With the recent success of large language models (LLMs), the idea of AI-augmented Business Process Management systems is becoming more feasible. One of their essential characteristics is the ability to be conversationally actionable, allowing humans to interact with the LLM effectively to perform crucial process life cycle tasks such as process model design and redesign. However, most current research focuses on single-prompt execution and evaluation of results, rather than on continuous interaction between the user and the LLM. In this work, we aim to explore the feasibility of using LLMs to empower domain experts in the creation and redesign of process models in an iterative and effective way. The proposed conversational process model redesign (CPMR) approach receives as input a process model and a redesign request by the user in natural language. Instead of just letting the LLM make changes, the LLM is employed to (a) identify process change patterns from literature, (b) re-phrase the change request to be aligned with an expected wording for the identified pattern (i.e. the meaning), and then to (c) apply the meaning of the change to the process model. This multi-step approach allows for explainable and reproducible changes. In order to ensure the feasibility of the CPMR approach, and to find out how well the patterns from literature can be handled by the LLM, we perform an extensive evaluation, also in comparison to a baseline approach without change patterns. The results show that some patterns are hard to understand by LLMs and by users and that clear change descriptions by users are essential. Overall, we recommend a hybrid approach that identifies all used change patterns and then directly applies those patterns that work correctly and for the others derives follow-up questions in order to improve user input.

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.

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.

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.

Predictive Process Monitoring (PPM) aims to forecast the future behavior of an ongoing process instance based on historical event logs. PPM approaches which follow an offline training and online prediction paradigm, often struggle to adapt to the evolving nature of real-world processes. While state-of-theart approaches address such challenges by continuously updating or retraining models, they typically overlook dynamic processes, where numerous unseen activities emerge or the order between activities evolves over time. To address this gap, we propose a Class Incremental Learning with Drift Detection and Data Augmentation (CIL2D) framework for next activity prediction in dynamic process environments. CIL2D employs a representationbased drift detection mechanism to identify shifted, novel, or unseen activities. To enhance model generalization to drifting patterns, CIL2D applies data augmentation in feature space to generate diverse and novel feature representations that are unseen but likely to occur as processes evolve. Upon drift detection, the model is incrementally updated to the newly observed data in combination with augmented samples and replay buffer traces. Experiments on real-life event logs demonstrate that CIL2D consistently outperforms existing methods in prediction accuracy and adaptation efficiency, highlighting its effectiveness for next activity prediction in highly dynamic process environments.

Suffix prediction of business processes forecasts the remaining sequence of events until process completion. Current approaches focus on predicting the most likely suffix, representing a single scenario. However, when the future course of a process is highly uncertain and variable, a single scenario may have limited predictive value. To address this limitation, we propose probabilistic suffix prediction, a novel approach that returns a set of sampled suffixes. The method is based on an uncertainty-aware encoder-decoder LSTM combined with a Monte Carlo suffix sampling algorithm. We capture epistemic uncertainty via MC dropout and aleatoric uncertainty as learned loss attenuation. Comparisons with two other uncertainty-aware PPM approaches across four datasets demonstrate that our probabilistic suffix prediction approach achieves reasonable predictive performance while it allows estimating prediction intervals for multiple objectives within a single model.

Allocating resources to process tasks during runtime (online) is hard. A solution method for such allocation is required to be computationally efficient while being subjected to uncertainties such as resources suddenly becoming (un)available. Resource allocation problems where task processing times differ across resources can be formalized as an assignment or parallel machines scheduling problem. This work presents adaptations to both problem formulations to address resource (un)availabilities. These adaptations require a prediction model to estimate the processing time of a task for all of its authorized resources. We evaluate and compare the proposed adaptations with existing allocation approaches on two process simulation models created from an artificial and a real-life event log. Our results show that both approaches can outperform traditional allocation strategies, such as the shortest queue, random, round-robin, and batch-allocation approaches.

The current push towards interoperability drives companies to collaborate through process choreographies. At the same time, they face a jungle of continuously changing regulations, e.g., due to the pandemic and developments such as the BREXIT, which strongly affect cross-organizational collaborations. Think of, for example, supply chains spanning several countries with different and maybe even conflicting COVID19 traveling restrictions. Hence, providing automatic compliance verification in process choreographies is crucial for any cross-organizational business process. A particular challenge concerns the restricted visibility of the partner processes at the presence of global compliance rules (GCR), i.e., rules that span across the process of several partners. This work deals with the question how to verify global compliance if affected tasks are not fully visible. Our idea is to decompose GCRs into so called assertions that can be checked by each affected partner whereby the decomposition is both correct and lossless. The algorithm exploits transitivity properties of the underlying rule specification, and its correctness and complexity are proven, considering advanced aspects such as loops. The algorithm is implemented in a proof-of-concept prototype, including a model checker for verifying compliance. The applicability of the approach is further demonstrated on a real-world manufacturing use case.

Enterprise Application Integration (EAI) constitutes the cornerstone in enterprise IT landscapes that are characterized by heterogeneity and distribution. Starting from established Enterprise Integration Patterns (EIPs) such as Content-based Router and Aggregator, EIP compositions are built to describe, implement, and execute integration scenarios. The EIPs and their compositions must be correct at design and runtime in order to avoid functional errors or incomplete functionalities. However, current EAI system vendors use many of the EIPs as part of their proprietary integration scenario modeling languages that are not grounded on any formalism. This renders correctness guarantees for EIPs and their composition impossible. Thus this work advocates responsible EAI based on the formalization, implementation, and correctness of EIPs. For this, requirements on an EIP formalization are collected and based on these requirements an extension of db-net, i.e., timed db-net , is proposed, fully equipped with execution semantics. It is shown how EIPs can be realized based on timed db-nets and how the correctness of these realizations can be shown. Moreover, the simulation of EIP realizations based on timed db-nets is enabled which is essential for later implementation. The concepts are evaluated in many ways, including a proof-of-concept implementation and case studies. The EIP formalization based on timed db-nets constitutes the first step towards responsible EAI.

Ensuring the smooth and efficient execution of business processes requires a continuous process quality assessment and optimization. Process optimization exploits process attributes and their values, e.g. cost or duration of a process task, in order to derive, for example, control flow adaptations. The complexity of the analysis can range from a few to a plethora of attributes, e.g. machining times and sensor parameters in the manufacturing domain. Hence, it is crucial to support single and multiple users (e.g. process analysts) in visually exploring process attributes and their values. Specifically, comparing and assessing different visualization and interaction approaches with respect to their efficiency is required in order to offer the most suitable approach to users for a specific analysis task. However, such assessments are currently missing. To close this gap, this paper assesses three visualization and interaction approaches, i.e. 2D, 3D, and 3D with virtual reality (VR) support, based on a controlled experiment. We choose the process modeling standard Business Process Modeling and Notation (BPMN) as 2D approach. 3DViz is introduced as 3D approach with focus on attribute representation, and additionally augmented with VR. A statistically significant difference between the approaches can be observed with respect to their efficiency.

Detecting anomalies in process runtime behavior is crucial: they might reflect, on the one side, security breaches and fraudulent behavior and on the other side desired deviations due to, for example, exceptional conditions. Both scenarios yield valuable insights for process analysts and owners, but happen due to different reasons and require a different treatment. Hence a distinction into malign and benign anomalies is required. Existing anomaly detection approaches typically fall short in supporting experts when in need to take this decision. An additional problem are false positives which could result in selecting incorrect countermeasures. This paper proposes a novel anomaly detection approach based on association rule mining. It fosters the explanation of anomalies and the estimation of their severity. In addition, the approach is able to deal with process change and flexible executions which potentially lead to false positives. This facilitates to take the appropriate countermeasure for a malign anomaly and to avoid the possible termination of benign process executions. The feasibility and result quality of the approach are shown by a prototypical implementation and by analyzing real life logs with injected artificial anomalies. The explanatory power of the presented approach is evaluated through a controlled experiment with users.

Instance spanning constraints (ISC) are the instrument to establish controls across multiple instances of one or several processes. A multitude of applications crave for ISC support. Consider, for example, the bundling and unbundling of cargo across several instances of a logistics process or dependencies between examinations in different medical treatment processes. Non-compliance with ISC can lead to severe consequences and penalties, e.g., dangerous effects due to undesired drug interactions. ISC might stem from regulatory documents, extracted by domain experts. Another source for ISC are process execution logs. Process execution logs store execution information for process instances, and hence, inherently, the effects of ISC. Discovering ISC from process execution logs can support ISC design and implementation (if the ISC was not known beforehand) and the validation of the ISC during its life time. This work contributes a categorization of ISC as well as four discovery algorithms for ISC candidates from process execution logs. The discovered ISC candidates are put into context of the associated processes and can be further validated with domain experts. The algorithms are prototypically implemented and evaluated based on artificial and real-world process execution logs. The results facilitate ISC design as well as validation and hence contribute to a digitalized ISC and compliance management.

Comprehensive and continuous compliance monitoring is crucial for many process scenarios, e.g., machine-spanning production processes. However, business process execution data is often scattered over several heterogeneous event sources and needs to be seamlessly incorporated into the data structure of the compliance checking system. The proposed COMS approach offers the possibility to define business rules based on process behaviour and the ability to handle events from different information system sources in an integrated way. COMS consists of a generic event data structure to store process information, a rule language, and matching concepts. COMS concepts are prototypically implemented and evaluated based on a real-world event stream from the manufacturing domain.

Business processes describe and implement the business logic of companies, control human interaction, and invoke heterogeneous services during runtime. Therefore, ensuring the correct execution of processes is crucial. Existing work is addressing this challenge through process verification. However, the highly dynamic aspects of the current processes and the deep integration and frequent invocation of third party services limit the use of static verification approaches. Today, one frequently utilized approach to address this limitation is to apply process tests. However, the complexity of process models is steadily increasing. So, more and more test cases are required to assure process model correctness and stability during design and maintenance. But executing hundreds or even thousands of process model test cases lead to excessive test suite execution times and, therefore, high costs. Hence, this paper presents novel coverage metrics along with a genetic test case selection algorithm. Both enable the incorporation of user-driven test case selection requirements and the integration of different knowledge sources. In addition, techniques for test case selection computation performance optimization are provided and evaluated. The effectiveness of the presented genetic test case selection algorithm is evaluated against five alternative test case selection algorithms.

The optimization of their business processes is a crucial challenge for many enterprises. This applies especially for organizations using complex cooperative information systems to support human work, production lines, or computing services. Optimizations can touch different aspects such as costs, throughput times, and quality. Nowadays, improvements in workflows are mostly achieved by restructuring the process model. However, in many applications there is a huge potential for optimizations during runtime as well. This holds particularly true for collaborative processes with critical activities, i.e. activities that require a high setup or changeover time, typically leading to waiting queues in instance processing. What is usually suggested in this situation is to bundle several instances in order to execute them as a batch. How the batching is achieved, however, has been only decided on static rules so far. In this paper, we feature dynamic instance queuing (DIQ) as an approach towards clustering and batching instances based on the current conditions in the process, e.g. attribute values of the instances. Specifically, we extend our previous work on applying DIQ at single activities towards a queuing approach that spans activity sequences (DIQS). The approach is evaluated based on a real-world case study from the manufacturing domain. We discuss limitations and further applications of the DIQ idea, e.g. with respect to collaborative human tasks.

Business process collaborations among multiple partners require particular considerations regarding flexibility and change management. Indeed, each change or process redesign originated by a partner may cause ripple effects on other partners participating in the choreography. Consequently, a change request could spread over partners in an unexpected way with relevant costs due to its transitivity (e.g. in supply chains). In order to avoid costly negotiations or propagation failures, understanding this behavior becomes critical. This paper focuses on analyzing the behavior of change requests in process choreographies, i.e. the change propagation behavior. The input data might be available in two different formats, i.e. as change logs or change propagation logs (CPs). In order to understand the data and to explore potential analysis models and techniques, we employ exploratory data analysis as well as analysis techniques from process mining and change management to simulation data. The results yield the requirements for designing a mining algorithm that derives the propagation behavior behind change logs. This algorithm is a memetic algorithm that is based on different heuristics. Its feasibility is shown based on a comparison with the other mining techniques.

Enabling process changes constitutes a major challenge for any process-aware information system. This not only holds for processes running within a single enterprise, but also for collaborative scenarios involving distributed and autonomous partners. In particular, if one partner adapts its private process, the change might affect the processes of the other partners as well. Accordingly, it might have to be propagated to concerned partners in a transitive way. A fundamental challenge in this context is to find ways of propagating the changes in a decentralized manner. Existing approaches are limited with respect to the change operations considered as well as their dependency on a particular process specification language. This paper presents a generic change propagation approach that is based on the Refined Process Structure Tree, i.e., the approach is independent of a specific process specification language. Further, it considers a comprehensive set of change patterns. For all these change patterns, it is shown that the provided change propagation algorithms preserve consistency and compatibility of the process choreography. Finally, a proof-of-concept prototype of a change propagation framework for process choreographies is presented. Overall, comprehensive change support in process choreographies will foster the implementation and operational support of agile collaborative process scenarios.

In recent years, monitoring the compliance of business processes with relevant regulations, constraints, and rules during runtime has evolved as major concern in literature and practice. Monitoring not only refers to continuously observing possible compliance violations, but also includes the ability to provide fine-grained feedback and to predict possible compliance violations in the future. The body of literature on business process compliance is large and approaches specifically addressing process monitoring are hard to identify. Moreover, proper means for the systematic comparison of these approaches are missing. Hence, it is unclear which approaches are suitable for particular scenarios. The goal of this paper is to define a framework for Compliance Monitoring Functionalities (CMF) that enables the systematic comparison of existing and new approaches for monitoring compliance rules over business processes during runtime. To define the scope of the framework, at first, related areas are identified and discussed. The CMFs are harvested based on a systematic literature review and five selected case studies. The appropriateness of the selection of CMFs is demonstrated in two ways: (a) a systematic comparison with pattern-based compliance approaches and (b) a classification of existing compliance monitoring approaches using the CMFs. Moreover, the application of the CMFs is showcased using three existing tools that are applied to two realistic data sets. Overall, the CMF framework provides powerful means to position existing and future compliance monitoring approaches.

The need for more flexibility of process-aware information systems (PAISs) has been discussed for several years and different approaches for adaptive process management have emerged. However, only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. Furthermore, knowledge about process changes has not yet been exploited by any of these systems. This paper presents the ProCycle approach which overcomes this practical limitation by capturing the whole process life cycle and all kinds of changes in an integrated way. Users are not only allowed to deviate from the predefined process in exceptional situations, but are also assisted in retrieving and reusing knowledge about previously performed changes in this context. If similar instance deviations occur frequently, process engineers will be supported in deriving improved process models from them. This, in turn, allows engineers to evolve the PAIS (including the knowledge about the changes) over time. Feasability of the ProCycle approach is demonstrated by a proof-of-concept prototype which combines adaptive process management technology with concepts and methods provided by case-based reasoning (CBR) technology.

Process-aware information systems have to be frequently adapted due to business process changes. One important challenge not adequately addressed so far concerns the evolution of process choreographies. If respective modifications are applied in an uncontrolled manner, inconsistencies or errors might occur in the sequel. In particular, modifications of private processes performed by a single party may affect the implementation of the private processes of partners as well. In this paper we sketch a framework that allows process engineers to detect how changes of private processes may affect related public views and - if so - how they can be propagated to the public and private processes of partners. Our approach exploits the semantics of the applied changes in order to automatically determine the adaptations necessary for the partner processes.

In the ADEPT project we have been working on the design and implementation of next generation process management software. Based on a conceptual framework for dynamic process changes, on novel process support functions, and on advanced implementation concepts, the developed system enables the realization of adaptive, process-aware information systems (PAIS). Basically, process changes can take place at the type as well as the instance level: changes of single process instances may have to be carried out in an ad-hoc manner and must not affect system robustness and consistency. Process type changes, in turn, must be quickly accomplished in order to adapt the PAIS to business process changes. ADEPT2 offers powerful concepts for modeling, analyzing, and verifying process schemes. Particularly, it ensures schema correctness, like the absence of deadlock-causing cycles or erroneous data flows. This, in turn, constitutes an important prerequisite for dynamic process changes as well. ADEPT2 supports both ad-hoc changes of single process instances and the propagation of process type changes to running instances.