In contrast to traditional business models, digital business ecosystems (DBEs) have several distinctive features—heterogeneity of involved actors, symbiosis in the exchange of resources, co-evolution ...

Organizations need to manage numerous business processes for delivering their services and products to customers. One important consideration thereby lies in the adherence to regulations such as laws,...

Process discovery aims to generate models that capture behaviors recorded in event logs from information systems. While many approaches exist, few ensure key properties such as soundness and free-choi...

Uncertainty and probability often play a key role in the rigorous evaluation of extra-functional requirements, such as reliability, availability, and performability, on architecture models. For that p...

Critical infrastructure systems — for which high reliability and availability are paramount — must operate securely. Attack trees (ATs) are hierarchical diagrams that offer a flexible modelling langua...

Digital twins (DT) constitute complex software systems that need to be continuously modified/updated to meet evolving user requirements and priorities, and support continual improvement. Because their...

Threat modeling based on attack graphs can be used to understand and evaluate system security, and has become an important means of enterprise system security protection. However, existing approaches ...

Verification and validation of smart contracts is crucial, as these programs administer valuable assets. Indeed, there is a proliferation of companies, specialized in smart contract audits that has be...

This paper addresses fundamental limitations in visual GUI automation by redefining current approaches as process-based and introducing a novel model-based alternative. By addressing the inherent stoc...

Endless Runner games are a popular genre of games known for their fast-paced, continuously generated content. Due to their dynamic and non-deterministic nature, these games present unique challenges f...

Much research has been done on the comprehension and development of visual business process models. In related areas such as linguistics and software engineering, researchers have used techniques from neuroscience to study the physiological and neurological processes when working with text in tasks such as program code debugging and natural language understanding. Such techniques have only to a limited degree been used to improve our understanding of visual conceptual models. In this paper, we will present ongoing research on using techniques for collecting biometric data to investigate how we work with visual conceptual models. We will provide results from an experiment collecting biometric data from 57 people as they respond to comprehension questions in connection with process models. The approach, based on techniques used in multi-modal learning analytics (MMLA), investigates how performance on modelling tasks is correlated with biometric data, collecting data in parallel from EEG, eye-tracking, wristbands, and facial expression (through cameras). We find that improved understanding of the performance of modelling tasks can be achieved by using biometric data in a close-to-natural usage situation. Results from the experiment can also be the basis for providing a neuro-adaptive modelling tool. We have just started initial work on this topic, and we present the start of a larger research program in this area in the concluding remarks.

Processes often exhibit significant variability, posing challenges for process discovery and insight extraction. While most studies focus on detecting variability over time (e.g., concept drift), control-flow variability can also manifest across other dimensions, such as case durations or performance metrics. Identifying and understanding these changes is vital for uncovering inefficiencies and undesired behaviors. This paper introduces a novel framework that combines control-flow change detection across performance dimensions with explainability, providing insights into where and how control flow evolves. The framework uses a sliding window approach with the earth mover’s distance to detect behavioral shifts. To enhance interpretability, event logs are encoded into a feature space defined by declarative constraints, capturing intuitive control-flow properties. Clustering these features reveals distinct behavioral patterns and their evolution along performance dimensions, linking detected changes to specific process dynamics. We validate the framework using three real-life event logs, including one from the UWV employee insurance agency in the Netherlands, demonstrating its ability to uncover meaningful changes, explain process variability, and support data-driven decision-making. The framework is implemented as an open-source tool for broader applicability.

Jjodel is a cloud-based reflective platform designed to address the challenges of Model-Driven Engineering (MDE), particularly the cognitive complexity and usability barriers often encountered in existing model-driven tools. This article presents the motivation and requirements behind the design of Jjodel and demonstrates how it satisfies these through its key features. By offering a low-code environment with modular viewpoints for syntax, validation, and semantics, Jjodel empowers language designers to define and refine domain-specific languages (DSLs) with ease. Its innovative capabilities, such as live co-evolution support, and syntax customization, ensure adaptability for academic and industrial contexts. A practical case study of an algebraic expression language highlights the ability of Jjodel to manage positional semantics and event-driven workflows, illustrating its effectiveness in simplifying complex modeling scenarios. Built on modern front-end technologies, Jjodel aims to operationalize concepts from MDE research into a usable platform that supports a range of modeling tasks.

This paper introduces bigER2CDS, a novel model-driven engineering approach and tool support for SAP Core Data Services (CDS). bigER2CDS addresses the need for a higher abstraction level in CDS develop...

Large language models (LLMs) are rapidly transforming various fields, including the field of business process management (BPM). LLMs provide new ways for analyzing and improving operational processes....

Purpose: Conceptual modeling is crucial for information systems development, yet novice modelers often struggle with ensuring a high semantic quality, leading to flawed system designs. This study inve...