Keywords: Conceptual Modeling, Multimodal data, Model Enrichment

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: Collaboration, Conceptual Modeling, Experiment, Model Visualization, Participatory Enterprise Modeling
Astract: Participatory modeling is considered more effective for creating higher-quality enterprise models with broader stakeholder acceptance compared to traditional approaches. However, involving stakeholders directly requires more time and effort. To elaborate on the benefits, we conducted an experiment to compare the outcome of participatory enterprise modeling and traditional modeling, e.g., by interviewing stakeholders separately. We let groups of participants work in three different settings, varying the possibility of collaborating and working on a preliminary model. Considering the different conditions, we addressed the following research questions: 1) Do the models differ in size? 2) How well-elaborated are the models in terms of connections made between the elements? 3) Are the contributions of the various participants linked differently across the conditions? We found that collaboration slows down the process, which results in smaller models. Collaboration, however, leads to models that are better integrated. We found no evidence that visualization significantly supports the modeling.

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: Design Process, Design Science Research, Soft Systems Methodology
Astract: Several efforts have been undertaken to define generic guidelines that address specific gaps in the ‘build’ activity of Design Science Research (DSR) artifacts, i.e., constructs, models, methods and frameworks, and instantiations. Unfortunately, explicit guidance is still lacking on how to coherently operationalize such guidelines when building a DSR artifact, particularly a framework. In addition, there is no an elaborate procedure or logical thinking pattern that can be followed when building a DSR artifact, particularly a framework for solving an unstructured problem. Consequently, structural compositions of some artifacts insufficiently subscribe to several general design guidelines, which often hinders the artifacts from fulfilling their intended purposes. To address this gap, Soft Systems Methodology can be leveraged during the design cycle of a DSR initiative, to elaborate the ‘build’ activity and simultaneously support the coherent operationalization of existing general design guidelines. This is demonstrated herein by presenting a Technique of Building Frameworks for guiding Interventions against unstructured problems (TBUFI). From 2011 to 2023, TBUFI has undergone 11 evaluation iterations, which involved: (a) using it to support the building of frameworks for guiding digital interventions in ten research studies; and (b) engaging information systems specialists in a group walkthrough meeting to deliberate its structural composition. Evaluation iterations since 2011 (including feedback from information systems specialists) confirm TBUFI’s ability to successfully guide the design of frameworks that can direct interventions against complex and unstructured problems, by making their ‘build’ activity more elaborate, coherent, and aligned with existing general design guidelines. Thus, TBUFI can be perceived as a supplement for the ‘build’ activity in DSR.

Keywords: Conceptual fidelity, Domain modeling, Return on modeling effort, Views
Astract: Humanity has long since used models, in different shapes and forms, to understand, redesign, communicate about, and shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This has resulted in a wide range of modeling practices. When the models as used in such modeling practices have a key role to play in the activities in which these practices are ‘embedded’, the need emerges to consider the effectiveness and efficiency of such processes, and speak about modeling capabilities. In the latter situation, it also becomes relevant to develop a thorough understanding of the artifacts involved in modeling practices/capabilities. One context in which models play (an increasingly) important role is model-driven systems development, including software engineering, information systems engineering, business process engineering, enterprise engineering, and enterprise architecture management. In such a context, we come across a rich variety of modeling related artifacts, such as views, diagrams, programs, animations, specifications, etc. In this paper, which is actually part of an ongoing ‘journey’ in which we aim to gain deeper insights into the foundations of modeling, we take a fundamental look at the variety of modeling related artifacts as used in the context of model-driven (systems) development, while also presenting an associated framework for understanding, synthesizing the insights we obtained during the ‘journey’ so-far. In doing so, we will also argue that the aforementioned artifacts are actually specific kinds of models, albeit for fundamentally different purposes. The provided framework for understanding involves definitions of domain model, the Return on Modeling Effort (RoME), the conceptual fidelity of domain models, as well as views as a mechanism to manage the complexity of domain models.

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: DEMO, Enterprise modeling, Enterprise ontology, Low-code, MDSD, Mendix
Astract: Due to hyper-competition, technological advancements, regulatory changes, etc, the conditions under which enterprises need to thrive become increasingly turbulent. Consequently, enterprise agility increasingly determines an enterprise’s chances for success. As software development often is a limiting factor in achieving enterprise agility, enterprise agility and software adaptability become increasingly intertwined. As a consequence, decisions that regard flexibility should not be left to software developers alone. By taking a Model-driven Software Development (MDSD) approach, starting from DEMO ontological enterprise models and explicit (enterprise) implementation design decisions, the aim of this research is to bridge the gap from enterprise agility to software adaptability, in such a way that software development is no longer a limiting factor in achieving enterprise agility. Low-code technology is a growing market trend that builds on MDSD concepts and claims to offer a high degree of software adaptability. Therefore, as a first step to show the potential benefits to use DEMO ontological enterprise models as a base for MDSD, this research shows the design of a mapping from DEMO models to Mendix for the (automated) creation of a low-code application that also intrinsically accommodates run-time implementation design decisions.

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: Architecture, Domain model, Enterprise architecture, Enterprise architecture model, Enterprise architecture modeling, Model quality, Ontology
Astract: Models have long since been used, in different shapes and forms, to understand, communicate about, and (re)shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This is also the case in the context of enterprise architecture (EA), where we see a wide range of models in many different shapes and forms being used as well. Researchers in EA modeling usually introduce their own lexicon, and perspective of what a model actually is, while accepting (often implicitly) the accompanying ontological commitments. Similarly, practitioners of EA modeling implicitly also commit to (different) ontologies, resulting in models that have an uncertain ontological standing. This is because, for the subject domain of enterprise architecture models (as opposed to the content of such models), no single ontology has gained major traction. As a result, studies into aspects of enterprise architecture models, such as “model quality” and “return on modeling effort”, are fragmented, and cannot readily be compared or combined. This paper proposes a comprehensive applied ontology, specifically geared to enterprise architecture modeling. Ontologies represent structured knowledge about a particular subject domain. It allows for study into, and reasoning about, that subject domain. Our ontology is derived from a theory of modeling, while clarifying concepts such as “enterprise architecture model”, and introduces novel concepts such as “model audience” and “model objective”. Furthermore, the relevant interrelations between these different concepts are identified and defined. The resulting ontology for enterprise architecture models is represented in OntoUML, and shown to be consistent with the foundational ontology for modeling, Unified Foundational Ontology.