Towards Scalability Guidelines for Semantic Data Container Management

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Improving automation standards via semantic modelling: Application to ISA88

Standardization is essential for automation. Extensibility, scalability, and reusability are important features for automation software that rely in the efficient modelling of the addressed systems. The work presented here is from the ongoing development of a methodology for semi-automatic ontology construction methodology from technical documents. The main aim of this work is to systematically check the consistency of technical documents and support the improvement of technical document consistency. The formalization of conceptual models and the subsequent writing of technical standards are simultaneously analyzed, and guidelines proposed for application to future technical standards. Three paradigms are discussed for the development of domain ontologies from technical documents, starting from the current state of the art, continuing with the intermediate method presented and used in this paper, and ending with the suggested paradigm for the future. The ISA88 Standard is taken as a representative case study. Linguistic techniques from the semi-automatic ontology construction methodology is applied to the ISA88 Standard and different modelling and standardization aspects that are worth sharing with the automation community is addressed. This study discusses different paradigms for developing and sharing conceptual models for the subsequent development of automation software, along with presenting the systematic consistency checking methodPeer ReviewedPostprint (author's final draft

Describing and Processing Topology and Quality of Service Parameters of Applications in the Cloud

Typical cloud applications require high-level policy driven orchestration to achieve efficient resource utilisation and robust security to support different types of users and user scenarios. However, the efficient and secure utilisation of cloud resources to run applications is not trivial. Although there have been several efforts to support the coordinated deployment, and to a smaller extent the run-time orchestration of applications in the Cloud, no comprehensive solution has emerged until now that successfully leverages applications in an efficient, secure and seamless way. One of the major challenges is how to specify and manage Quality of Service (QoS) properties governing cloud applications. The solution to address these challenges could be a generic and pluggable framework that supports the optimal and secure deployment and run-time orchestration of applications in the Cloud. A specific aspect of such a cloud orchestration framework is the need to describe complex applications incorporating several services. These application descriptions must specify both the structure of the application and its QoS parameters, such as desired performance, economic viability and security. This paper proposes a cloud technology agnostic approach to application descriptions based on existing standards and describes how these application descriptions can be processed to manage applications in the Cloud

On Constructing Persistent Identifiers with Persistent Resolution Targets

Persistent Identifiers (PID) are the foundation referencing digital assets in scientific publications, books, and digital repositories. In its realization, PIDs contain metadata and resolving targets in form of URLs that point to data sets located on the network. In contrast to PIDs, the target URLs are typically changing over time; thus, PIDs need continuous maintenance -- an effort that is increasing tremendously with the advancement of e-Science and the advent of the Internet-of-Things (IoT). Nowadays, billions of sensors and data sets are subject of PID assignment. This paper presents a new approach of embedding location independent targets into PIDs that allows the creation of maintenance-free PIDs using content-centric network technology and overlay networks. For proving the validity of the presented approach, the Handle PID System is used in conjunction with Magnet Link access information encoding, state-of-the-art decentralized data distribution with BitTorrent, and Named Data Networking (NDN) as location-independent data access technology for networks. Contrasting existing approaches, no green-field implementation of PID or major modifications of the Handle System is required to enable location-independent data dissemination with maintenance-free PIDs.Comment: Published IEEE paper of the FedCSIS 2016 (SoFAST-WS'16) conference, 11.-14. September 2016, Gdansk, Poland. Also available online: http://ieeexplore.ieee.org/document/7733372

Flexible media transport framework based on service composition for future network

This work introduces common guidelines defined in several standardization organisms towards future networks based on the actual mechanisms and protocols used to treat the multimedia data, most of them placed in the application layer of the OSI reference model.Peer ReviewedPreprin

Interoperability and FAIRness through a novel combination of Web technologies

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs

Ontology-based patterns for the integration of business processes and enterprise application architectures

Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data. Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their applicability in business process-driven application integration is demonstrated

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems