Digital twins are virtual replicas of their physical counterparts, providing real-time monitoring and decision-making capabilities. By integrating forecasting-based methods, the potential of digital twins can be augmented significantly, enabling them to execute advanced predictive tasks. However, with digital twins typically involving a human-in-the-loop, the need for explainability becomes crucial for understanding how and why a forecast was made. To effectively integrate explainability methods, forecasting methods, and digital twins, it is essential to define the relations between these components in a structured manner. In this work, we address this issue by providing a meta-model for the integration of explainable forecasting methods with digital twins. We evaluate our meta-model in the context of a smart building digital twin with multiple forecasting and explainability methods. The evaluation demonstrates the inherent trade-off between providing explanations and generating accurate forecasts in this context.

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 of their interactions, and self-organisation. Designing DBEs is a task demanding a well-defined DBE’s scope, roles and responsibilities of the actors, their interactions and dependencies, as well as versatile technologies and data. The study focuses on two DBEs—Marispace-X and Skippo in the maritime domain to capture the tenets of the blue economy with dataspaces. Because the design approaches to DBE are scarce due to the paradigm’s novelty, the study aims to evaluate a model-based design method, DBEmap. The evaluation results concerning practitioners’ perceived usefulness of the DBEmap and its support for integrating DBE-related perspectives and benchmarking DBE resilience are presented. Reflections on the applicability of DBEmap and its implications for organisational support, highlighting the systematic guidance and actionable outcomes observed in the two case workshops, are discussed alongside the study’s limitations and directions for future research.

Digital twin systems can benefit from the integration of artificial intelligence (AI) algorithms for providing for example some predictive capabilities or supporting internal decision-making. As AI algorithms are often opaque, it becomes necessary to explain their decisions to a human operator working with the digital twin. In this study, we investigate the integration of explainable AI techniques with digital twins, which we termed XAI-DT system. We define the concept of XAI-DT system and provide a use case in smart buildings, where explainable AI is used to forecast CO₂ concentration. Further, we present a core architectural model for our digital twin, outlining its interaction with the smart building and its internal processing. We evaluate five AI algorithms and compare their explainability for the operator and the entire digital twin model based on standard explainability properties from the literature.

This special issue brings together the best papers from the ER 2023 conference, held in November 2023 in Lisbon, Portugal. Widely recognized as ER, the conference is the world’s leading forum for exploring the state of the art, emerging trends, and future challenges in conceptual modeling. The inaugural ER conference took place in 1979 in Los Angeles. Initially a biennial event, it became an annual conference in 1985 and has since been hosted in 20 countries across five continents. Over the decades, ER has fostered a globally connected, scientifically rigorous community of academics and practitioners committed to advancing the field.The Data & Knowledge Engineering (DKE) journal, dedicated to the intersection of data and knowledge engineering, is a natural venue for this special issue. Conceptual modeling—ER’s core focus—lies at the heart of knowledge engineering, relying on data as a foundational concept for representing and managing knowledge through information systems. The ER conference has a long history of fruitful collaboration with the DKE journal, which motivated us, the ER 2023 Conference Chairs, to continue the tradition of organizing a post-conference special issue.

Modeling for information systems (IS) analysis and design offers broad insights into the advances and challenges of enterprise, business process, software, and conceptual modeling. In celebration of its 30th edition, this paper presents a data-driven retrospective analysis of studies published at the Exploring Modeling Methods for Systems Analysis and Development (EMMSAD) working conference from 2005 to 2024. EMMSAD has long been a key venue for research on Information Systems (IS) Modeling, covering areas such as conceptual modeling, enterprise modeling, and model-driven engineering, as well as the evaluation of modeling techniques and tools. Using machine learning, specifically Dynamic Topic Modeling (DTM) with BERTopic, this study identifies recurring topics, emerging trends, and shifts in research focus within the IS modeling community. The findings highlight key areas of alignment between IS modeling and the broader modeling landscape, providing insights into the field’s evolution and future research opportunities.

Object-Centric Process Mining (OCPM) enables business process analysis from multiple perspectives. For example, an educational path can be examined from the viewpoints of students, teachers, and groups. This analysis depends on Object-Centric Event Data (OCED), which captures relationships between events and object types, representing different perspectives. Unlike traditional process mining techniques, extracting OCED minimizes the need for repeated log extractions when shifting the analytical focus. However, recording these complex relationships increases the complexity of the log extraction process. To address this challenge, this paper proposes a methodology for extracting OCED based on PM2, a well-established process mining framework. Our approach introduces a structured framework that guides data analysts and engineers in extracting OCED for process analysis. We validate this framework by applying it in a real-world educational setting, demonstrating its effectiveness in extracting an Object-Centric Event Log (OCEL), which serves as the standard format for recording OCED, from a learning management system and an administrative grading system.

Capabilities provide a structured and stable business-centric view of what an organization does. By enabling the organization’s architecture of what it is able to do, as modular functional building blocks, capability has become a standard design element of enterprise architecture frameworks such as TOGAF, Archimate, OMG Business Architecture, and many others. Despite clear modularity and purpose of the capability notion, for many companies becoming capability-aware and being able to continuously and efficiently manage a large portfolio of capabilities is a very tedious task. To address this challenge, this study sets a foundation to leverage capability management by the means of a capability middleware broker and LLM support. The envisioned theoretical solution is exemplified by a real business case from the HE domain.

This book constitutes the proceedings of the 17th IFIP Working Conference on the Practice of Enterprise Modeling, PoEM 2024, which took place in Stockholm, Sweden, during December 3-5, 2024.

PoEM offers a forum for sharing experiences and knowledge between the academic community and practitioners from industry and the public sector. This year the theme of the conference is Industry 5.0 and Society 5.0.

The 17 full papers presented in this volume were carefully reviewed and selected from a total of 48 submissions. They were organized in topical sections named as follows: Enterprise modeling for digital transformation and industry applications; advances in enterprise modelling techniques; process mining and business process analysis; security, compliance, and configuration in enterprise modeling.

For the time series forecasting task, several state-of-the-art algorithms employ moving-average decomposition for improved accuracy. However, the potential of decomposition techniques to enhance time series forecasting methods has not been explored in detail. In this work, we comprehensively investigate the use of decomposition methods for the forecasting task, comparing different decomposition techniques and their effect on forecasting accuracy, as well as the possibility of providing model-agnostic interpretability. We rework recent forecasting models to be compatible with any decomposition technique and experimentally evaluate their effectiveness in different forecasting setups. We further propose and assess a model-agnostic framework using decomposition for interpretability. Our results show that decomposition can improve forecasting accuracy, especially for the proposed decomposition-adapted models. Additionally, we demonstrate that the architectural choices of existing forecasting models can be improved by using different decomposition blocks internally. We found that decomposition techniques must be configured with a low number of components to provide model-agnostic interpretability. Our work concludes that decomposition can enhance time series forecasting algorithms, improving both their performance and interpretability.