Most applications generate large 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 grows rapidly, since applications 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, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. 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 is significant in designing and implementing a smart environment.
Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: 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 main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.
Table of Contents
Preface. Contributors. Mobility-Aware Solutions for Edge Data Center Deployment in Urban Environments. Effective Data Assimilation with Machine Learning. Semantic Data Model for Energy Efficient Integration of Data Centres in Energy Grids. Managing the safety in smart buildings using semantically-enriched BIM and occupancy data approach. Belief Rule-Based Adaptive Particle Swarm Optimization. NoSQL Environments and Big Data Analytics for Time Series. A Territorial Intelligence-based Approach for Smart Emergency Planning. Big Data Analysis and Applications for Energy Performant Buildings and Smart Cities. Selecting Suitable Plants for a Given Area using Data Analysis Approaches. Ontology-Based Security Requirements Framework for Current and Future Vehicles. Dynamic Resource Provisioning Using Cognitive Intelligent Networks based on Stochastic Markov Decision Process. Data model for water resource management. References.
Marta Chinnici graduated in Mathematics (2004) magna cum laude at the University of Naples (Italy), where she received her PhD in Mathematics and Computer Science (2008) with a thesis on stochastic self-similar processes and applications in non-linear dynamical systems. Currently, she is a Senior Researcher at the ENEA’s Department of Energy Technologies and Renewable Energy Sources, ICT Division, where she works on ICT for Energy Efficiency issues. She is a European Commission Expert in the field of ICT and a review/evaluator for several European programs on the same topic. She is Member of Technical Programme Committees of various international conferences and workshops, and Editor/Referee of relevant journals in the fields of computer science, data science and energy. To date, she authored more than 50 scientific articles and books, and presented in leading national and international conferences on ICT and energy topics.
Florin POP is professor at the Department of Computer and Information Technology, the Politehnica University of Bucharest. He also works as a 1st degree scientific researcher at National Institute for Research and Development in Informatics (ICI) Bucharest. His general research interests are: distributed systems (design and performance), grid computing and cloud computing, peer-to-peer systems, Big Data management, data aggregation, information retrieval and classification techniques, Bio-inspired optimization methods.
Cǎtǎlin NEGRU is a system engineer and researcher at the Department of Computer and Information Technology at University Politehnica of Bucharest (UPB). He obtained his PhD from UPB in 2016. His research interests include distributed systems, energy efficiency, cloud storage, cyber- physical systems, GIS. His research has led to the publishing of numerous papers and articles at prestigios journals and conferences. He is involved in several national and international research projects.