This research uses design science research methodology to present a comparativesynthesis of balancing between materialized and virtualized knowledge graph approaches articulating domain-centric monolithic federated virtual knowledge graphs (FVKGs) leveraging data mapping techniques in healthcare information systems (HIS). Materialized knowledge graphs enable fast query execution by physical instantiation of graph structures. In contrast, virtualized knowledge graphs using the Ontop virtual system to fetch real-time, on-demand querying over disparate data sources. This research thoroughly examines the strengths, shortcomings, and suitability of usage in diverse HIS applications landscape. Key critical factors such as scalability, performance, maintenance overhead, and query efficiency are analyzed to evaluate the effectiveness of each approach. The authors use a federated ontological model leveraging disparate static data models in materialization and support real-time, dynamic changing datasets in virtualization to construct FVKGs. The experimental findings showcase ontological artifacts, balancing between approaches, enhancing data integration and interoperability capabilities to optimize HIS.

The research focuses on the limitations of traditional systems in optimizinginformation flow in the healthcare domain. It focuses on integrating knowledge graphs(KGs) and utilizing AI-powered applications, specifically conversational agents (CAs),particularly during peak operational hours in emergency departments (EDs). Leveragingthe Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designedto harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KGaugmentation empowers CAs with advanced reasoning, knowledge management, andcontext awareness abilities. We utilized a hybrid method integrating a participatory designcollaborative methodology (CM) and Methontology to construct a domain-centric robustformal ontological model depicting and mapping information flow during peak hours inEDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamlessinteraction with end users and enhancing the quality of care within EDs. The authorsleveraged semantic web rule language (SWRL) to enhance inferencing capabilities withinthe KG framework further, facilitating efficient information management for assistinghealthcare practitioners and patients. This innovative assistive solution helps efficientlymanage information flow and information provision during peak hours. It also leads tobetter care outcomes and streamlined workflows within EDs. © 2025 by the authors.

Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. © 2025 by the authors.

The ongoing transition towards electric vehicle (EV) mobility creates a need for charging that drives the development of mobility ecosystems for charging, introducing new actors and a new value creation process. We know little about how value is created and captured in charging mobility ecosystems. The complexity of relations between actors needs to be explained. We therefore ask: What characterizes the value creation process for charging in mobility ecosystems? The study builds on qualitative interviews with different actors in the mobility ecosystem to identify the role of actors in the value creation process for charging. We identify eight value sources and discuss their role for value creation and value realization. In addition, we identify four types of resource combinations in the orchestration of value creation in charging mobility ecosystems.

Achieving seamless data integration (DI) and semantic interoperability (SI) is essential in the healthcare sector and demands patient-centric data that is readily accessible and shared unambiguously across health information systems (HIS). The complex landscape of federated IS highlights auspicious challenges for DI and SI stemming from diverse data sources and formats. The authors showcase a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles and employs Ontop to develop the stateof-the-art federated virtual knowledge graph (FVKG) framework embedded with the virtual knowledge graph (VKG) approach to handle the aforementioned challenges effectively. The proposed FVKG facilitates a unified virtual view of dispersed data sources and diverse models, forming a virtual data federation that effectively addresses data bottlenecks using the Ontop semantic query engine. Using virtual techniques, the FVKG reduces data migration, ensuring low latency and freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, authors suggest a customized hybrid framework for constructing ontological monolithic semantic artifacts, especially in HIS. The FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtual ontological model, integrating various ontology-driven artifacts and ensuring semantic alignment using schema mapping techniques.

AI-based assistants, such as conversational agents (CAs) and social robots, are becoming increasingly important in healthcare organizations. CAs provide a scalable and cost-effective platform for organizations supporting their employees by retrieving, structuring, and analyzing information to assist work processes. This study targets how knowledge graphs as ontological models manage CA that help improve the patient flow processes and reduce patients’ waiting time in the emergency departments (EDs). We tailored the design thinking (DT) method with modelling workshops employing conceptual modelling (CM) techniques to address these issues. We incorporated a hybrid formal approach of Methontology and Tove methodologies to build design artifacts, develop a goal-oriented interactive conversational system between humans and machines, and support information systems (IS). As a result, this ontology-driven approach contribution helps developers build value-added CAs to facilitate healthcare practitioners and patients. It is helpful for quality care delivery experience and improves bottlenecks in information flow within Eds.

The study presents an innovative approach to incorporating AI-driven conversational agents (CAs) or social robots technologies into healthcare information systems (HISs) and revolutionizing healthcare delivery systems. The study aims to improve accessibility and personalization, and minimize adverse risks, especially in the emergency departments (EDs). The study investigates patient-related experiences, long waiting times, and overcrowding issues during peak hours in EDs. Design science research methodology (DSRM) principles were tailored with modelling workshop method to capture domain contextual knowledge and include practitioners' cognitive-tacit knowledge-ability into HIS to address the above-mentioned issues. The developed social robot artifact incorporates an artificial intelligence markup language (AIML) technique as a model to restore domain knowledge of EDs, which serves as a foundation for developing goal-oriented interactive conversational system artifact between humans and machines. As a result, the study contributes that CAs, considered value-added AI-driven applications such as CAs or social robots, serve as a coworker to facilitate healthcare practitioners and patients, catering to patients' needs and communication to enhance care delivery experience and improve information flow processes using interactive services within EDs. The research presents a promising solution to improve patient outcomes, reduce waiting times, and enhance communication between patients and practitioners in EDs.

The enactment of the revised Payment Services Directive (PSD2) by the European Union, has pushed banks to develop digital open banking platforms to enhance market competition and promote innovation in the banking industry. PSD2 mandates banks to offer APIs to provide access to banking data to external third-party providers (TPPs). This mandate might disrupt the role of banks since external complementors such as fintechs have the legal right to access and leverage banking data to offer innovative banking services. As such, the emergence of regulated open banking platforms engenders complex interdependent relationships between banks and fintechs. Drawing on a case of an open banking platform by Nordea Bank, we aim to examine these interdependencies with emphasis on the engagement between banks and external complementors in a regulated platform context. We conclude with insights into the lack of access control by platform owners and new conditions of platform openness. © 2023 International Conference on Information Systems, ICIS 2023: "Rising like a Phoenix: Emerging from the Pandemic and Reshaping Hu. All Rights Reserved.

This study focuses on using AI systems, specifically conversational agents (CAs), to improve information flow during peak hours in healthcare emergency departments (EDs). We customized a Cross Industry Standard Process for Data Mining CRISP-DM approach to a CRISP-Knowledge graph (CRISP-KG) for overall design research. We use a knowledge graph approach to create an intelligent knowledge base (KBs) for CAs, which can enhance their reasoning, knowledge management, and context awareness abilities. We employ a collaborative methodology and ontology design patterns to develop a formal ontological model. Our goal is to build intelligent KBs for CAs that can interact with end-users and improve care quality in EDs, using Semantic Web Rule Language (SWRL) for inference. The KG approach can assist healthcare practitioners and patients in managing information flow more efficiently in EDs, ultimately improving care outcomes. © 2023 CEUR-WS. All rights reserved.

This research work highlights the need for AI-powered applications and their usages for the optimization of information flow processes in the medical sector, from the perspective of how AI-agents can impact human-machine interaction (HCI) for acquiring relevant and necessary information in emergency department (ED). This study investigates how AI-agents can be applied to manage situations of patient related unexpected experiences, such as long waiting times, overcrowding issues, and high number of patients leaving without being diagnosed. For knowledge acquisition, we incorporated modelling workshop techniques for gathering domain information from the domain experts in the context of emergency department in Karolinska Hospital, Solna, Stockholm, Sweden, and for designing the AI-agent utilizing NLP techniques. We discuss how the proposed solution can be used as an assistant to healthcare practitioners and workers to improve medical assistance in various medical procedures to increase flow and to reduce workloads and anxiety levels. The implementation part of this work is based on the natural language processing (NLP) techniques that help to develop the intelligent behavior for information acquisition and its retrieval in a natural way to support patients/relatives’ communication with the healthcare organization efficiently and in a natural way.