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.

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.

Process mining involves analyzing event data from business process executions to uncover valuable insights. Although obtaining meaningful results is crucial for any process mining initiative, there is still little understanding of how process analysts derive these insights. In this paper, we fill this gap by characterizing findings of process mining analysis, the processes that lead to these findings, and the role of process mining expertise in guiding these processes. To this end, we leverage empirical data from a study with process mining analysts, including user interactions from process mining tools and inference steps from think-aloud data. Our empirical insights provide a comprehensive understanding of how analysts interact with process mining tools, highlighting approaches that lead to valuable findings. The results of our analysis lay the groundwork for the design of tools and interactive visualizations that support process analysts in their analysis and reasoning processes.

Process model comprehension is essential for a variety of technical and managerial tasks. To facilitate comprehension, process models are often divided into subprocesses when they reach a certain size. However, depending on the task type this can either support or impede comprehension. To investigate this hypothesis, we conduct a comprehensive eye-tracking study, where we test two different types of comprehension tasks. These are local tasks focusing on a single subprocess, thereby benefiting from abstraction (i.e., irrelevant information is hidden), and global tasks comprising multiple subprocesses, thereby also benefiting from abstraction but impeded by fragmentation (i.e., relevant information is distributed across multiple fragments). Our subsequent analysis at task (coarse-grained) and phase (fine-grained) levels confirms the opposing effects of abstraction and fragmentation. For global tasks, we observe lower task comprehension, higher cognitive load, as well as more complex search and inference behaviors, when compared to local ones. An additional qualitative analysis of search and inference phases, based on process maps and time series, provides additional insights into the evolution of information processing and confirms the differences between the two task types. The fine-grained analysis at the phase level is based on a novel research method, allowing to clearly separate information search from information inference. We provide an extensive validation of this research method. The outcome of this work provides a more thorough understanding of the effects of fragmentation, in the context of modularized process models, at a coarse-grained level as well as at a fine-grained level, allowing for the development of task- and user-centric support, and opening up future research opportunities to further investigate information processing during process comprehension.

We propose a set of techniques to analyze final or intermediate process outcomes. The main novelties are (i) outcome flow diagrams - a visualization of a process as a sequence of global decisions and their contribution to the process outcomes, and (ii) an interactive method to find explanations for process outcomes. We demonstrate the effectiveness of our techniques in the context of an in-depth study on the Road Traffic Fine Management event log, for which we obtain so far undocumented insights.

The design and analysis of process models is a critical factor for organizational improvement across various industries. Thanks to its potential to enable common understanding and foster automation, process modeling is increasingly adopted in the healthcare sector. However, the complexity of the healthcare domain makes process modeling a challenging task, potentially explaining the modest uptake of process modeling standards like the Business Process Model and Notation (BPMN). In this paper, we identify common challenges of process modeling in healthcare, elicited from healthcare process modeling initiatives and supported by the literature. For each challenge, we present some BPMN best practices in the form of ready-to-use process fragments that guide the standard modeling of complex healthcare aspects. Also, we report the results of a first evaluation of the use and perceived usefulness of best practices conducted with junior experts in medicine and IT. We observed that the domain-specific process fragments help to capture healthcare aspects in detail and are perceived as a source of learning, turning out to be especially useful for modelers with a basic understanding of BPMN and the healthcare domain.

The creation of a process model is a process consisting of five distinct phases, i.e., problem understanding, method finding, modeling, reconciliation, and validation. To enable a fine-grained analysis of process model creation based on phases or the development of phase-specific modeling support, an automatic approach to detect phases is needed. While approaches exist to automatically detect modeling and reconciliation phases based on user interactions, the detection of phases without user interactions (i.e., problem understanding, method finding, and validation) is still a problem. Exploiting a combination of user interactions and eye tracking data, this paper presents a two-step approach that is able to automatically detect the sequence of phases a modeler is engaged in during model creation. The evaluation of our approach shows promising results both in terms of quality as well as computation time demonstrating its feasibility.

Business process (BP) models are usually defined manually by business analysts through imperative languages considering activity properties, constraints imposed on the relations between the activities as well as different performance objectives. Furthermore, allocating resources is an additional challenge since scheduling may significantly impact BP performance. Therefore, the manual specification of BP models can be very complex and time-consuming, potentially leading to non-optimized models or even errors. To overcome these problems, this work proposes the automatic generation of imperative optimized BP models from declarative specifications. The static part of these declarative specifications (i.e. control-flow and resource constraints) is expected to be useful on a long-term basis. This static part is complemented with information that is less stable and which is potentially unknown until starting the BP execution, i.e. estimates related to (1) number of process instances which are being executed within a particular timeframe, (2) activity durations, and (3) resource availabilities. Unlike conventional proposals, an imperative BP model optimizing a set of instances is created and deployed on a short-term basis. To provide for run-time flexibility the proposed approach additionally allows decisions to be deferred to run-time by using complex late-planning activities, and the imperative BP model to be dynamically adapted during run-time using replanning. To validate the proposed approach, different performance measures for a set of test models of varying complexity are analyzed. The results indicate that, despite the NP-hard complexity of the problems, a satisfactory number of suitable solutions can be produced.

Concept location is a key activity during software modernization since it allows maintainers to exactly determine what pieces of source code support a specific concept. Real-world business processes and information systems providing operational IT support for respective processes can be misaligned as a consequence of uncontrolled maintenance over time. When concepts supported by an information system are getting outdated or misaligned, concept location becomes a time-consuming and error-prone task. Moreover, enterprise information systems (which implement business processes) embed significant business knowledge over time that is neither present nor documented anywhere else. To support the evolution of existing information systems, the embedded knowledge must first be retrieved and depicted in up-to-date business process models and then be mapped to the source code. This paper addresses this issue through a concept location approach that considers business activities as the key concept to be located and discovers different partial business process views for each piece of source code. Thus, the concept location problem becomes the problem of extracting such views. This approach follows model-driven development principles and an automatic model transformation is implemented to facilitate its adoption. Moreover, a case study involving two real-life information system demonstrates its feasibility.

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.