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.

As artificial intelligence (AI) systems are increasingly deployed in high-stakes environments, the need for explanations that convey uncertain information has become evident. Conventional explainable AI (XAI) methods often overlook uncertainty, focusing solely on point predictions. To address this gap, we propose using permutation feature importance (PFI) combined with predictive uncertainty evaluation measures. This novel approach examines the significance of features by relating them to the model’s confidence in its predictions. By using split conformal prediction (SCP) to quantify predictive uncertainty and integrating the outcomes to PFI, we aim to enhance the robustness and interpretability of machine learning (ML) algorithms. More importantly, we examine three scenarios for conformal prediction-based PFI explanations: permuting feature values in the test data, the calibration data, and both. These scenarios assess the impact of feature permutations from different perspectives, revealing feature sensitivity and the importance of features in various settings. We also perform a series of sensitivity analyses, particularly exploring calibration data size and computational efficiency, to demonstrate the robustness and scalability of our approach for industrial applications. Our comprehensive evaluation offers insights into feature impact on predictions and their associated confidence levels. We validate our proposed approach through a real-world predictive process monitoring use case in manufacturing.

Process mining aims to derive insights into business processes from event logs recorded from information systems. Process discovery algorithms construct process models that describe the executed process. With the increasing availability of large-scale event logs, process discovery has shifted towards a data-oriented research discipline, aiming to design algorithms that are applicable and useful in practice. This shift has revealed a fundamental problem in process discovery research: Currently, contributions can only be considered in isolation. Researchers conduct experiments to show that they move the field forward, but due to a lack of reliability and validity, the individual contributions are hard to generalize. In this article, we argue that one reason for these problems is the lack of conventions or standards for experimental design in process discovery. Hence, we propose “process discovery engineering”: a research methodology for process discovery, consisting of a shared terminology and a checklist for conducting experiments. We demonstrate its applicability by means of an example experimental evaluation of process discovery algorithms and discuss the implications of the methodology on the field. This article is not meant to be prescriptive but to invite and encourage the community to contribute to this discussion to advance the field as a whole.

Abstract This paper proposes a multi‐stage approach consisting of deep learning‐based image classification, process trace clustering, and visual/statistical knowledge discovery of process data. The proposed decision augmentation solution aims to facilitate the production planners in estimating the process‐specific production parameters such as activity duration, idle time, or machine utilization. This study focuses on ‘one‐of‐a‐kind production’ (OKP). Planning in OKP is especially challenging due to the increasing individualization of customer requirements. Furthermore, the uniqueness of products adds complexity to data and information structuring. To tackle this issue, we first train deep convolutional neural networks (CNN) with image data of production parts obtained from computer‐aided design (CAD) systems to extract meaningful features. After cross‐validation, uncertainty, and robustness assessment of the adopted deep learning approach, we use the data representation from the penultimate layer as input for clustering production parts. The goodness of clustering results is evaluated using a series of internal clustering validation indices. Finally, process event log data provided by manufacturing execution systems (MES) is mapped to each production part, allowing us to conduct statistical and visual knowledge discovery of process parameters for each cluster. The relevance of our proposed approach has been validated by studying a real‐world use case in a small, medium‐sized enterprise (SME) operating in the fixture and jig manufacturing industry.

Abstract Artificial ants are “small” units, moving autonomously on a shared, dynamically changing “space”, directly or indirectly exchanging some kind of information. Artificial ants are frequently conceived as a paradigm for collective adaptive systems. In this paper, we discuss means to represent continuous moves of “ants” in discrete models. More generally, we challenge the role of the notion of “time” in artificial ant systems and models. We suggest a modeling framework that structures behavior along causal dependencies rather than temporal relations. We present all arguments with the help of a simple example. As a modeling framework we employ Heraklit; an emerging framework that has already proven its worth in many contexts. Different concrete collective systems share similar features, despite differences in the size of basic sets, concrete values of functions, etc., and can therefore be conceived as instantiations of a single schema. Hence, we need a representation of systems on the schematic level.

Cloud computing offers readily available, scalable infrastructure to tackle problems involving high data volume and velocity. Discovering processes from event streams, especially when the business processes execute in a cloud environment, is such a problem. Event stream data is generated rapidly with varying volume and must be processed on-the-fly, making stream processing an important use case for cloud computing. This paper describes a distributed, streaming implementation of the flexible heuristics miner on Amazon Kinesis, a cloud-based event stream infrastructure, showing how mining methods can scale effortlessly to tens of millions of events per minute.

Process modeling plays an increasingly important role in organizational and software design. However, there is a lack of concrete examples of process models. An open and freely available corpus covering process models from different domains and application areas would offer a great potential for research and development. Typical applications of a model corpus are: (1) Creating a consistent and coherent understanding of business application systems in different domains and industries, (2) reusing the process models in other contexts, (3) creating a homogeneous data basis for different application and analysis scenarios. Against that background, this paper aims at developing a process model corpus, which may serve as a standardized data basis for the mentioned application scenarios. In order to realize that objective, the authors propose a procedure model serving as the basis for the process model corpus. This corpus contains reference models, models from practice and models from controlled environments and, in total, comprises 16 model collections with 2,290 process models.

Every scientific discipline aims at accumulating and developing scientific knowledge in order to develop its theoretical foundations. Scientific knowledge is typically shared and communicated by means of articles published in journals, conference proceedings or books printed on paper or in electronic form. As researchers are typically not interested in articles but their specific content such as concepts, theories or facts, the full potential of electronic knowledge availability has not fully been tapped. Against this background, Bars developed a conceptual model of a general scientific knowledge infrastructure supporting a more adequate electronic provision of knowledge which is typically requested by scholars. In this contribution, the development of a scientific knowledge infrastructure which has been extended to specific needs of Information Systems research using OWL-DL is presented.

Information Systems (IS) research strives for the design of innovative as well as the investigation of existing methods and techniques for information management in organizations. IS methods and techniques can support the automation of operational tasks as well as management tasks in organizations. Furthermore, they offer considerable potential for the automation of research processes in IS research. This paper examines the potential and boundaries of automated analyses of fads and trends in the field of IS research based on a design-oriented research approach. A concept for automated trend analysis is developed and implemented in an innovative software tool. The proof of concept software is exemplarily applied for the investigation of fads and trends in Business Process Management (BPM) research as an important field in IS research. Moreover, the potential and boundaries of the presented approach are discussed.