Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently been applied to this task, learning directly from event logs without requiring a predefined process model. However, since anomaly detection is a purely statistical task, these models fail to incorporate human domain knowledge. As a result, rare but conformant traces are often misclassified as anomalies due to their low frequency, which limits the effectiveness of the detection process. Recent developments in the field of neuro-symbolic AI have introduced Logic Tensor Networks (LTN) as a means to integrate symbolic knowledge into neural networks using real-valued logic. In this work, we propose a neuro-symbolic approach that integrates domain knowledge into neural anomaly detection using LTN and Declare constraints. Using autoencoder models as a foundation, we encode Declare constraints as soft logical guiderails within the learning process to distinguish between anomalous and rare but conformant behavior. Evaluations on synthetic and real-world datasets demonstrate that our approach improves F1 scores even when as few as 10 conformant traces exist, and that the choice of Declare constraint and by extension human domain knowledge significantly influences performance gains.

Business process simulation (BPS) has emerged as a crucial tool that offers a risk-free virtual environment to analyze, test, and optimize complex service compositions and orchestrations in cloud and edge computing environments. BPS enables the evaluation of alternative scenarios (a.k.a. “what-if” scenarios) by capturing the dynamic behavior of service interactions, including control-flow, task durations, and resource utilization. Several techniques to discover BPS models employ black-box predictors to characterize the run-time simulation aspects. The downside of these approaches is that the rules guiding the predictors cannot be provided in an intelligible form. This means that service architects and process analysts cannot modify or explore the decision-making logic that drives service orchestration and composition in these simulations. Moreover, these models are often deterministic and thus unable to capture uncertainty, which is essential to realistically simulate dynamic environments. This paper presents white-box predictors based on probabilistic decision trees, which are intelligible and easy to configure. The experiments show that white-box predictors can improve the simulation accuracy and variability, while being naturally intelligible.

With the growing availability of unstructured text data in organizations, automating the extraction of process models from natural language descriptions has become increasingly crucial. Traditional rule-based techniques face challenges such as limited generalization and constraints to narrow sets of control-flow constructs. We present a systematic empirical study of Large Language Models (LLMs) for translating natural language sentences into declarative process constraints using the Declare modeling language. Through comprehensive evaluation across seven LLM architectures, we demonstrate that fine-tuned models achieve 97.8% template accuracy compared to 53.8% for existing rule-based approaches. Our fine-tuning approach substantially outperforms prompting techniques (99.4% vs. 56.5% template accuracy), establishing clear guidance for practical deployment. We contribute a benchmark dataset of 969 sentence-constraint pairs across 11 Declare templates, direction-sensitive evaluation metrics, and complete reproducibility materials. The extracted constraints enable practical decision support applications including compliance monitoring, conformance checking, and automated governance dashboards.

The Inductive Miner (IM) family is a prominent class of process discovery techniques, combining efficient recursive decomposition with soundness-by-construction guarantees. However, IM techniques usually assume traces to be totally ordered sequences of activity occurrences. This assumption is convenient, but can introduce systematic bias: activities may have durations, events may share coarse timestamps, or the data may constrain only some event pairs. Forcing such executions into arbitrary sequences hides inherent concurrency and may introduce sequential dependencies that were never observed as causal constraints. Partial orders provide a more faithful representation, but integrating them into IM discovery is challenging because standard abstractions are sequence-based; directly reusing them would require linearizing each partial order, which becomes prohibitively expensive under high concurrency. We introduce a lifting of IM discovery from total orders to partially ordered traces. Instead of redesigning the miner and its cut detection logic, we redefine the trace abstraction layer and the recursive projections to operate directly on partial orders. The approach is conservative over totally ordered traces, avoids linearization explosion, and preserves the recursive structure and guarantees that make IM attractive. Experimental results show that the proposed lifting avoids the combinatorial overhead of linearization, reduces sensitivity to arbitrary tie-breaking in timestamped event data, and allows process behavior to be learned from fewer observations by preserving concurrency at the trace level.

The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. While early versions of POWL relied on strict block-structured operators for choices and loops, the language has recently evolved into POWL 2.0, introducing choice graphs to enable the modeling of non-block-structured decisions and cycles. To bridge the gap between the theoretical advantages of POWL and the practical need for compatibility with established notations, robust model transformations are required. This paper presents a novel algorithm for transforming safe and sound workflow nets (WF-nets) into equivalent POWL 2.0 models. The algorithm recursively identifies structural patterns within the WF-net and translates them into their POWL representation. Unlike the previous approach that required separate detection strategies for exclusive choices and loops, our new algorithm utilizes choice graphs to capture generalized decision and cyclic patterns. We formally prove the correctness of our approach, showing that the generated POWL model preserves the language of the input WF-net. Furthermore, we prove the completeness of our algorithm on the class of separable WF-nets, which corresponds to nets constructed via the hierarchical nesting of state machines and marked graphs. We evaluate our algorithm on large-scale process models to demonstrate its high scalability. Furthermore, to test its practical expressiveness, we applied it to a benchmark of 1,493 industrial and synthetic process models. Our algorithm successfully transformed all models in this benchmark, suggesting that POWL 2.0's expressive power is generally sufficient to capture the complex logic found in real-world business processes. This work paves the way for broader adoption of POWL in practical process analysis and improvement applications.

Digital transformation often entails small-scale updates to the business functions exposed by enterprise systems, which support the execution of organizational business processes. These changes may increase operational friction in process execution, decreasing the process performance, increasing the complexity of maintenance activities, and making system documentation and training obsolete. Detecting and understanding their impact on process execution is therefore crucial to maintain smooth operations. Previous research contributions concerning the tracking and impact analysis of information systems updates are limited in scope, functionality, and tool support. In this paper, we propose a data-driven approach that uses the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates. We model the updates using the configuration of DTOs and the newly introduced concept of actions in DTOs. The impact of the application of actions in a given configuration is quantitatively assessed by different types of impacts: structural, operational, and performance-related. We show the feasibility of the proposed approach by discussing a software prototype implementing it, a case study involving artificially-generated data from a standard ERP procure-to-pay process, and a case study using real-world data from a loan application process.

Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical events and biometrics. However, such approaches fail to incorporate domain-specific process constraints (knowledge), e.g., surgery can only be planned if the patient was released more than a week ago, limiting the adherence to compliance and providing less accurate predictions. In this paper, we present a neuro-symbolic approach for predictive process monitoring, leveraging Logic Tensor Networks (LTNs) to inject process knowledge into predictive models. The proposed approach follows a structured pipeline consisting of four key stages: 1) feature extraction; 2) rule extraction; 3) knowledge base creation; and 4) knowledge injection. Our evaluation shows that, in addition to learning the process constraints, the neuro-symbolic model also achieves better performance, demonstrating higher compliance and improved accuracy compared to baseline approaches across all compliance-aware experiments.

Process mining provides powerful insights into organizational workflows, but extracting these insights typically requires expertise in specialized query languages and data science tools. Large Language Models (LLMs) offer the potential to democratize process mining by enabling business users to interact with process data through natural language. However, using LLMs as direct analytical engines over raw event logs introduces fundamental challenges: LLMs struggle with deterministic reasoning and may hallucinate metrics, while sending large, sensitive logs to external AI services raises serious data-privacy concerns. To address these limitations, we present PMAx, an autonomous agentic framework that functions as a virtual process analyst. Rather than relying on LLMs to generate process models or compute analytical results, PMAx employs a privacy-preserving multi-agent architecture. An Engineer agent analyzes event-log metadata and autonomously generates local scripts to run established process mining algorithms, compute exact metrics, and produce artifacts such as process models, summary tables, and visualizations. An Analyst agent then interprets these insights and artifacts to compile comprehensive reports. By separating computation from interpretation and executing analysis locally, PMAx ensures mathematical accuracy and data privacy while enabling non-technical users to transform high-level business questions into reliable process insights.

Service-oriented architectures facilitate distributed business processes where multiple services collaborate to achieve business goals. However, traditional process mining approaches rely on single-case event logs that fail to capture the complex interactions between services and their associated data objects. Object-Centric Event Logs (OCELs) address this challenge by modeling service processes as graphs of interconnected events and objects, providing a foundation for comprehensive service analysis. While existing querying approaches offer capabilities for traditional event data, they lack the expressiveness needed for analyzing object-centric service interactions. This paper introduces GOProQ, a graphical query language specifically designed for querying object-centric service processes. We identify key requirements for service-oriented process querying, define a formal language that addresses these requirements, and implement an interactive tool for service analysts. Our evaluation across multiple real-world service datasets demonstrates GOProQ’s expressiveness and performance for service monitoring in complex service-oriented environments.

Decomposition is widely used in process mining to enhance scalability, facilitate focused analysis, and mitigate the cognitive complexity of large-scale models. While both process discovery and conformance checking benefit from decomposition, existing approaches—especially those based on single-entry single-exit (SESE) fragments—impose structural constraints that limit diagnostic value. This paper proposes a complementary decomposition technique that partitions a WF-net into isolated subnets, i.e., internally coherent fragments with clear interfaces. The decomposition is hierarchical, independent of SESE assumptions, and compatible with valid system net decompositions, enabling divide-and-conquer conformance checking. Experiments show that our approach improves alignment runtime and yields fitness approximations that are closer to the true values than existing decomposition methods.

Process discovery enables organizations to analyze and improve their operations by automatically deriving process models from event logs. While the Inductive Mining framework is widely adopted for ensuring model soundness, its strict block-structured nature often fails to capture the true complexity of real-world control flows. Recent advancements, such as the Partially Ordered Workflow Language (POWL), have relaxed these constraints for concurrency, yet a significant gap remains in effectively modeling complex decision logic and unstructured loops. We bridge this gap by introducing POWL 2.0, an extended modeling language that integrates choice graphs to represent non-block-structured decisions and cyclic flows within a hierarchical framework. In this paper, we present a robust inductive discovery algorithm that leverages POWL 2.0, and we explore several mining strategies for choice graphs. These strategies range in strictness to resolve the ambiguity between genuine loops and hidden concurrency. Our experimental evaluation demonstrates that the proposed approach captures complex behaviors without compromising the scalability and quality guarantees of the Inductive Mining framework.

Large Reasoning Models (LRMs), a subset of Large Language Models (LLMs) trained to articulate their chain-of-thought, have shown promise in tackling complex scientific tasks. However, evaluating and configuring their reasoning processes remains underexplored. This paper leverages a process mining-specific LLM evaluation framework to propose a methodology for analyzing and configuring LRMs. We introduce an approach to extract and classify reasoning steps by type (e.g., Deductive Reasoning, or Hypothesis Generation) and effect (Positive, Indifferent, Negative) on the overall reasoning, enabling a detailed assessment of reasoning quality. From this, we derive a new benchmark, PMLRM-Bench, which evaluates not only the correctness of outputs but also the robustness of the reasoning process. A case study on the QwQ-32B LLM demonstrates how targeted adjustments to reasoning type frequencies can boost task-specific performance. Our results reveal distinct reasoning patterns across models and provide actionable insights for LRM configuration. This work bridges process mining and LLM evaluation, offering a scalable framework for reasoning analysis.

Process modeling is a sub-domain of Business Process Management (BPM) focused on the translation of process artifacts into formal models. This task traditionally requires extensive human input and domain expertise in both BPM notations and the specific business context. While Large Language Models (LLMs) can now automate much of this manual work, current text-to-model approaches focus predominantly on the control-flow perspective-ordering activities without considering the collaborative aspect of the processes. In this paper, we introduce a resource-aware generation pipeline that produces formal BPMN 2.0 collaboration diagrams from natural-language descriptions. Rather than solely prompting an LLM for raw XML, we describe a compact, executable intermediate language with mandatory resource details defining both the organization (pool) and the role (lane). Cross-organization dependencies are materialized using the standard formal notation for such interactions-message events-while an orthogonal layout routine automatically handles the spatial arrangement of elements within pools and lanes. Experiments on ten business processes with nine LLMs show strong resource discovery while preserving control-flow quality and adding only marginal runtime overhead. This approach moves generative modeling toward a more comprehensive, multi-collaborative representation of business operations.

In business processes, an operational problem refers to a deviation and an inefficiency that prohibits an organization from reaching its goals, e.g., a delay in approving a purchase order in a Procure-To-Pay (P2P) process. Operational process monitoring aims to assess the occurrence of such operational problems by analyzing event data that record the execution of business processes. Once the problems are detected, organizations can act upon the corresponding problems with viable actions, e.g., adding more resources, bypassing problematic activities, etc. A plethora of approaches have been proposed to implement operational process monitoring. The lion's share of existing approaches assumes that a single case notion (e.g., a purchase order in a P2P process) exists in a business process and analyzes operational problems defined over the single case notion. However, most real-life business processes manifest the interplay of multiple interrelated objects. For instance, an execution of an omnipresent P2P process involves multiple objects of different types, e.g., purchase orders, goods receipts, invoices, etc. Applying the existing approaches in these object-centric business processes results in inaccurate or misleading results. In this study, we propose a novel approach to assessing operational problems within object-centric business processes. Our approach not only ensures an accurate assessment of existing problems but also facilitates the analysis of object-centric problems that consider the interaction among different objects. We evaluate this approach by applying it to both simulated business processes and real-life business processes.

The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. However, its adoption in practice is hindered by the prevalence of standard notations like workflow nets (WF-nets) and BPMN. This paper presents a novel algorithm for transforming safe and sound WF-net into equivalent POWL models. The algorithm recursively identifies structural patterns within the WF-net and translates them into their POWL representation. We formally prove the correctness of our approach, showing that the generated POWL model preserves the language of the input WF-net. Furthermore, we demonstrate the high scalability of our algorithm, and we show its completeness on a subclass of WF-nets that encompasses equivalent representations for all POWL models. This work bridges the gap between the theoretical advantages of POWL and the practical need for compatibility with established notations, paving the way for broader adoption of POWL in process analysis and improvement applications.

Process discovery algorithms traditionally linearize events, failing to capture the inherent concurrency of real-world processes. While some techniques can handle partially ordered data, they often struggle with scalability on large event logs. We introduce a novel, scalable algorithm that directly leverages partial orders in process discovery. Our approach derives partially ordered traces from event data and aggregates them into a sound-by-construction, perfectly fitting process model. Our hierarchical algorithm preserves inherent concurrency while systematically abstracting exclusive choices and loop patterns, enhancing model compactness and precision. We have implemented our technique and demonstrated its applicability on complex real-life event logs. Our work contributes a scalable solution for a more faithful representation of process behavior, especially when concurrency is prevalent in event data.

Model abstraction (MA) and event abstraction (EA) are means to reduce complexity of (discovered) models and event data. Imagine a process intelligence project that aims to analyze a model discovered from event data which is further abstracted, possibly multiple times, to reach optimality goals, e.g., reducing model size. So far, after discovering the model, there is no technique that enables the synchronized abstraction of the underlying event log. This results in loosing the grounding in the real-world behavior contained in the log and, in turn, restricts analysis insights. Hence, in this work, we provide the formal basis for synchronized model and event abstraction, i.e., we prove that abstracting a process model by MA and discovering a process model from an abstracted event log yields an equivalent process model. We prove the feasibility of our approach based on behavioral profile abstraction as non-order preserving MA technique, resulting in a novel EA technique.

Process discovery aims to automatically derive process models from event logs, enabling organizations to analyze and improve their operational processes. Inductive mining algorithms, while prioritizing soundness and efficiency through hierarchical modeling languages, often impose a strict block-structured representation. This limits their ability to accurately capture the complexities of real-world processes. While recent advancements like the Partially Ordered Workflow Language (POWL) have addressed the block-structure limitation for concurrency, a significant gap remains in effectively modeling non-block-structured decision points. In this paper, we bridge this gap by proposing an extension of POWL to handle non-block-structured decisions through the introduction of choice graphs. Choice graphs offer a structured yet flexible approach to model complex decision logic within the hierarchical framework of POWL. We present an inductive mining discovery algorithm that uses our extension and preserves the quality guarantees of the inductive mining framework. Our experimental evaluation demonstrates that the discovered models, enriched with choice graphs, more precisely represent the complex decision-making behavior found in real-world processes, without compromising the high scalability inherent in inductive mining techniques.

Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discover simulation models from historical event logs, reducing the cost and time to manually design them. However, in dynamic business environments, organizations continuously refine their processes to enhance efficiency, reduce costs, and improve customer satisfaction. Existing techniques to process simulation discovery lack adaptability to real-time operational changes. In this paper, we propose a streaming process simulation discovery technique that integrates Incremental Process Discovery with Online Machine Learning methods. This technique prioritizes recent data while preserving historical information, ensuring adaptation to evolving process dynamics. Experiments conducted on four different event logs demonstrate the importance in simulation of giving more weight to recent data while retaining historical knowledge. Our technique not only produces more stable simulations but also exhibits robustness in handling concept drift, as highlighted in one of the use cases.