Artificial Intelligence Advancements for Digitising Industry

In the digital transformation era, when flexibility and know-how in manufacturing complex products become a critical competitive advantage, artificial intelligence (AI) is one of the technologies driving the digital transformation of industry and industrial products. These products with high complexity based on multi-dimensional requirements need flexible and adaptive manufacturing lines and novel components, e.g., dedicated CPUs, GPUs, FPGAs, TPUs and neuromorphic architectures that support AI operations at the edge with reliable sensors and specialised AI capabilities. The change towards AI-driven applications in industrial sectors enables new innovative industrial and manufacturing models. New process management approaches appear and become part of the core competence in the organizations and the network of manufacturing sites. In this context, bringing AI from the cloud to the edge and promoting the silicon-born AI components by advancing Moore’s law and accelerating edge processing adoption in different industries through reference implementations becomes a priority for digitising industry. This article gives an overview of the ECSEL AI4DI project that aims to apply at the edge AI-based technologies, methods, algorithms, and integration with Industrial Internet of Things (IIoT) and robotics to enhance industrial processes based on repetitive tasks, focusing on replacing process identification and validation methods with intelligent technologies across automotive, semiconductor, machinery, food and beverage, and transportation industries.publishedVersio

A Holistic Framework for Complex Big Data Governance

Big data assets are large datasets that can be leveraged by organisations if the capabilities exist, but it also brings considerable challenges. Despite the benefits that can be realised, the lack of proper big data governance is a major barrier, making the processing and control of this data exceptionally difficult to execute correctly. More specifically, organisations reportedly struggle to incorporate big data governance into their existing structures and business models to derive value from big data initiatives. Big data governance is an emerging research domain, gaining attention from both Information Systems scholars and the practitioner community. Nonetheless, there appears to have been limited scientific research in the area and most existing data governance approaches were limited, given they do not address end-to-end aspects of how big data could be governed. Furthermore, no suitable framework for handling data governance against big data complexities was found to be available. Thus, the main contribution of the work presented in this thesis is to address this requirement; by advancing research in this field and designing a novel holistic big data governance framework capable of supporting global organisations to effectively manage big data as an asset, thereby obtaining value from their big data initiatives. An extensive systematic literature review was done in order to uncover the published content that reflects the current state of knowledge in big data governance. To facilitate the creation of the proposed framework a grounded theory methodology was used to analyse openly available parliamentary inquiry data sources, with particular focus on identifying the core criteria for big data governance. The resulting novel framework generated provides new knowledge by identifying several big data governance building blocks; which are classified as strategic goals, execution stages, enablers and 22 core big data governance components to ensure an effective big data governance programme. Moreover, thesis findings indicate that big data complexities extend to the ethical side of big data governance and this is taken into consideration in the framework design. An ‘ethics by design’ component is proposed to influence how this can be addressed in a structured way

The Elements of Big Data Value

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation

A survey on the development status and application prospects of knowledge graph in smart grids

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Knowledge management as a catalyst for business process digitalisation

Accelerated by the COVID-19 pandemic, businesses are increasingly characterised by a pervasive role of business digitalisation redefining organisations' management of customer experience efficiencies. Digital transformation challenges enterprises' adaptability, development, technology integration, and resilience in evolving their business models. It redefines value creation strategies. This paper argues that enterprises should rethink the creation and delivery of value to their customers, not based on technologies but knowledge management strategies instead. As technologies transform business processes, KM is a catalyst in the evolving nature, knowledge assets, and transformation preparedness of enterprises' value drivers. Integrating Osterwalder and Pigneur's Business Model Canvas, Parmar et al.'s five patterns for value creation, and Goncalves' cloud enterprise transcoding proxy, few cases are briefly discussed. Digital transformation drivers and the role of KM for strategic relevance are underlined through digitalised knowledge processes and customer-centric global marketing strategies to access and manage resources, core competencies, and dynamic capabilities.Published versio

Capabilities for Big Data: An Empirical Study in a Global Pharmaceutical Company

The increasing availability of large quantities of digital data (Big Data) and advanced analytic tools is driving many industries to change their practices. Currently there are limited theoretically informed, in depth empirical studies of the processes and activities that needed to leverage Big Data strategically. This research is a response to calls by international scholars for more in depth and theoretically informed studies of on Big Data (e.g. Brynjolfsson et al., 2011; McAfee et al., 2012; Wamba et al., 2015; Braganza et al., 2017; Mikalef et al., 2017). The study frames Big Data as a new resource and adopts the Resource Based View (RBV) and the Dynamic Capabilities (DC) perspective in order to explore the enhancement of existing capabilities and development of new capabilities required to leverage this new resource. The empirical context of this study is a global pharmaceutical company. Employing a qualitative in-depth case-study approach, this research investigates why a Multinational Corporation (MNC) in the pharmaceutical industry adopted Big Data adoption and how it identified and developed capabilities for this new resource. Data from 24 in-depth interviews and observations of Europe, the Middle East and Africa (EMEA) and Global managers in two project teams were analysed and synthesised using thematic analysis methods. The findings from this study show that the use of Big Data required new and enhanced capabilities that were developed through the action of dynamic capabilities, which operated as mediators between existing capabilities and the new and enhanced capabilities. Although some elements of these dynamic capabilities were embedded in the organisational processes, the activities of senior managers played a crucial role in their development and use. Further, the findings show that the organisation’s cultural transformation was critical for the operation of the dynamic capabilities identified and the new and enhanced Big Data capabilities. In the case study company the development of a Big Data capabilities was found to be an incremental, extended process. The study makes a number of contributions. It provides an in-depth case study of Big Data preparation in the specific context of a MNC pharmaceutical company that is of value to both academics and practitioners. It provides a theoretically based and empirically validated model of the development of capabilities associated with Big Data adoption. Finally, it makes a contribution to academic theory by contributing to the ongoing discussion in the academic literature of the utility of the concept of Dynamic Capabilities

E-finance-lab at the House of Finance : about us

The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..

Towards Big data Governance in Cybersecurity

Big data refers to large complex structured or unstructured data sets. Big data technologies enable organisations to generate, collect, manage, analyse, and visualise big data sets, and provide insights to inform diagnosis, prediction, or other decision-making tasks. One of the critical concerns in handling big data is the adoption of appropriate big data governance frame- works to: 1) curate big data in a required manner to support quality data access for effective machine learning, and 2) ensure the framework regulates the storage and processing of the data from providers and users in a trustworthy way within the related regulatory frame- works (both legally and ethically). This paper proposes a framework of big data governance that guides organisations to make better data-informed business decisions within the related regularity framework, with close attention paid to data security, privacy and accessibility. In order to demonstrate this process, the work also presents an example implementation of the framework based on the case study of big data governance in cyber- security. This framework has the potential to guide the management of big data in different organisations for information sharing and cooperative decision-making