Design Approach to Unified Service API Modeling for Semantic Interoperability of Cross-enterprise Vehicle Applications

This work was partially supported by Ministry of Education, Youth and Sports of the Czech Republic, university specific research, project SGS-2019-018 Processing of heterogeneous data and its specialized applications

Framework for a business interoperability quotient measurement model

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova da Lisboa para obtenção do grau de Mestre em Engenharia e Gestão Industrial (MEGI)Over the last decade the context of Interoperability has been changing rapidly. It has been expanding from the largely technically focused area of Information Systems towards Business Processes and Business Semantics. However, there exists a need for more comprehensive ways to define business interoperability and enable its performance measurement as a first step towards improvement of interoperability conditions between collaborating entities. Through extensive literature reviews and analysis of European Research initiatives in this area, this dissertation presents the State of the Art in Business Interoperability. The objective of this dissertation is to develop a model that closely captures the factors that are responsible for Business Interoperability in the context of Collaborative Business Processes. This Business Interoperability Quotient Measurement Model (BIQMM), developed in this dissertation uses an interdisciplinary approach to capture the key elements responsible for collaboration performance. Through the quantification of the relevance of each element to the particular collaboration scenario in question, this model enables a quantitative analysis of Business Interoperability, so that an overall interoperability score can be arrived at for enhanced performance measurements.Finally, the BIQMM is applied to a business case involving Innovayt and LM Glassfiber to demonstrate its applicability to different collaboration scenarios

A metamodel to annotate knowledge based engineering codes as enterprise knowledge resources

The encoding of Knowledge Based Engineering (KBE) software applications is becoming a prominent tool for the automation of knowledge intensive tasks carried out using Computer Aided Design (CAD) technology. However, limitations exist on the ability to manage the engineering knowledge models embedded in these executable KBE applications. This research proposes a metamodel to annotate encoded KBE applications. Resulting from the annotation, XKMs become explicit knowledge resources whose content can be better accessed and managed. The attachment of metadata to data sets in enterprise repositories is a necessary step to identify and index them so they can be queried, browsed and changed. The sophistication of metadata models for these data “items” ranges from the simple indexing using numbers to more sophisticated representations describing their context information (i.e. author, creation date, etc.), their internal structure and their content. Current engineering data repositories like Product Data Management and Product Lifecycle Management systems offer predefined metamodels to annotate a range of engineering data items including CAD files or special types of documents. At the moment, there is no metadata model specifically designed to annotate KBE codes. In this situation, an undifferentiated metadata model needs to be used for XKMs. However, in this case the only information retained by the system about them would be context metadata. Once an instance of the metadata is attached to an XKM, it can be used as its identifier within an enterprise data repository. The proposed metamodel contains abstract entities to annotate XKMs. The resulting descriptive model for an XKM pays attention to its internal structure and its operation at different levels of granularity. The particular design of the proposed metamodel positions it at a level of abstraction between non executable domain knowledge models and executable KBE applications. This design choice is made to support the use of the metadata not only as an informative model but also as an executable one. The achievement of this target is becoming possible through the emergence of semantic modelling standards that allow the description of data models independently from the language of implementation. Using this approach, the generation of code and metadata is made automatically using mapping rules resulting from the semantic agreement between models and specific syntax rules. The immediate application of the developed metamodel is to annotate XKMs within PLM systems. The approach shall contribute not only to systematically store instances of XKMs but also to manage the lifecycle of the engineering knowledge encoded within them. The proposed representation provides a more comprehensive approach for non KBE language experts to understand the code. On this basis, the change on the metamodels can be automatically traced back to the code and vice-versa. During the research, evidence has been gathered from the community of KBE technology users and vendors on the need to support this research effort. In the long term, the research contributes to the use of PLM systems as a platform for engineering knowledge management.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Internet of Things Applications - From Research and Innovation to Market Deployment

The book aims to provide a broad overview of various topics of Internet of Things from the research, innovation and development priorities to enabling technologies, nanoelectronics, cyber physical systems, architecture, interoperability and industrial applications. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from technology to international cooperation and the global "state of play".The book builds on the ideas put forward by the European research Cluster on the Internet of Things Strategic Research Agenda and presents global views and state of the art results on the challenges facing the research, development and deployment of IoT at the global level. Internet of Things is creating a revolutionary new paradigm, with opportunities in every industry from Health Care, Pharmaceuticals, Food and Beverage, Agriculture, Computer, Electronics Telecommunications, Automotive, Aeronautics, Transportation Energy and Retail to apply the massive potential of the IoT to achieving real-world solutions. The beneficiaries will include as well semiconductor companies, device and product companies, infrastructure software companies, application software companies, consulting companies, telecommunication and cloud service providers. IoT will create new revenues annually for these stakeholders, and potentially create substantial market share shakeups due to increased technology competition. The IoT will fuel technology innovation by creating the means for machines to communicate many different types of information with one another while contributing in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge shared into the Internet of Everything. The success of IoT depends strongly on enabling technology development, market acceptance and standardization, which provides interoperability, compatibility, reliability, and effective operations on a global scale. The connected devices are part of ecosystems connecting people, processes, data, and things which are communicating in the cloud using the increased storage and computing power and pushing for standardization of communication and metadata. In this context security, privacy, safety, trust have to be address by the product manufacturers through the life cycle of their products from design to the support processes. The IoT developments address the whole IoT spectrum - from devices at the edge to cloud and datacentres on the backend and everything in between, through ecosystems are created by industry, research and application stakeholders that enable real-world use cases to accelerate the Internet of Things and establish open interoperability standards and common architectures for IoT solutions. Enabling technologies such as nanoelectronics, sensors/actuators, cyber-physical systems, intelligent device management, smart gateways, telematics, smart network infrastructure, cloud computing and software technologies will create new products, new services, new interfaces by creating smart environments and smart spaces with applications ranging from Smart Cities, smart transport, buildings, energy, grid, to smart health and life. Technical topics discussed in the book include: • Introduction• Internet of Things Strategic Research and Innovation Agenda• Internet of Things in the industrial context: Time for deployment.• Integration of heterogeneous smart objects, applications and services• Evolution from device to semantic and business interoperability• Software define and virtualization of network resources• Innovation through interoperability and standardisation when everything is connected anytime at anyplace• Dynamic context-aware scalable and trust-based IoT Security, Privacy framework• Federated Cloud service management and the Internet of Things• Internet of Things Application

Parallel and Distributed Execution of Model Management Programs

The engineering process of complex systems involves many stakeholders and development artefacts. Model-Driven Engineering (MDE) is an approach to development which aims to help curtail and better manage this complexity by raising the level of abstraction. In MDE, models are first-class artefacts in the development process. Such models can be used to describe artefacts of arbitrary complexity at various levels of abstraction according to the requirements of their prospective stakeholders. These models come in various sizes and formats and can be thought of more broadly as structured data. Since models are the primary artefacts in MDE, and the goal is to enhance the efficiency of the development process, powerful tools are required to work with such models at an appropriate level of abstraction. Model management tasks – such as querying, validation, comparison, transformation and text generation – are often performed using dedicated languages, with declarative constructs used to improve expressiveness. Despite their semantically constrained nature, the execution engines of these languages rarely capitalize on the optimization opportunities afforded to them. Therefore, working with very large models often leads to poor performance when using MDE tools compared to general-purpose programming languages, which has a detrimental effect on productivity. Given the stagnant single-threaded performance of modern CPUs along with the ubiquity of distributed computing, parallelization of these model management program is a necessity to address some of the scalability concerns surrounding MDE. This thesis demonstrates efficient parallel and distributed execution algorithms for model validation, querying and text generation and evaluates their effectiveness. By fully utilizing the CPUs on 26 hexa-core systems, we were able to improve performance of a complex model validation language by 122x compared to its existing sequential implementation. Up to 11x speedup was achieved with 16 cores for model query and model-to-text transformation tasks

A formal architecture-centric and model driven approach for the engineering of science gateways

From n-Tier client/server applications, to more complex academic Grids, or even the most recent and promising industrial Clouds, the last decade has witnessed significant developments in distributed computing. In spite of this conceptual heterogeneity, Service-Oriented Architecture (SOA) seems to have emerged as the common and underlying abstraction paradigm, even though different standards and technologies are applied across application domains. Suitable access to data and algorithms resident in SOAs via so-called ‘Science Gateways’ has thus become a pressing need in order to realize the benefits of distributed computing infrastructures.In an attempt to inform service-oriented systems design and developments in Grid-based biomedical research infrastructures, the applicant has consolidated work from three complementary experiences in European projects, which have developed and deployed large-scale production quality infrastructures and more recently Science Gateways to support research in breast cancer, pediatric diseases and neurodegenerative pathologies respectively. In analyzing the requirements from these biomedical applications the applicant was able to elaborate on commonly faced issues in Grid development and deployment, while proposing an adapted and extensible engineering framework. Grids implement a number of protocols, applications, standards and attempt to virtualize and harmonize accesses to them. Most Grid implementations therefore are instantiated as superposed software layers, often resulting in a low quality of services and quality of applications, thus making design and development increasingly complex, and rendering classical software engineering approaches unsuitable for Grid developments.The applicant proposes the application of a formal Model-Driven Engineering (MDE) approach to service-oriented developments, making it possible to define Grid-based architectures and Science Gateways that satisfy quality of service requirements, execution platform and distribution criteria at design time. An novel investigation is thus presented on the applicability of the resulting grid MDE (gMDE) to specific examples and conclusions are drawn on the benefits of this approach and its possible application to other areas, in particular that of Distributed Computing Infrastructures (DCI) interoperability, Science Gateways and Cloud architectures developments

Building the Hyperconnected Society- Internet of Things Research and Innovation Value Chains, Ecosystems and Markets

This book aims to provide a broad overview of various topics of Internet of Things (IoT), ranging from research, innovation and development priorities to enabling technologies, nanoelectronics, cyber-physical systems, architecture, interoperability and industrial applications. All this is happening in a global context, building towards intelligent, interconnected decision making as an essential driver for new growth and co-competition across a wider set of markets. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from research to technological innovation, validation and deployment.The book builds on the ideas put forward by the European Research Cluster on the Internet of Things Strategic Research and Innovation Agenda, and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in future years. The concept of IoT could disrupt consumer and industrial product markets generating new revenues and serving as a growth driver for semiconductor, networking equipment, and service provider end-markets globally. This will create new application and product end-markets, change the value chain of companies that creates the IoT technology and deploy it in various end sectors, while impacting the business models of semiconductor, software, device, communication and service provider stakeholders. The proliferation of intelligent devices at the edge of the network with the introduction of embedded software and app-driven hardware into manufactured devices, and the ability, through embedded software/hardware developments, to monetize those device functions and features by offering novel solutions, could generate completely new types of revenue streams. Intelligent and IoT devices leverage software, software licensing, entitlement management, and Internet connectivity in ways that address many of the societal challenges that we will face in the next decade