DataScienceBook-2020

Many applications generate massive datasets, like social networking and social influence programs, smart cities applications, smart house environments, cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data proliferate since demands produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, meaningful solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create smart environments. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to answer the user needs better. A variety of solutions for specific applications and platforms exist, so a thorough and systematic analysis of existing solutions for Data Science, Data Analytics, methods and algorithms used in Big Data processing and storage environments has high importance in designing and implementing smart environments.

Many fundamental issues smart environments (smart cities, intelligent buildings, ambient assisted leaving, greenhouses, cyber-physical systems, etc.) are open. Most of the current efforts still do not fully express the heterogeneity of different distributed systems, the interoperability between them, and the resilience of the system.

This book will primarily encompass practical approaches that advance research in Data Science and related applications involving Big Data and on all aspects of Data Analytics and Data Processing in a different type of systems: Data Centers, Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The central role of resource management techniques in this domain is to create suitable frameworks for applications development and deployment in smart environments, concerning to high performance. The book also aims to focus on topics covering algorithms, architectures, management models, high-performance computing techniques and large-scale distributed systems with references to energetic sustainability.

A proposal for a book chapter is needed from prospective authors before the due date, describing the goals and scopes of the proposed chapter.

Target Audience

Although contributions will be open from both academia and industry practitioners and researchers, the audiences of this book are those working in/or are interested in joining interdisciplinary and transdisciplinary works in the Data Science and Big Data Analytics areas. Specifically, this book will attract (1) researchers or senior graduates working in academia; (2) academics, instructors and senior students in colleges and universities (master, PhD, and postdoc students), (3) software developers and (4) stakeholders.