The role of linked data and the semantic web in building operation

Effective Decision Support Systems (DSS) for building service managers require adequate performance data from many building data silos in order to deliver a complete view of building performance. Current performance analysis techniques tend to focus on a limited number of data sources, such as BMS measured data (temperature, humidity, C02), excluding a wealth of other data sources increasingly available in the modern building, including weather data, occupant feedback, mobile sensors & feedback systems, schedule information, equipment usage information. This paper investigates the potential for using Linked Data and Semantic Web technologies to improve interoperability across AEC domains, overcoming many of the roadblocks hindering information transfer currently

DESIGN OPTIONS FOR DATA SPACES

Data spaces receive considerable attention nowadays since they are at the heart of numerous large-scale European research initiatives shaping the data economy. Their goal is to establish secure environments that enable cross-organizational data management and thereby collect, integrate and make available heterogeneous data from various sources. Although we can observe a great interest in establishing new data spaces, questions of what exactly makes a data space and what it takes to design one remain open. To clarify that, we extracted and organized data space characteristics based on the analysis of 53 papers, as well as an empirical analysis of 47 real-world data spaces. We formalize the findings in a taxonomy to provide an intuitive tool that captures important data space design options. Our paper contributes to the understanding of an emerging artifact with significant implications for business, namely data spaces

Design Recommendations for Smart Energy Monitoring: a Case Study in Italy

In the era of green energy and smart grids, the ability to access energy information and effectively analyze such data to extract key performance indicators is a crucial factor for successful building management. Energy data can in fact be exploited both in long-term policy adaptation and in shorter term habits modification, providing the basis for stable improvements of the overall efficiency of buildings and dwellings. To reach the ambitious goal of actually improving how buildings consume energy, four main challenges emerge from literature: (a) lack of skills and experience of energy managers, (b) complex and disparate data sets, which are currently blocking decision making processes, (c) mostly-manual work-flows that struggle to find relevant information into overwhelming streams of data sourced by monitoring systems, and (d) lack of collaborations between organizational departments. This paper provides deeper insights on these challenges, by investigating the kind of analysis currently performed by energy managers (in Italy) and the expectations they have if required to reason about systems that will be available within the next five years, and proposes design recommendations for next generation energy intelligence systems

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

A collaborative approach for metadata management for Internet of Things: Linking micro tasks with physical objects

There has been considerable efforts in modelling the semantics of Internet of Things and their specific context. Acquiring and managing metadata related to the physical devices and their surrounding environment becomes challenging due to the dynamic nature of environment. This paper focuses on managing metadata for Internet of Things with the help of crowds. Specifically, the paper proposes a collaborative approach for collecting and maintaining metadata through micro tasks that can be performed using variety of platforms e.g. mobiles, laptops, kiosks, etc. The approach allows non-experts to contribute towards metadata management through micro tasks, therefore resulting in reduced cost and time. Applicability of the proposed approach is demonstrated through a use case implementation for managing sensor metadata for energy management in small buildings

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Towards Predictive Energy Management in Information Systems: A Research Proposal

The progressive energy transition, driven by the growing number of renewable energies, the increasing social importance of sustainable actions, as well as new technologies, causes major challenges for enterprises and power supply companies (PSCs). While the electricity price fluctuations will continue to increase in the future, the installation of smart meters and smart meter gateways is aimed to ensure grid stability. They provide the basis for communication between companies and PSCs. In order to make companies energy consumption predictable even before the energy is needed, an automated data exchange between an energy management system (EnMS) and enterprise resource planning (ERP) system is essential. Therefore, we address this problem by following five research steps to develop a prototype for predictive energy management in information systems

Linked Water Data For Water Information Management

The management of water consumption is hindered by low general awareness and absence of precise historical and contextual information. Effective and efficiency management of water resources requires a holistic approach considering all the stages of water usage. A decision support tool for water management services requires access to a number of different data domains and different data providers. The design of next-generation water information management systems poses significant technical challenges in terms of information management, integration of heterogeneous data, and real-time processing of dynamic data. Linked Data is a set of web technologies that enables integration of different data sources. This work investigates the usage of Linked Data technologies in the Water Management domain, describes the fundamental concepts of the approach, details an architecture, and discusses possible water management applications

Architecture Design Options for Federated Data Spaces

The massive growth of data and the increasing potential of data analytics in industrial production fuel the emergence of data spaces and corresponding platforms that realize data ecosystems and enable data-driven sustainability applications. To leverage their benefits of demand-driven and scalable data integration, the stakeholders of emerging data space initiatives must make informed decisions about their data space support platforms (DSSPs). This study proposes a conceptual framework based on federated architectures and by considering existing endeavors of data infrastructures. Based on existing literature about data ecosystem resources and an explorative single case study of an industrial data space with sustainability-focused applications, we elaborate on the key design options of data, services, and computing infrastructures. The resulting conceptual framework guides design decisions for DSSPs. The framework captures not only the resources involved but also the operational concepts of federated services and shared services to introduce governance mechanisms and sustainability policies