This article aims to foster a collaborative environment between the visual analytics and process mining communities by bringing together analysis methods, techniques, and tools from the process mining and visual analytics domains to devise a new knowledge-assisted, human-in-the-loop approach to process mining. Building on recent advances in methods emphasizing the role of human knowledge in analysis, we introduce knowledge-assisted interactive visual process mining (KAVA-PM) as a framework where analysts’ tacit knowledge and the externalizations of this knowledge play a key role. To achieve this, we extend an established conceptual model of KAVA that combines interactive visualizations and automated methods to support a richer process mining analysis practice that has human experts and their knowledge at its core. The paper outlines the key components of KAVA-PM as a conceptual model and its relations, proposes key analytical patterns, and demonstrates the use and validity of the patterns through usage scenarios. We then present challenges and open problems which we validate through a survey with experts. We anticipate that along with the conceptual model, these challenges will bring the VA and PM communities together along a shared research agenda where the role of humans and their knowledge is better established. • Conceptual model adapted to process mining for distinguishing between tacit and explicit knowledge. • Key research challenges validated by the visual analytics and process mining communities through an international survey.

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.

Operational processes often exhibit concurrency, where the execution of activities can overlap in time. Moreover, repetitions of activities, both intentional (e.g., iterative tasks) and unintentional (e.g., rework) often occur. Existing process mining techniques and visualizations largely assume sequential event data, making it difficult to analyze repetitions in partially ordered event data, which better captures real-world process behavior. We address this gap by introducing a novel arc-diagram-based visualization that highlights recurring activity patterns within individual process execution variants. This approach allows analysts to intuitively detect repetitions that are otherwise obscured in raw data or traditional variant views. We validate the usefulness and ease of use of the proposed visualization through a user study with process mining experts and provide an implementation of our contribution in an open-source tool, supporting practical adoption.

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 mining enables organizations to gain datadriven insights into their business processes. An increasing number of organizations are either launching process mining initiatives or expanding the current scope and areas of application. Despite the growing adoption of process mining among practitioners, several challenges remain, such as the lack of clear value propositions. While recent studies have examined factors influencing value identification, little attention has been given to what affects individual business users in generating insights from process mining. As value creation is driven by the business user of process mining output, it is essential that they are able to make sense and interpret its outputs into actionable insights. In this paper, we present the results of an interview study with process mining business users. Based on the interview data, we derive the factors that influence the use of process mining outputs in order to obtain insights. We report on the factors grouped into three main categories: (1) process mining knowledge, (2) tooling and visualization, and (3) business and domain knowledge. We then discuss practical implications of these factors for practitioners, highlighting both PM output design-related considerations and contextual factors, such as user training and clearly defined analysis goals, that influence how process mining outputs are interpreted and used.

The execution of operational processes generates event data stored in enterprise information systems. Process mining techniques analyze such event data to obtain insights vital for decision-makers to improve the reviewed process. In this context, event data visualizations are essential. We focus on visualizing variants describing process executions that are control flow equivalent. Such variants are an integral concept for process mining and are used, e.g., for data exploration and filtering. We propose high-level and low-level variants covering different levels of abstraction and present corresponding visualizations. Compared to existing variant visualizations, we support partially ordered event data and allow for heterogeneous temporal information per event, i.e., we support both time intervals and time points. We evaluate our contributions using automated experiments showing practical applicability to real-life event data. Finally, we present a user study indicating significantly improved usefulness and ease of use of the proposed high-level variant visualization compared to existing variant visualizations for typical analysis tasks.

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.

In the field of Business Process Management (BPM), modeling business processes and related data is a critical issue since process activities need to manage data stored in databases. The connection between processes and data is usually handled at the implementation level, even if modeling both processes and data at the conceptual level should help designers in improving business process models and identifying requirements for implementation. Especially in data- and decision-intensive contexts, business process activities need to access data stored both in databases and data warehouses. In this paper, we complete our approach for defining a novel conceptual view that bridges process activities and data. The proposed approach allows the designer to model the connection between business processes and database models and define the operations to perform, providing interesting insights on the overall connected perspective and hints for identifying activities that are crucial for decision support.