Next-generation big data analytics: state of the art, challenges, and future research topics

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.This work was supported in part by the “Open3D: Collaborative Editing for 3D Virtual Worlds” [EPSRC (EP/M013685/1)], in part by the “Distributed Java Infrastructure for Real-Time Big-Data” (CAS14/00118), in part by eMadrid (S2013/ICE-2715), in part by the HERMES-SMARTDRIVER (TIN2013-46801-C4-2-R), and in part by the AUDACity (TIN2016-77158-C4-1-R). Paper no. TII-16-1

An offloading method using decentralized P2P-enabled mobile edge servers in edge computing

Edge computing has emerged as a promising infrastructure for providing elastic resources in the proximity of mobile users. Owing to resource limitations in mobile devices, offloading several computational tasks from mobile devices to mobile edge servers is the main means of improving the quality of experience of mobile users. In fact, because of the high speeds of moving vehicles on expressways, there would be numerous candidate mobile edge servers available for them to offload their computational workload. However, the selection of the mobile edge server to be utilized and how much computation should be offloaded to meet the corresponding task deadlines without large computing bills are topics that have not been discussed much. Furthermore, with the increasing deployment of mobile edge servers, their centralized management would cause certain performance issues. In order to address these challenges, we firstly apply peer-to-peer networks to manage geo-distributed mobile edge servers. Secondly, we propose a new deadline-aware and cost-effective offloading approach, which aims to improve the offloading efficiency for vehicles and allows additional tasks to meet their deadlines. The proposed approach was validated for its feasibility and efficiency by means of extensive experiments, which are presented in this paper

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

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