interpret and value outcomes. In this paper, we provide a conceptual foundation for uncertainty in process mining by introducing a four-level specification that separately addresses uncertainty in log attributes (e.g., activity labels of events, frequencies) and model elements (e.g., service times, read guards). For each type of uncertainty, we illustrate the levels with concrete examples to help understanding and application. We then provide a structured overview of the state of the art in stochastic process mining, classified using our specification, and present key open research challenges.

Together with Information Systems, we celebrate the journal’s 50th anniversary and the 10th anniversary of our joint work on a systematic framework for compliance monitoring functionalities.

Dynamic systems in AI are often complex and heterogeneous, so that an internal specification is not accessible and verification techniques such as model checking are not applicable. Monitoring is in such cases an attractive alternative, as it evaluates desirable properties along traces generated by an unknown dynamic system. In this work, we consider anticipatory monitoring of linear-time properties enriched with an arbitrary SMT theory over finite traces (LTLfMT). Anticipatory monitoring in this setting is highly challenging, as the monitoring state depends on both the trace prefix seen so far and all its possible finite continuations. Under reasonable assumptions on the background theory, we present and formally prove the correctness of a novel foundational framework for monitoring properties in an expressive fragment of LTLfMT. The framework combines automata-theoretic methods to handle the temporal aspects of the logic, with automated reasoning techniques to address the first-order dimension. Moreover, we identify for the first time decidable fragments of this monitoring problem that are practically relevant as they combine linear arithmetic with uninterpreted functions, which covers e.g. data-aware business processes and dynamic systems operating over a read-only database. Feasibility is witnessed by a prototype implementation and preliminary evaluation.

A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines the boundaries in which the system must operate. Compared to traditional process models, the process frame should, in principle, provide a somewhat more permissive representation of the managed processes, such that the (semi) autonomous behavior of an ABPMS, referred to as framed autonomy, could emerge. At the same time, it is not limited to a single linguistic or symbolic formalism and may incorporate heterogeneous knowledge ranging from predefined procedures to commonsense rules and best practices. In this paper, we conceptualize the notion of an ABPMS process frame as a hybrid business process representation, consisting of semi-concurrently executed procedural and declarative process models. We rely on our earlier works to outline the execution semantics of this type of process frame, arguing in favor of adopting the open-world assumption of the declarative paradigm also for procedural process models. The latter leads to a constraint-like interpretation, where each procedural model is considered to constrain the activities within that model, without imposing explicit execution requirements nor limitations on activities that may be present in other models. This is analogous to existing declarative languages, such as Declare, where each constraint has a direct effect only on the specific activities being constrained. Given this similarity, we propose mapping subsets of discovered declarative constraints into equivalent semi-concurrently executed procedural fragments, thus laying the foundation for a corresponding process (frame) discovery approach.

This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for governing autonomous agents executing processes in organizations. From a management perspective, APM represents a paradigm shift from the traditional view on business processes. This shift is driven by the realization of process awareness by agent-oriented abstractions: software and human agents act as primary functional entities that perceive, reason, and act within explicit process frames. Thus, APM moves away from automation-oriented BPM towards systems in which autonomy is constrained, aligned, and made operational through process aware agents.We introduce the core abstractions and architectural elements required to realize APM systems and elaborate on four key capabilities that agents in APM systems must support: framed autonomy, explainability, conversational actionability, and self-modification. These capabilities jointly ensure that agents’ goals are aligned with organizational goals and that agents behave in a framed yet proactive manner in pursuing those goals. We discuss the extent to which the capabilities can be realized and identify research challenges whose resolution requires further advances in BPM, AI, and multi-agent systems. The manifesto thus serves as a roadmap for bridging these communities and for guiding the development of APM systems in practice.

A stochastic process model combines control flow and stochasticity in a single representation. Answering queries on the behaviour and probabilities of such a model is essential not only for analysis and verification, but also towards stochastic process mining, in which the frequency of events and traces is explicitly taken into account. In this paper, we focus on probability-based queries on stochastic process models, dealing with questions like “what are the 10 most likely traces?" and “what are the traces with a probability higher than 1

While traditional process mining techniques assume that the event data used for analysis are faithful and complete, the field is increasingly recognizing the need of novel techniques able to accommodate uncertainty on events and the activities they refer to. The vast majority of approaches proposed so far adopts a probabilistic interpretation of uncertainty, where every event comes with a probability distribution on the possible activities. Motivated by scenarios where event data are obtained through event recognition pipelines starting from raw, unstructured data, in this work we argue for the first time for a different interpretation of uncertainty, akin to a fuzzy semantics. Under this interpretation, every event comes with an indication of the “intensity” of execution of each activity. In this novel setting, we study a form of conformance checking where fuzzy events are verified against declarative temporal rules specified using linear temporal logic over finite traces (LTLf), the logic underlying the well-known Declare declarative process specification language. Dealing with fuzzy events requires to relax the assumption that at each instant only one activity is executed, and in turn to adopt a fuzzy semantics for the state formulae of the logic. Technically, we tackle LTL-based conformance checking of fuzzy event logs with a fourfold contribution. First, we introduce a fuzzy counterpart of LTLf tailored to our purpose. Second, we define conformance checking over fuzzy event logs as a verification problem in this logic. Third, we provide an efficient implementation based on the PyTorch Python library, suited to check conformance of multiple fuzzy traces at once. Finally, we show feasibility and scalability through an extensive experimental evaluation.

We propose a novel algorithm for epistemic planning based on dynamic epistemic logic (DEL). The novelty is that we limit the depth of reasoning of the planning agent to an upper bound b, meaning that the planning agent can only reason about higher-order knowledge to at most (modal) depth b. We then compute a plan requiring the lowest reasoning depth by iteratively incrementing the value of b. The algorithm relies at its core on a new type of "canonical" b-bisimulation contraction that guarantees unique minimal models by construction. This yields smaller states wrt. standard bisimulation contractions, and enables to efficiently check for visited states. We show soundness and completeness of our planning algorithm, under suitable bounds on reasoning depth, and that, for a bound b, it runs in (b+1)-EXPTIME. We implement the algorithm in a novel epistemic planner, DAEDALUS, and compare it to the EFP 2.0 planner on several benchmarks from the literature, showing effective performance improvements.

Despite growing interest in process analysis and mining for data-aware specifications, alignment-based conformance checking for declarative process models has focused on pure control-flow specifications, or mild data-aware extensions limited to numerical data and variable-to-constant comparisons. This is not surprising: finding alignments is computationally hard, even more so in the presence of data dependencies. In this paper, we challenge this problem in the case where the reference model is captured using data-aware Declare with general data types and data conditions. We show that, unexpectedly, it is possible to compute data-aware optimal alignments in this rich setting, enjoying at once efficiency and expressiveness. This is achieved by carefully combining the two best-known approaches to deal with control flow and data dependencies when computing alignments, namely A* search and SMT solving. Specifically, we introduce a novel algorithmic technique that efficiently explores the search space, generating descendant states through the application of repair actions aiming at incrementally resolving constraint violations. We prove the correctness of our algorithm and experimentally show its efficiency. The evaluation witnesses that our approach matches or surpasses the performance of the state of the art while also supporting significantly more expressive data dependencies, showcasing its potential to support real-world applications.

Object-centric process mining investigates the intertwined behavior of multiple objects in business processes. From object-centric event logs, object-centric Petri nets (OCPN) can be discovered to replay the behavior of processes accessing different object types. Although they indicate how objects flow through the process and co-occur in events, OCPNs remain underspecified about the relationships of objects. Hence, they are not able to represent synchronization, i.e. executing objects only according to their intended relationships, and fail to identify violating executions. Existing formal modeling approaches, such as object-centric Petri nets with identifiers (OPID), represent object identities and relationships to synchronize them correctly. However, OPID discovery has not yet been studied. This paper uses explicit data models to bridge the gap between OCPNs and formal OPIDs. We identify the implicit assumptions of stable many-to-one relationships in object-centric event logs, which implies synchronization of related objects. To formally underpin this observation, we combine OCPNs with explicit stable many-to-one relationships in a rigorous mapping from OCPNs to OPIDs explicitly capturing the intended stable relationships and the synchronization of related objects. We prove that the original OCPNs and the resulting OPIDs coincide for those executions that satisfy the intended relationships. Moreover, we provide an implementation of the mapping from OCPN to OPID under stable relationships.

A choreographed process defines the agreement between organizations with respect to the protocol that should be respected when the involved organizations interact with each other through their business processes. The resulting BPMN Choreographed Process that models this protocol can formalize business constraints. Although there are solutions that can monitor whether these constraints are satisfied, situations of non-conformance that have an impact on monitoring can arise. This happens especially when the involved parties are not equipped with a BPMS that can force business processes to align with the protocol. The purpose of this paper is twofold. First of all, it proposes the use of an extension of timed commitments to express business constraints. This extension takes into account situations of non-conformance that are not acceptable by the protocol, but are compatible with business objectives. In addition, it proposes a monitoring application solution that can support the proposed extension.