Keywords: Multimodal data analysis, Mixed Reality, Process Mining
Astract: This paper discusses an end-to-end methodology for real-time surgical conformance checking that uses multimodal process mining, mixed reality (MR), and large language model (LLM) prompting. Our approach aims to support surgeons and medical teams by comparing as-is operational data—captured through a variety of sensors including MR-based gaze tracking—with a reference surgical process model encoded in Business Process Modeling Notation (BPMN). We illustrate how shallow and deep human-in-the-loop feedback mechanisms can be integrated with chain-of-thought prompting to provide relevant, context-aware, and iterative feedback during surgery. We further indicate which aspects of the surgery can be monitored (and hence queried) by our multimodal process mining engine. By enabling precise, actionable feedback during critical surgical procedures, our approach enhances the ability to identify deviations, ensure adherence to best practices, and reduce human error. Ultimately, this methodology empowers surgical teams to make data-driven adjustments, promotes better patient outcomes, and allows hospitals to monitor surgical conformance effectively, setting a new standard for process-driven healthcare assistance.

Keywords: Artificial Intelligence, Event Log Completion, Event Log Creation, Event Log Quality Improvement, Multimodal data
Astract: Process mining uses data from event logs to understand which activities were undertaken, their timing, and the involved entities, providing a data trail for process analysis and improvement. However, a significant challenge involves ensuring that these logs accurately reflect the actual processes. Some processes leave few digital traces, and their event logs often lack details about manual and physical work that does not involve computers or simple sensors. We introduce the Business-knowledge Integration Cycles (BICycle) method and mm_proc_miner tool to convert raw and unstructured data from various modalities, such as video, audio, and sensor data, into a structured and unified event log, while keeping human-in-the-loop. Our method analyzes the semantic distance between visible, audible, and textual evidence within a self-hosted joint embedding space. Our approach is designed to consider (1) preserving the privacy of evidence data, (2) achieving real-time performance and scalability, and (3) preventing AI hallucinations. We also publish a dataset consisting of over 2K processes with 16K steps to facilitate domain inference-related tasks. For the evaluation, we created a novel test dataset in the domain of DNA home kit testing, for which we can guarantee that it was not encountered during the training of the employed AI foundational models. We show positive insights in both event log enrichment with multimodal evidence and human-in-the-loop contribution.

Keywords: Conceptual Modeling, Enterprise Modeling, Systems analysis and design
Astract: The Exploring Modeling Methods for Systems Analysis and Development (EMMSAD) conference series organized 29 events from 1996 to 2024, associated with Conference on Advanced Information Systems Engineering. In 2009, EMMSAD became a two-day working conference. Since 2017, the authors of EMMSAD’s best papers are invited to submit extended versions of their paper, for consideration to be published in the Journal of Software and Systems Modeling. The main topics of the EMMSAD series focus on models and modeling methods for the analysis and development of software information systems of any kind. These are organized into five tracks: (1) Foundations of Modeling and Method Engineering; (2) Enterprise, Business, Process, and Capability Modeling; (3) Information Systems and Requirements Modeling; (4) Domain-Specific and Knowledge Modeling; and (5) Evaluation of Models and Modeling Approaches. The aims, topics, and history of EMMSAD can be also found on its website at http://www.emmsad.org/.

Keywords: Abstract syntax, DSL, EMF, Graphical concrete syntax, M3B, MDSE, Metamodeling, Model transformation, MSDKVS, Sirius
Astract: Many powerful metamodeling platforms enabling model-driven software engineering (MDSE) exist, each with its strengths, weaknesses, functionalities, programming language(s), and developer community. Platform interoperability would enable users to exploit their mutual benefits. Such interoperability would allow the transformation of metamodels and models created in one platform into equivalent metamodels and models in other platforms. Language engineers could then freely choose the metamodeling platform without risking a lock-in effect. Two well-documented and publicly available metamodeling platforms are the eclipse modeling framework (EMF) and the modeling SDK for visual studio (MSDKVS). In this paper, we propose an M3-level-bridge (M3B) that establishes interoperability between EMF and MSDKVS on the abstract syntax level and on the graphical concrete syntax level. To establish such interoperability we (i) compare the two platforms, (ii) present a conceptual mapping between them, and (iii) implement a bidirectional transformation bridge including both the metamodel and model layer. We evaluate our approach by transforming a collection of publicly available metamodels and automatically generated or manually created models thereof. The transformation outcomes are then used to quantitatively and qualitatively evaluate the transformation’s validity, executability, and expressiveness.

Keywords: Circular economy, Digital thread, Digital twins, Sustainability, Systems engineering
Astract: The perception of the value and propriety of modern engineered systems is changing. In addition to their functional and extra-functional properties, nowadays’ systems are also evaluated by their sustainability properties. The next generation of systems will be characterized by an overall elevated sustainability—including their post-life, driven by efficient value retention mechanisms. Current systems engineering practices fall short of supporting these ambitions and need to be revised appropriately. In this paper, we introduce the concept of circular systems engineering, a novel paradigm for systems sustainability, and define two principles to successfully implement it: end-to-end sustainability and bipartite sustainability. We outline typical organizational evolution patterns that lead to the implementation and adoption of circularity principles, and outline key challenges and research opportunities.