Abstract Software engineering involves more than coding. It encompasses planning, development, communication, and process management. Scrum, the most widely adopted agile methodology, helps teams deliver value iteratively, yet practitioners often struggle with challenges such as maintaining requirement clarity, reducing cognitive load, and managing communication overhead. As artificial intelligence (AI) becomes increasingly integrated into the software engineering lifecycle, its potential to improve productivity, quality, and decision-making is gaining significant attention. Moreover, Scrum offers a structured yet flexible framework, but it remains unclear which AI features can most effectively support its practices in real-world settings. Therefore, this study addresses that gap by identifying and prioritizing key Scrum AI Support Features (SAISFs) based on industry needs. A two-phase research approach was used. First, a focus group with five software engineering industry experts identified 18 relevant SAISFs. Second, a survey using the Kano methodology was conducted with 344 experienced Scrum practitioners to evaluate and prioritize these features. The results were analyzed across three Scrum team size groups: small ( < = 6), medium (7–10), and large (11+), and four functional SAISF groups: Requirements Support (R), Development Support (D), Communication Support (C), and Scrum Process Support (S). The research also provides prioritization of SAISFs according to Scrum roles. Our findings offer actionable insights for designing AI-enhanced tools tailored to Scrum teams, highlighting the importance of considering team size and Scrum roles when prioritizing AI features. This study contributes to the agile software engineering literature by offering a practitioner-informed foundation for integrating AI into Scrum-based project environments. Future Scrum tools may become adaptive and context-aware, automatically tailoring workflows, predicting bottlenecks, and optimizing team communication and performance.

ABSTRACT Introduction Defining an experimental configuration is a complex decision problem for early‐stage researchers, who must map goals, constraints, and requirements onto datasets, algorithms, and parameter settings that directly affect experimental outcomes. Existing scientific workflow engines improve execution and reproducibility; however, they rarely capture the decision rationale behind configuration choices, which is needed to inform future selections. Method We propose a context‐aware decision‐support framework that formalises experiment configuration as a structured and sequential decision problem. The framework combines three components: a semantic Knowledge Graph (KG) storing historical configurations, contextual attributes, and decision rationale; an MDP‐based Option Explorer that filters the KG under user‐defined constraints and ranks feasible configurations by expected cumulative reward; and a Graphical User Interface for specifying constraints, inspecting ranked alternatives, and providing structured feedback. Unlike existing workflow systems, the framework explicitly separates user‐defined context from automated reasoning, producing an interpretable ranked list rather than a single opaque recommendation. We evaluated the framework in a user study with 90 MSc‐ and PhD‐level researchers performing a model‐selection task, using a synthetic dataset of one million experimental configurations under three levels of contextual detail. Results Compared with manual search, the framework reduced decision time (up to 68%), reduced perceived difficulty (up to 36%), and increased user satisfaction (up to 43%) under the constrained condition. Conclusion By formalising the link between experimental context and probabilistic decision ranking, the framework improves reproducibility and scalability of decision support in scientific experimentation.

Drug traceability is currently a very challenging area given the complexity of several issues, including drug quality and counterfeit medications. The counterfeited drugs have a major impact on human life, treatment outcomes and economic burden. To deal with these issues, we propose a semantic blockchain-based system for drug traceability that aims at detecting counterfeit drugs in order to improve the patients’ safety and quality of life as well as eliminating manufacturers’ potential loss and increasing their revenue. Our proposal is based on blockchain and semantic web technologies to enhance the representation capability of data in the pharmaceutical supply chain.

Abstract Various smart applications, such as in the domain of smart construction, require the use of artificial intelligence (AI) based services. In order to support such environments, various AI/knowledge service provider and consumer ecosystems have started to emerge. Within such ecosystems, the goals are to improve the quality, reliability, dependability in terms of governance and trust in the data which is exchanged among the various actors, which must be supported by specific business models. This work introduces a novel multi‐party smart contract (SC) that is designed to address the above mentioned goals. Specific service level agreements that illustrate the interactions among the AI service providers and consumers are also presented. The developed multi‐party SC supports different pricing schemes, which are analyzed in detail against the goals of the study.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Summary Applications that make use of Internet of Things (IoT) can capture an enormous amount of raw data from sensors and actuators, which is frequently transmitted to cloud data centers for processing and analysis. However, due to varying and unpredictable data generation rates and network latency, this can lead to a performance bottleneck for data processing. With the emergence of fog and edge computing hosted microservices, data processing could be moved towards the network edge. We propose a new method for continuous deployment and adaptation of multi‐tier applications along edge, fog, and cloud tiers by considering resource properties and non‐functional requirements (e.g., operational cost, response time and latency etc.). The proposed approach supports matchmaking of application and Cloud‐To‐Things infrastructure based on a subgraph pattern matching (P‐Match) technique. Results show that the proposed approach improves resource utilization and overall application Quality of Service. The approach can also be integrated into software engineering workbenches for the creation and deployment of cloud‐native applications, enabling partitioning of an application across the multiple infrastructure tiers outlined above.

We are pleased to present a special issue that focuses on state-of-the-art research on Elastic Computing from Edge to the Cloud Environments. Today, a huge amount of data is being generated by the Internet of Things (IoT) devices such as smartphones, sensors, cameras, cars, and robots.1 In order to process the generated data, there exist Big Data platforms (such as Hadoop and Spark). Conventionally, they are deployed in centralized Data Centers, which, however, fall short of addressing time-critical requirements of the applications due to high latency between the Edge, where the data are generated and the Data Centers where they are processed.2 The emerging Edge and Fog computing paradigms promise to solve this problem by seamlessly integrating hardware and software resources across multiple computing tiers, from the Edge to the Data Center/Cloud. Since computing resources at the Edge may be power and capacity constrained, it is necessary to invent new lightweight platforms and techniques that seamlessly interact, sense, execute and produce results with very low latency, while at the same time address other high-level requirements of applications, such as security and privacy. Regarding these problems, there are many challenges that must be addressed with the invention of new architectures, methods, algorithms, and solutions. This special issue features six papers covering a range of topics including IoT application deployment frameworks in edge-cloud environments, cost optimization models, and lightweight virtualization model. The first paper in this special issue titled “Edge-adaptable serverless acceleration for machine learning Internet of Things applications”3 presents STOIC (serverless teleoperable hybrid cloud), an IoT application deployment and offloading system that extends the serverless model. The authors have developed a dynamic feedback control mechanism to precisely predict latency and dispatch workloads uniformly across edge and cloud systems using a distributed serverless framework. STOIC leverages hardware acceleration (e.g., GPU resources) for serverless function execution when available from the underlying cloud system. Finally, it configures the system to overcome deployment variability associated with public clouds. The system is evaluated using real-world machine learning applications and multitier IoT deployments (edge and cloud) and shown that it reduces overall execution time and achieves placement accuracy in the range of 92%–97%. The second paper in this special issue titled “Server Configuration Optimisation in Mobile Edge Computing: A Cost-Performance Tradeoff Perspective”4 studies the problem of server configuration optimization in Mobile edge computing environments. The authors use M/M/m queuing models and establish the performance and cost models for the system. The article considers cost-constrained performance optimization, and performance constrained cost optimization based on multiple numerical algorithms. The numerical simulation-based experiments have shown this approach is able to balance the trade-off between investment cost and service quality. The third paper in this special issue titled “EFFORT: Energy-efficient framework for offload. Communication in mobile cloud computing”5 proposes a task offloading mechanism from mobile to the remote cloud. The author's solution aims to solve the energy consumption of communication-intensive applications from mobile devices such as smartphones. The experimental evaluation is done by implementing a demonstration application in Android mobile OS. The results have shown that the proposed solution reduces the energy consumption of smartphones while executing applications and simultaneously reducing the communication cost. The fourth paper in this special issue titled “Human Microservices: A framework for turning humans into service providers”6 presents a framework facilitating the deployment of Application Programming Interface on companion devices (smartphones and IoT devices). The author's framework proposes a new approach aiming to integrate humans in the IoT loop and facilitate computation units' deployment in the devices that are closer to users, instead of remote clouds. The solution leverages existing standards for the rapid development of applications and improves software quality. Through a detailed case study on monitoring people's activity, the authors developed a software application following framework specification and demonstrated the feasibility of their proposed solution. The fifth paper in this special issue titled “Function delivery network: Extending serverless computing for heterogeneous platforms”7 introduces the extension to Function-as-a-Service model. The authors consider heterogeneous clusters and support heterogeneous functions through a network of distributed heterogeneous target platforms called Function Delivery Network (FDN). The article proposes Function-Delivery-as-a-Service, a service model to deliver the function to the required to be targeted platform. It also ensures that Service Level Objective (SLO) energy efficiency requirements are met when scheduling functions. The new benchmarking system FDNInspecto is implemented which benchmarks the different distributed target platforms. The results have found that functions scheduling on edge reduce energy consumption by 17× without violating the SLO requirements when compared to a high-end target platform. Finally, the last paper of this special issue titled “A Lightweight Virtualisation Model to Enable Edge Computing in Deeply Embedded Systems”8 builds a virtualization model for resource-constrained embedded devices. The existing containers cannot be used in many deeply embedded systems (DES) due to an underlying operating system's resource requirements including storage, memory, and processing power. To mitigate this issue, the authors present the Hellfire hypervisor, a lightweight virtualization model that enables separation and improves the security of IoT applications on DES. The Hellfire simplifies essential components of virtualization including compute, storage, and I/O among others. The experimental results conducted with the coremark benchmark show Hellfire has a small footprint of 23 KB while keeping a low average virtualization overhead of 0.62% for multiple virtual machines execution. To summarize, the articles in this special issue provide new definitions, architectural principles, systems, and approaches to solve the challenges posed by emerging distributed systems such as edge and cloud computing, mainly driven by IoT workloads. They address important problems including lightweight platforms, energy efficiency, reliability and easy to integrate frameworks. We hope that you will find this special issue truly useful and enjoyable. We sincerely thank the editor-in-chief for helping us to organize this special issue. We thank the editorial office staffs for their continuous support. We are also thankful to all the authors for submitting their research works. More importantly, thanks to the reviewers for their valuable contributions in providing thoughtful comments and improving the quality of articles.

Databases as software components may be used to serve a variety of smart applications. Currently, the Internet of Things (IoT), Artificial Intelligence (AI) and Cloud technologies are used in the course of projects such as the Horizon 2020 EU-Korea DECENTER project in order to implement four smart applications in the domains of Smart Homes, Smart Cities, Smart Construction and Robot Logistics. In these smart applications the Big Data pipeline starts from various sensor and video streams to which AI and feature extraction methods are applied. The resulting information is stored in database containers, which have to be placed on Edge, Fog or Cloud infrastructures. The placement decision depends on complex application requirements, including Quality of Service (QoS) requirements. Information that must be considered when making placement decisions includes the expected workload, the list of candidate infrastructures, geolocation, connectivity and similar. Software engineers currently perform such decisions manually, which usually leads to QoS threshold violations. This paper aims to automate the process of making such decisions. Therefore, the goals of this paper are to: (1) develop a decision making method for database container placement; (2) formally verify each placement decision and provide probability assurances to the software engineer for high QoS; and (3) design and implement a new architecture that automates the whole process. A new optimisation method is introduced, which is based on the theory and practice of stochastic Markov Decision Processes (MDP). It uses as input monitoring data from the container runtime, the expected workload and user-related metrics in order to automatically construct a probabilistic finite automaton. The generated automaton is used for both automated decision making and placement success verification. The method is implemented in Java. It also uses the PRISM model-checking tool. Kubernetes is used in order to automate the whole process when orchestrating database containers across Edge, Fog and Cloud infrastructures. Experiments are performed for NoSQL Cassandra database containers for three representative workloads of 50000 (workload 1), 200000 (workload 2) and 500000 (workload 3) CRUD database operations. Five computing infrastructures serve as candidates for database container placement. The new MDP-based method is compared with the widely used Analytic Hierarchy Process (AHP) method. The obtained results are used to analyse container placement decisions. When using the new MDP based method there were no QoS violations in any of the placement cases, while when using the AHP based method the placement results in some QoS threshold violations in all workload cases. Due to its properties, the new MDP method is particularly suitable for implementation. The paper also describes a multi-tier distributed computing system that uses multi-level (infrastructure, container, application) monitoring metrics and Kubernetes in order to orchestrate database containers across Edge, Fog and Cloud nodes. This architecture demonstrates fully automated decision making and high QoS container operation.

In recent years, several new multipurpose Big Data platforms have emerged. They are used in various application domains with diverse requirements. Evaluating complex Big Data solutions is not a trivial task, due to the need to assess their utility in both quantitative and qualitative terms based on existing use cases. In this short paper, we discuss the requirements and the methodology for such an evaluation. We also discuss how benchmarking could be part of such an evaluation methodology.

Many emerging smart applications use sensor data, which are integrated by using various Big Data platforms. Such smart applications must address several requirements including high Quality of Service, privacy and security. Emerging fog computing technologies may provide some new possibilities to address these requirements through the design of multi-tier, container-based applications. In this work, we present the design of a smart application for the domain of civil engineering, which is currently undergoing testing and evaluation.

Container-based cloud applications require sophisticated auto-scaling methods in order to operate under different workload conditions.The choice of an auto-scaling method may significantly affect important service quality parameters, such as response time and resource utilization.Current container orchestration systems such as Kubernetes and cloud providers such as Amazon EC2 employ auto-scaling rules with static thresholds and rely mainly on infrastructure-related monitoring data, such as CPU and memory utilization.This paper presents a new dynamic multi-level (DM) auto-scaling method with dynamically changing thresholds, which uses not only infrastructure, but also application-level monitoring data.The new method is compared with seven existing autoscaling methods in different synthetic and real-world workload scenarios.Based on experimental results, all eight auto-scaling methods are compared according to the response time and the number of instantiated containers.The results show that the proposed DM method has better overall performance under varied amount of workloads than the other auto-scaling methods.Due to satisfactory results, the proposed DM method is implemented in the SWITCH software engineering system for time-critical cloud applications.

Summary New software engineering technologies facilitate development of applications from reusable software components, such as Virtual Machine and container images (VMI/CIs). Key requirements for the storage of VMI/CIs in public or private repositories are their fast delivery and cloud deployment times. ENTICE is a federated storage facility for VMI/CIs that provides optimisation mechanisms through the use of fragmentation and replication of images and a Pareto Multi‐Objective Optimisation (MO) solver. The operation of the MO solver is, however, time‐consuming due to the size and complexity of the metadata, specifying various non‐functional requirements for the management of VMI/CIs, such as geolocation, operational cost, and delivery time. In this work, we address this problem with a new semantic approach, which uses an ontology of the federated ENTICE repository, knowledge base, and constraint‐based reasoning mechanism. Open Source technologies such as Protégé, Jena Fuseki, and Pellet were used to develop a solution. Two specific use cases, (1) repository optimisation with offline and (2) online redistribution of VMI/CIs, are presented in detail. In both use cases, data from the knowledge base are provided to the MO solver. It is shown that Pellet‐based reasoning can be used to reduce the input metadata size used in the optimisation process by taking into consideration the geographic location of the VMI/CIs and the provenance of the VMI fragments. It is shown that this process leads to reduction of the input metadata size for the MO solver by up to 60% and reduction of the total optimisation time of the MO solver by up to 68%, while fully preserving the quality of the solution, which is significant.

Trust is a crucial aspect when cyber-physical systems have to rely on resources and services under ownership of various entities, such as in the case of Edge, Fog and Cloud computing. The DECENTER’s Fog Computing Platform is developed to support Big Data pipelines, which start from the Internet of Things (IoT), such as cameras that provide video-streams for subsequent analysis. It is used to implement Artificial Intelligence (AI) algorithms across the Edge-Fog-Cloud computing continuum which provide benefits to applications, including high Quality of Service (QoS), improved privacy and security, lower operational costs and similar. In this article, we present a trust management architecture for DECENTER that relies on the use of blockchain-based Smart Contracts (SCs) and specifically designed trustless Smart Oracles. The architecture is implemented on Ethereum ledger (testnet) and three trust management scenarios are used for illustration. The scenarios (trust management for cameras, trusted data flow and QoS based computing node selection) are used to present the benefits of establishing trust relationships among entities, services and stakeholders of the platform.

• Probabilistic method for choosing an optimal cloud deployment option . • Equivalence classification of available cloud deployment options. • Model-checking approach to verify decision-making results. • Experimental study comparing the new probabilistic method with a baseline method. Context : Existing software workbenches allow for the deployment of cloud applications across a variety of Infrastructure-as-a-Service (IaaS) providers. The expected workload, Quality of Service (QoS) and Non-Functional Requirements (NFRs) must be considered before an appropriate infrastructure is selected. However, this decision-making process is complex and time-consuming. Moreover, the software engineer needs assurances that the selected infrastructure will lead to an adequate QoS of the application. Objective : The goal is to develop a new method for selection of an optimal cloud deployment option, that is, an infrastructure and configuration for deployment and to verify that all hard and as many soft QoS requirements as possible will be met at runtime. Method : A new Formal QoS Assurances Method (FoQoSAM), which relies on stochastic Markov models is introduced to facilitate an automated decision-making process. For a given workload, it uses QoS monitoring data and a user-related metric in order to automatically generate a probabilistic model. The probabilistic model takes the form of a finite automaton . It is further used to produce a rank list of cloud deployment options. As a result, any of the cloud deployment options can be verified by applying a probabilistic model checking approach. Results : Testing was performed by ranking deployment options for two cloud applications, File Upload and Video-conferencing. The FoQoSAM method was compared to a baseline Analytic Hierarchy Process (AHP). The results show that the first ranked cloud deployment options satisfy all hard and at least one of the soft requirements for both methods, however, the FoQoSAM method always satisfies at least an additional QoS requirement compared to the baseline AHP method. Conclusions : The proposed new FoQoSAM method is appropriate and can be used in decision-making when ranking and verifying cloud deployment options. Due to its practical utility it was integrated into the SWITCH workbench.

Summary The use of virtual machines (VMs) in Cloud computing provides various benefits in the overall software engineering lifecycle. These include efficient elasticity mechanisms resulting in higher resource utilization and lower operational costs. The VMs as software artifacts are created using provider‐specific templates, called virtual machine images (VMI), and are stored in proprietary or public repositories for further use. However, some technology‐specific choices can limit the interoperability among various Cloud providers and bundle the VMIs with nonessential or redundant software packages, leading to increased storage size, prolonged VMI delivery, stagnant VMI instantiation, and ultimately vendor lock‐in. To address these challenges, we present a set of novel functionalities and design approaches for efficient operation of distributed VMI repositories, specifically tailored for enabling (1) simplified creation of lightweight and size optimized VMIs tuned for specific application requirements; (2) multi‐objective VMI repository optimization; and (3) efficient reasoning mechanism to help optimizing complex VMI operations. The evaluation results confirm that the presented approaches can enable VMI size reduction by up to 55%, while trimming the image creation time by 66%. Furthermore, the repository optimization algorithms can reduce the VMI delivery time by up to 51% and cut down the storage expenses by 3%. Moreover, by implementing replication strategies, the optimization algorithms can increase the system reliability by 74%.

Recently, a promising trend has evolved from previous centralized computation to decentralized edge computing in the proximity of end-users to provide cloud applications. To ensure the Quality of Service (QoS) of such applications and Quality of Experience (QoE) for the end-users, it is necessary to employ a comprehensive monitoring approach. Requirement analysis is a key software engineering task in the whole lifecycle of applications; however, the requirements for monitoring systems within edge computing scenarios are not yet fully established. The goal of the present survey study is therefore threefold: to identify the main challenges in the field of monitoring edge computing applications that are as yet not fully solved; to present a new taxonomy of monitoring requirements for adaptive applications orchestrated upon edge computing frameworks; and to discuss and compare the use of widely-used cloud monitoring technologies to assure the performance of these applications. Our analysis shows that none of existing widely-used cloud monitoring tools yet provides an integrated monitoring solution within edge computing frameworks. Moreover, some monitoring requirements have not been thoroughly met by any of them.

Many new Internet of Things (IoT) applications such a disaster early warning systems, video-streaming, automated driving and similar, are increasingly being built by using advanced component based software engineering approaches. Software components can include various executable images, such as container or Virtual Machine images, scripts and others. Achieving adequate Quality of Service (QoS) for such applications is still a challenging issue due to runtime variations in running conditions intrinsic to the cloud, edge and fog environments. These types of systems should therefore be continuously monitored and hence adapted at various levels including infrastructure, container and application levels. In this work, we present an adaptation method using a new Incremental Learning approach based on Multi-Level Monitoring data. The method dynamically generates a set of rules representing a performance prediction model that allow us to find potential performance bottlenecks and then propose suitable application adaptation actions. Adaptation possibilities in this work include (1) live-migration of application components (such as containers) from the current infrastructure to another one with different characteristics, such as CPU, memory, disk or bandwidth capacity, and (2) dynamic horizontal or vertical scaling of container-based application instances to offer better fitted resource capacities.

Virtualization is a key enabling technology in Cloud computing that allows users to run multiple virtual machines (VMs) with their own application environment on top of physical hardware. It permits scaling up and down of applications by elastic on-demand provisioning of VMs in response to their variable load to achieve increased utilization efficiency at a lower operational cost, while guaranteeing the desired level of Quality of Service (QoS) to the end-users. Typically, VMs are created using provider-specific templates that are stored in proprietary repositories, leading to provider lock-in and hampering portability or simultaneous usage of multiple federated Clouds. In this context, optimization at the level of the virtual machine image is needed both by the applications and by the underlying Cloud providers for improved resource usage, operational costs, elasticity, storage use, and other desired QoS-related features. To overcome those issues, the ENTICE project researches and creates a novel VM repository and operational environment for federated Cloud infrastructures. There exists a large variety of industrial applications that can strongly benefit by the ENTICE environment. In this paper we present an interesting selection of complementary use cases that drive the definition of the essential requirements for the ENTICE environment, and more importantly, validate the introduced innovations.

Software developers increasingly need to include time-critical functionalities in their Web applications. Examples of these can be Internet-of-Things (IoT), gaming systems, instant messaging, video conferencing, and similar. Ensuring the Quality of Service (QoS) for such applications has been a challenging issue mostly due to runtime variations in the network quality between the clients and the service running in the Cloud. In this paper, we propose an adaptive multi-instance container-based architecture called Autonomous Self-Adaptation Platform (ASAP) that applies an edge computing concept with technologies, such as Docker and Kubernetes to facilitate the desired QoS for every single usage event of the time-critical functionality. We use a "File Upload" use case as an example to explore time-critical functionality. Each time a client needs to use the File Upload functionality, a specific setup, physical host selection and resources allocation is made in order to provide the desired QoS to that functionality.

Time critical applications have very high requirements on network and computing services, in particular on well-tuned software architecture with sophisticated optimisation on data communication. Their development is often customised to dedicated infrastructure, and system performance is difficult to maintain when infrastructure changes. This fatal weakness in existing architecture and software tools causes very high development costs, and makes it difficult to fully utilise the virtualised, programmable and quality-on-demand services provided by networked Clouds to improve the system productivity. The Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications (SWITCH) is a newly funded project by EU H2020 to address this urgent industrial need, it aims at improving the existing development and execution model of time critical applications by introducing a novel conceptual model called application-infrastructure co-programming and control model, in which application QoS/QoE together with the programmability and controllability of Cloud environments can be all included in the complete lifecycle of applications.

The development of applications for the Cloud requires programming skills and knowledge about the several programming models, APIs and underlying infrastructures, which are provided by Cloud vendors. The European Project mOSAIC aims at developing an API, Platform and a set of tools to facilitate language and platform agnostic application development and deployment on a variety of Infrastructures as a Service offers. Within the mOSAIC project, appropriate ontologies, a knowledge base and an associated Semantic Engine [10] have been developed to support the Cloud application developer in the tasks of discovering the needed functionalities and resources for application development through vendor independent representations of such application components, and representation of generic programming concepts and patterns, including application domain related ones. In this paper the use of the Semantic Engine, its ontologies and knowledge base is illustrated by following the design and implementation of an application for analysis of structures under static loading that is based on the Finite Element Method.

Considered a combination of Software-as-a-Service and Business Process Outsourcing, Business Process as a Service has the potential to provide the killer applications able to propel Cloud computing to a higher technological level. Following a general overview of the current state-of-the-art in research and implementation of the BPaaS, we propose a mind switch towards the applicability of re-usable business process patterns and a multi-modeling approach for BPaaS design, deployment and operation. In order to demonstrate the feasibility of the approach we provide two examples of BPaaSs that can take advantage of Cloud computing in order to gain clear benefits in terms of services' efficiency.

Achieving sustainability has become an important goal in the construction, refurbishment, operation and management of buildings. To this end, we need to achieve greater information exchange, especially, about practices and solutions for Energy Efficiency (EE) and the use of Renewable Energy Sources (RES) in buildings. However, in the building life-cycle, complex and disparate information sources are used by various stakeholders, thus understanding, integrating, managing and providing means for sharing such information is a challenging task. In this paper, we analyze the possibilities to capture, distill and disseminate expert know-how related to sustainable buildings, addressing the needs of the various stakeholders. A Sustainable Building Profile (SBP) is presented, which is a novel conceptual model designed to integrate information on EE and RES aspects of buildings. The SBP makes it possible to analyse the transformation of a particular building over time. Different stakeholders can use it to study various engineering, operation and maintenance problems in buildings related to energy efficiency.