George Fragiadakis

Secure, sovereign data sharing is essential for the digital transformation of industrial sectors, yet designing sustainable Business Models (BMs) for multi-stakeholder Data Spaces remains a significant challenge. This study situates BM innovation for Data Spaces in the context of European manufacturing, addressing regulatory, technological, and economic constraints. The methods involve mapping stakeholder needs to platform capabilities, and systematically developing and validating revenue and value-capture models using established frameworks and real-world use cases. Results show that collaborative, modular BMs—built on principles of data sovereignty, interoperability, and trust—enable the creation and distribution of economic and strategic value for operators, data providers, and consumers. Application in predictive maintenance use cases for refineries and wind farms validates these models, demonstrating cost reductions, improved operational efficiency, and compliance with current European data governance standards. The findings suggest that viable BMs for industrial Data Spaces hinge on inclusive stakeholder participation, flexible monetization strategies, and continuous adaptation to regulatory environments. These insights provide practical guidance for stakeholders seeking to realize the full value of data-driven manufacturing ecosystems.

Secure, sovereign data sharing is essential for the digital transformation of industrial sectors, yet designing sustainable Business Models (BMs) for multi-stakeholder Data Spaces remains a significant challenge. This study situates BM innovation for Data Spaces in the context of European manufacturing, addressing regulatory, technological, and economic constraints. The methods involve mapping stakeholder needs to platform capabilities, and systematically developing and validating revenue and value-capture models using established frameworks and real-world use cases. Results show that collaborative, modular BMs—built on principles of data sovereignty, interoperability, and trust—enable the creation and distribution of economic and strategic value for operators, data providers, and consumers. Application in predictive maintenance use cases for refineries and wind farms validates these models, demonstrating cost reductions, improved operational efficiency, and compliance with current European data governance standards. The findings suggest that viable BMs for industrial Data Spaces hinge on inclusive stakeholder participation, flexible monetization strategies, and continuous adaptation to regulatory environments. These insights provide practical guidance for stakeholders seeking to realize the full value of data-driven manufacturing ecosystems.

Cybersecurity is increasingly recognized as a key enabler of Sustainable Development Goals (SDGs) and especially SDG 3 (Good Health and Well-being) as healthcare systems become more digitized. This study prioritizes cybersecurity control families from the NIST 800-53r5 framework using a structured framework combining the Analytic Hierarchy Process (AHP) and the Impact–Feasibility Matrix. From the impact–feasibility perspective, expert judgment reveals that while impact is the primary driver in selecting controls, feasibility—particularly budget and cost constraints—plays a decisive role in real-world implementation. A group of fifteen experts, including cybersecurity officers, health IT professionals, and public health advisors, has participated in structured surveys as per the methodological framework of this paper. Financial and budgetary limitations emerged as the top feasibility barrier, often determining whether high-impact controls are deployed or delayed. This underscores the need for strategic investments and phased implementation approaches, particularly in resource-constrained health systems. The results provide a practical roadmap for policymakers and healthcare administrators to allocate cybersecurity resources effectively, balancing technical necessity with economic feasibility to support resilient digital health infrastructures.

To maximize the business value of the cloud, the cost of cloud solutions is explored alongside technical quality and performance. To enable this form of exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides the required decision-making. Prominent providers, such as Google and Microsoft, provide FinOps to their customers, integrating cost factors when designing a cloud solution. However, different providers apply different pricing policies for their products, and these policies also change through time. Therefore, there are numerous efforts to explore price evolution through time for different cloud products applying different decision-making methods using different datasets. In an effort to establish a systematic approach to support decision-making on alternative pricing policies for cloud services and compare them across services and providers, the CloudPricingOps framework is proposed in this paper. It constitutes a decision support system that provides alternative decision-making methods, such as hedonic models, time series, and clustering and machine learning techniques, to deal with problems related to cloud product pricing policy analysis, comparison and prediction. It also constitutes a systematic method of discrete steps to integrate additional decision-making methods to deal with these problems and input datasets to be used regardless of the method or the cloud pricing problem that needs to be solved. Two discrete examples based on real data are also presented in this paper to demonstrate the usefulness of the CloudPricingOps framework for cloud engineering and business teams.

To maximise the business value of the cloud the cost of cloud solutions is explored together with technical quality and performance. To enable such exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides such sort of decision making. Prominent providers, such as Google and Microsoft provide FinOps to their customers, integrating cost factors when designing a cloud solution. However, different providers apply different pricing policies for their products, while these policies also adjust through time. To this end, there are numerous efforts to explore price evolution through time for different cloud products applying different decision-making methods using different data-sets. In an effort to establish a systematic approach to support decision making on alternative pricing policies for cloud services and compare them across services and providers the CloudPricingOps framework is proposed in the paper. It constitutes a decision support system that provides alternative decision making methods, such as hedonic models, time series, clustering and machine learning techniques, to deal with problems related cloud product pricing policy analysis, comparison and prediction. It also constitutes a systematic method of discrete steps to integrate additional decision making methods to deal with these problems and input datasets to be used independently of method or cloud pricing problem to solve. To discrete examples based on real-data are also presented in the paper to demonstrate the usefulness of CloudPricingOps framework for cloud engineering and business teams.

When exploring alternative cloud solution designs, it is important to also consider cost. Thus, having a comprehensive view of the cloud market and future price evolution allows well-informed decisions to choose between alternatives. Cloud providers offer various service types with different pricing policies. Currently, infrastructure-as-a-Service (IaaS) is considered the most mature cloud service, while reserved instances, where virtual machines are reserved for a fixed period of time, have the largest market share. In this work, we employ a machine-learning approach based on the CatBoost algorithm to explore a price-prediction model for the reserve instance market. The analysis is based on historical data provided by Amazon Web Services from 2016 to 2022. Early results demonstrate the machine-learning model’s ability to capture the underlying evolution patterns and predict future trends. Findings suggest that prediction accuracy is not improved by integrating data from older time periods.

As cloud computing has grown in popularity, several different models and deployment strategies have emerged, fulfilling specific needs of different users. Thus cloud service selection is a major task that combines business and technical aspects. Several cloud providers categorize their bundles to help users find the best fit. However, the policy behind this categorization is unclear, making it difficult to be consistent among providers. Therefore, this work explores the complication of selecting the optimal cloud service among numerous and comparable solutions and introduces a selection framework for cloud services based on clustering analysis, providing an overall size categorization of cloud services derived from the cloud market and making the categorization policy explicit and homogeneous across all providers. Finally, a real-word case study is presented, highlighting the application of the proposed framework.

Cloud computing environments allow businesses to deploy applications in a fast and scalable way. Infrastructure-as-a-service (IaaS) and container-as-a-service (CaaS) models can be adopted for the deployment of cloud-based applications. The current paper presents a specific near-real-world scenario of a cloud-based application, deployed by the two aforementioned cloud models. The deployment cost differs between the cloud models and relies on the number of utilized resources, which is driven by the user demand. Since the cost is a major importance factor that finally determines the adoption of cloud technology, it is challenging to estimate and examine the cost of each proposed approach. This research can help cloud computing professionals pick a model that meets their goals and budget. It's also useful for cloud cost analysis. The corresponding costs of the two deployments are estimated based on the pricing policies of major providers, Amazon, Google, and Microsoft.