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Full Description
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.
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.
