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

Monitoring in fog computing: state-of-the-art and research challenges

Fog computing has rapidly become a widely accepted computing paradigm to mitigate cloud computing-based infrastructure limitations such as scarcity of bandwidth, large latency, security, and privacy issues. Fog computing resources and applications dynamically vary at run-time, and they are highly distributed, mobile, and appear-disappear rapidly at any time over the internet. Therefore, to ensure the quality of service and experience for end-users, it is necessary to comply with a comprehensive monitoring approach. However, the volatility and dynamism characteristics of fog resources make the monitoring design complex and cumbersome. The aim of this article is therefore three-fold: 1) to analyse fog computing-based infrastructures and existing monitoring solutions; 2) to highlight the main requirements and challenges based on a taxonomy; 3) to identify open issues and potential future research directions.This work has been (partially) funded by H2020 EU/TW 5G-DIVE (Grant 859881) and H2020 5Growth (Grant 856709). It has been also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52 PID2019-108713RB-C52 / AEI / 10.13039/501100011033)

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

Collaborative application servers

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.Includes bibliographical references (leaves 121-123).by Ivan S. Limansky.M.Eng

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

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

Quantum node portal- Devices and information management

An Internship in a European Company for developing a Web application-Domatica Global Solutions, Lisbon was undertaken to complete the Master’s Degree of Computer Engineering-Mobile Computing in the Polytechnic Institute of Leiria. The team Domatica deals with providing IoT solutions used for monitoring, controlling and collecting the data from the IoT gateways. The present work aims to develop a Web application for client’s side. The Web application named Quantum Node Portal is developed for the Devices and Information management. It provides access to the clients to their IoT gateways. Clients can monitor their devices, get various information, also can access the Portal for claiming their IoT gateways. The present work was developed using various technologies such as PHP framework named Laravel and several languages

Real Cyber Value at Risk: An Approach to Estimate Economic Impacts of Cyberattacks on Businesses

To compete in today’s digitized economy, companies rely on computer programs to manage processes efficiently and bring their services directly to customers. However, these tools increase not only business opportunities but also the risk of falling victim to cyber attacks. Consulting firms and academic literature provide several approaches to manage this risk exposure. Nonetheless, most solutions fail to provide individualized, quantitative attack cost estimates based on real-world empirical data. Especially Small and Middle-Sized Enterprises (SME) struggle to quantify their attack exposure due to limited resources and a lack of IT knowledge. This thesis addresses this gap in the current literature by proposing the novel Real Cyber Value at Risk (RCVaR) framework. Consisting of three components, the RCVaR provides a monetary, annualized cost and risk prediction for an individual firm. Thus, addressing the issue of individual risk perception and allowing cross-domain risk comparisons. Evaluating the cost predictions on previously “unseen” data from real-world incidents shows that the RCVaR achieves an Absolute Percentage Error (APE) of 2%. The evaluation further proves that the model reflects quantitative real-world attack cost behavior. To portray the risk component of the RCVaR, the newly proposed Cyber Value at Risk (CVaR) is integrated into the model. In contrast to previous research, the CVaR is not computed with Monte Carlo simulations but on the basis of actual historical quantitative data. Both, cost and risk predictions, are tailored towards SMEs and are easily accessible over a web application. The last contribution of this thesis is a Federated Learning (FL) methodology to address the prevalent lack of realworld cost incident data in cyber security economics. Comparing the performance of different FL models against traditional centralized networks suggests that the process can successfully learn cost prediction functions. Consequently, Federated Learning presents a viable solution to the data scarcity issue. In conclusion, the Real Cyber Value at Risk provides a novel and cost-effective approach to obtain quantitative cost and risk measures that integrate seamlessly into the company’s overall budget planning process