In process mining, alignments quantify the degree of deviation between an observed event trace and a business process model and constitute the most important conformance checking technique. We study the algorithmic complexity of computing alignments over important classes of Petri nets. First, we show that the alignment problem is PSPACE-complete on the class of safe Petri nets and also on the class of safe and sound workflow nets. For live, bounded, free-choice systems, we prove the existence of optimal alignments of polynomial length which positions the alignment problem in NP for this class. We further show that computing alignments is NP-complete even on basic subclasses such as process trees and T-systems. We establish NP-completeness on several related classes as well, including acyclic systems. Finally, we demonstrate that on live, safe S-systems the alignment problem is solvable in P and that both assumptions (liveness and safeness) are crucial for this result.

Object-centric process mining enables the analysis of complex business processes involving multiple interacting objects, such as in inventory management or e-business workflows. However, long object lifecycles often result in convoluted event logs, obscuring actionable insights and challenging process mining techniques. This paper introduces a segmentation framework that decomposes object-centric event logs into focused, analytically tractable units called segments. We propose segmentation strategies based on object relationships, event types, and temporal intervals, and integrate them with machine learning techniques for anomaly detection and correlation analysis. Implemented in the OC-PM tool, our approach empowers users to uncover localized patterns and optimize process execution. A case study on inventory management demonstrates how segmentation reveals insights into understock and overstock issues, enhancing decision-making in data-intensive domains.

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.

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.

Gaujal, Haar, and Mairesse proved in 2003 that every cluster in a live, bounded, free-choice Petri net system (i.e., transitions that share the same set of input places) has a blocking marking, i.e., a marking which enables only the transitions of the cluster. Moreover, for each cluster, this blocking marking is unique and reachable from every state of the system. In this paper, we study live, bounded, free-choice systems that, in addition, possess a home cluster, i.e., a cluster whose blocking marking only marks the places of the cluster. Prototypical examples are sound free-choice workflow nets, a commonly used subset of the standard model class in process mining. Our main result shows that in such systems, shortest firing sequences to blocking markings have linear length. Moreover, we obtain a quadratic upper bound on the length of shortest firing sequences between arbitrary markings once a home cluster is present. Both results strongly improve known bounds, as the best currently known bound is the cubic one of Desel and Esparza for general live, bounded, free-choice systems. Furthermore, we show that each transition can be enabled via an elementary firing sequence, i.e., a firing sequence which contains at most one transition per cluster. These insights might be of independent interest for future research on free-choice systems.

Modern organizations manage complex processes involving multiple object types, event types, and dynamic attributes, such as stock levels, patient vital signs, or machine status, which define critical object states (e.g., Understock, Patient at Risk, Machine Down). While object-centric process mining (OCPM) with OCEL 2.0 captures these attributes, it does not systematically model state transitions, limiting insights into process dynamics. We propose State-Aware Object-Centric Process Mining (SA-OCPM), an extension of OCEL 2.0 that introduces (1) object state transition events to log changes (e.g., Normal to Understock, Patient at Risk to Stable, Machine Down to Running) and (2) object state-aware events to refine events with state context (e.g., Goods Receipt (Understock), Patient Admission (Patient at Risk), Maintenance Start (Machine Down)). Implemented in a commercial platform, SA-OCPM enables precise analysis of when, why, and how processes deviate from the optimum, as demonstrated in a logistics case study revealing inefficiencies like prolonged understock. SA-OCPM’s state-based approach enhances diagnostic granularity and is applicable to domains like healthcare, manufacturing, and customer relationship management.

Object-centric process mining (OCPM) is gaining traction in both academia and industry due to its ability to model real-world processes more accurately than traditional case-centric approaches. By considering multiple interacting objects, object-centric Petri nets (OCPNs) offer a richer process representation, but this also introduces unique challenges for visualization. The presence of multiple object types, variable arcs, and complex interactions complicates the creation of clear and interpretable layouts. In this paper, we address these challenges by presenting a dedicated layout algorithm tailored to the structural characteristics of OCPNs. Inspired by the Sugiyama framework, the algorithm balances aesthetic and functional criteria, guided by a set of domain-specific quality metrics. We implemented our approach in an open-source web-based tool, OCPN Visualizer, and a reusable JavaScript library for integration into third-party applications. A user study confirms the practical relevance of our approach and highlights its effectiveness in improving the interpretability of object-centric process visualizations.

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.

This contribution revisits our article titled “A General Process Mining Framework for Correlating, Predicting, and Clustering Dynamic Behavior Based on Event Logs”, published in the Information Systems journal in 2016. It reflects on how the proposed general framework for process mining has grown in relevance with the rise of AI, emphasizing its value as a extensible approach to transforming event data into analytical and predictive insights. It also discusses how the framework relevance and the underlying message remains valid, including for emerging research directions such as prescriptive analytics, causal and/or object-centric process mining.

As information systems grow in complexity, standard process analysis frequently encounters computational bottlenecks. While decomposition offers a scalable alternative, its utility depends on the structural and behavioral properties of the fragments. We identify three prerequisites for reliable decomposition: property preservation (e.g., soundness), valid result aggregation, and hierarchical abstraction. This paper presents IsoNet, a tool implementing a decomposition strategy based on subnets that are independent of the rest of the net, apart from designated entry/exit points. IsoNet automatically identifies fragments that can be independently transformed into workflow nets (WF-net) that inherit soundness and free-choiceness properties. By maintaining consistent WF-net semantics both internally and externally, the tool supports a natural zoom-in/zoom-out workflow that simplifies complicated processes while maintaining a connection to the global model.

A central decision problem in Petri net theory is reachability asking whether a given marking can be reached from the initial marking. Related is the covering problem (or sub-marking reachbility), which decides whether there is a reachable marking covering at least the tokens in the given marking. For live and bounded free-choice nets as well as for sound free-choice workflow nets, both problems are polynomial in their computational complexity. This paper refines this complexity for the class of sound acyclic free-choice workflow nets to a quadratic polynomial, more specifically to O(P^2 + T^2). Furthermore, this paper shows the feasibility of accurately explaining why a given marking is or is not reachable. This can be achieved by three new concepts: admissibility, maximum admissibility, and diverging transitions. Admissibility requires that all places in a given marking are pairwise concurrent. Maximum admissibility states that adding a marked place to an admissible marking would make it inadmissible. A diverging transition is a transition which originally "produces" the concurrent tokens that lead to a given marking. In this paper, we provide algorithms for all these concepts and explain their computation in detail by basing them on the concepts of concurrency and post-dominance frontiers - a well known concept from compiler construction. In doing this, we present straight-forward implementations for solving (sub-marking) reachability.

Process mining is a discipline focused on extracting valuable insights about business processes from event logs collected within information systems. A key component of process mining is visualizing process models with the aim to transform complex processes into clear and intuitive graphical representations. Such visualizations create transparency and can be overlaid with conformance and performance diagnostics. Recently, a stable graph layout algorithm has been introduced to enhance model readability by preserving the process backbone—the sequence of activities that best represents the event log. The backbone-based process layout offers a key advantage: even when filters are applied, the backbone remains intact. As a result, node positions change minimally compared to other layout methods, making it easier for users to locate relevant nodes and edges in the filtered view. Despite these benefits, several challenges remain that hinder process comprehension. In this paper, we identified key issues in existing layout methods, including unnecessary back edges, restricted horizontal alignment, non-orthogonal backbone structures, and greedy-based node placement. To address these limitations, we propose an enhanced process layout generation method that integrates integer programming with heuristic techniques. We evaluate our method using real-world event logs, showing notable reductions in back edges, edge crossings, and edge lengths, along with improvements in node orthogonality and layout symmetry. A qualitative user study further confirms a strong preference for our layout over the existing benchmark method, demonstrating its effectiveness in improving process model comprehension.

Process discovery is one of the central challenges in process mining. Petri nets are particularly attractive because simple local constructs can express complex behavior, including concurrency. While their global behavior may be difficult to analyze, individual places can be efficiently characterized using monotonic properties, enabling bottom-up discovery. Unlike top-down approaches such as the Inductive Miner, which rely on predefined constructs for sequences, choices, loops, and concurrency, our approach allows such structures to emerge organically and can exploit the full expressive power of Petri nets, including free-choice constructs and long-term dependencies. The main challenge is the exponential number of candidate places and their combinations. We present the SPECpp framework which implements strategies to obtain high-quality models under time and resource constraints. SPECpp supports rapid experimentation and is used to evaluate these strategies using both synthetic and real-life event data.

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.

Process mining relies on event logs extracted from information systems. Manual work on the factory shop floor leaves few digital traces and therefore remains largely outside its reach. Egocentric video, recorded by head-mounted cameras, captures this work in detail, but raw footage is not event data. This paper presents a fully automated pipeline that derives standard event logs from egocentric factory videos using foundation models. The pipeline transcribes each video into a timestamped textual narration, induces a controlled vocabulary of processes and activities for each factory, and converts every transcription into a sequence of events constrained to this vocabulary. Applied to six factories of the Egocentric-10K corpus, it produces event logs covering more than one hundred hours of manual work without task-specific training or human annotation. We evaluate the resulting logs in two steps. A validity assessment confirms that the extracted events are structurally sound and faithful to the transcribed footage. An analysis-oriented assessment then investigates which questions the logs can answer in practice: how worker time is distributed over value-adding work, material handling, transport, and rework; how often value-adding work is interrupted; and how workers performing the same task compare. In several cases, the logs surface improvement opportunities that qualitative reporting missed, while also revealing that the induced vocabulary determines what can and cannot be measured. Pipeline, prompts, and all derived artifacts are publicly available.

In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become more complex. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the comprehension of complex process models. We present different methods for abstracting business process models into a format accessible to LLMs, and we implement advanced prompting strategies specifically designed to optimize LLM performance within our framework. Additionally, we present a tool, AIPA, that implements our proposed framework and allows for conversational process querying. We evaluate our framework and tool through: i) an automatic evaluation comparing different LLMs, model abstractions, and prompting strategies; ii) a qualitative analysis assessing the ability to identify critical quality issues in process models; and iii) a user study designed to assess AIPA's effectiveness comprehensively. Results demonstrate our framework's ability to improve the comprehension and understanding of process models, pioneering new pathways for integrating AI technologies into the BPM field.