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

Deep Reinforcement Learning (DRL)-based Methods for Serverless Stream Processing Engines: A Vision, Architectural Elements, and Future Directions

Streaming applications are becoming widespread across an extensive range of business domains as an increasing number of sources continuously produce data that need to be processed and analysed in real time. Modern businesses are aggressively using streaming data to generate valuable knowledge that can be used to automate processes, help decision-making, optimize resource usage, and ultimately generate revenue for the organization. Despite their increased adoption and tangible benefits, support for the automated deployment and management of streaming applications is yet to emerge. Although a plethora of stream management systems have flooded the open source community in recent years, all of the existing frameworks demand a considerably challenging and lengthy effort from human operators to manually and continuously tune their configuration and deployment environment in order to reach and maintain the desired performance goals. To address these challenges, this article proposes a vision for creating Deep Reinforcement Learning (DRL)-based methods for transforming stream processing engines into self-managed serverless solutions. This will lead to an increase in productivity as engineers can focus on the actual development process, an increase in application performance potentially leading to reduced response times and more accurate and meaningful results, and a considerable decrease in operational costs for organizations.Comment: 21 pages, 10 figure

Programmable Edge-to-Cloud Virtualization for 5G Media Industry: The 5G-MEDIA Approach

To ensure high Quality of Experience (QoE) for end users, many media applications require significant quantities of computing and network resources, making their realization challenging in resource constrained environments. In this paper, we present the approach of the 5G-MEDIA project, providing an integrated programmable service platform for the development, design and operations of media applications in 5G networks, facilitating media service management across the service life cycle. The platform offers tools to service developers for efficient development, testing and continuous correction of services. One step further, it provides a service virtualization platform offering horizontal services, such as a Media Service Catalogue and accounting services, as well as optimization mechanisms to flexibly adapt service operations to dynamic conditions with efficient use of infrastructure resources. The paper outlines three use cases where the platform was tested and validated

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