Responsive Open Learning Environments at the Open University

Personal Learning Environments (PLEs) offer new opportunities for supporting personalized and self regulated learning both in formal and in informal education. The Open University in the UK is an early adopter of PLEs through a number of different initiatives, one of which is the European project ROLE (Responsive Open Learning Environments). This paper presents some of the lessons learned and best practices from the introduction of ROLE technologies within an informal learning test-bed at the Open University

A semantic framework for cloud learning environments

Cloud Learning Environments (CLEs) are gradually gaining ground over traditional Learning Management Systems (LMS) by facilitating the lone or collaborative study of user-chosen blends of content and courses from heterogeneous sources, including Open Educational Resources (OER). In this chapter, we describe the use of ontologies for modelling various aspects of the learning process within such an environment. In particular, we consider a semantic knowledge base as the core of the learning environment, facilitating learners in finding educational services on the cloud. We describe how different stakeholder clusters are involved in the creation and maintenance of this knowledge base, through collaborative ontology management techniques. Finally, we define the mechanisms for the evolution of this knowledge base and the constant updating of the associated cloud learning services

A Concurrence Study on Interoperability Issues in IoT and Decision Making Based Model on Data and Services being used during Inter-Operability

The Internet-of-Things (IoT) has become an important topic among researchers owing to its potential to change the way we live and use smart devices. In recent years, many research work found in the world are interrelated and convey via the existing web structure which makes a worldwide system called IoT. This study focused on the significant improvement of answers for a wider scope of gadgets and the Internet of Things IoT stages in recent years. In any case, each arrangement gives its very own IoT framework, gadgets, APIs, and information configurations promoting interoperability issues. These issues are the outcome of numerous basic issues, difficulty to create IoT application uncovering cross-stage, and additionally cross-space, trouble in connecting non-interoperable IoT gadgets to various IoT stages, what's more, eventually averts the development of IoT innovation at an enormous scale. To authorize consistent data sharing between various IoT vendors, endeavors by a few academia, industrial, and institutional groups have accelerated to support IoT interoperability. This paper plays out a far-reaching study on the cutting-edge answers for encouraging interoperability between various IoT stages. Likewise, the key difficulties in this theme are introduced

Survey on Quality of Observation within Sensor Web Systems

The Sensor Web vision refers to the addition of a middleware layer between sensors and applications. To bridge the gap between these two layers, Sensor Web systems must deal with heterogeneous sources, which produce heterogeneous observations of disparate quality. Managing such diversity at the application level can be complex and requires high levels of expertise from application developers. Moreover, as an information-centric system, any Sensor Web should provide support for Quality of Observation (QoO) requirements. In practice, however, only few Sensor Webs provide satisfying QoO support and are able to deliver high-quality observations to end consumers in a specific manner. This survey aims to study why and how observation quality should be addressed in Sensor Webs. It proposes three original contributions. First, it provides important insights into quality dimensions and proposes to use the QoO notion to deal with information quality within Sensor Webs. Second, it proposes a QoO-oriented review of 29 Sensor Web solutions developed between 2003 and 2016, as well as a custom taxonomy to characterise some of their features from a QoO perspective. Finally, it draws four major requirements required to build future adaptive and QoO-aware Sensor Web solutions

Introducing personal learning environments to informal learners: lessons learned from the OpenLearn case study

Personal Learning Environments (PLEs) hold the potential to address the needs of informal learners for multi-sourced content and easily customisable learning environments. This paper presents the lessons learned from a case study regarding the use of widget-based PLEs by informal learners for finding and evaluating Open Educational Resources (OER). The lessons learned from this case study have allowed the authors to detect some of the obstacles for the successful adoption of PLEs by informal learners, as well as to identify ways for overcoming these obstacles

Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D

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

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

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network

Digital Twin in the IoT context: a survey on technical features, scenarios and architectural models

Digital Twin is an emerging concept that is gaining attention in various industries. It refers to the ability to clone a physical object into a software counterpart. The softwarized object, termed logical object, reflects all the important properties and characteristics of the original object within a specific application context. To fully determine the expected properties of the Digital Twin, this paper surveys the state of the art starting from the original definition within the manufacturing industry. It takes into account related proposals emerging in other fields, namely, Augmented and Virtual Reality (e.g., avatars), Multi-agent systems, and virtualization. This survey thereby allows for the identification of an extensive set of Digital Twin features that point to the “softwarization” of physical objects. To properly consolidate a shared Digital Twin definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a Digital Twin. Once the Digital Twin definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities. Four application scenarios illustrate how the Digital Twin concept can be used and how some industries are applying it. The scenarios also lead to a generic DT architectural Model. This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the Digital Twin. The paper, eventually, analyses a set of possible evolution paths for the Digital Twin considering its possible usage as a major enabler for the softwarization process