Big Data Platforms and Applications [electronic resource] : Case Studies, Methods, Techniques, and Performance Evaluation /

1. Data Center for Smart Issues: Energy and Sustainability Issue -- 2 Apache Spark for Digitalization, Analysis and Optimization of Discrete Manufacturing Process -- 3. An Empirica Study on Teleworking among Slovakia's Office-Based Academics -- 4. DSS for Pro-Active Flood Management of Water Reservoir Systems -- 5. exhiSTORY: Small Self-Organizing Exhibits -- 6. IoT Cloud Design Patterns -- 7. Cloud-based mHealth Streaming for IoT Processing -- 8. A System for Monitoring Water Quality Parameters in Rivers: Challenges and Solutions.

This book provides a review of advanced topics relating to the theory, research, analysis and implementation in the context of big data platforms and their applications, with a focus on methods, techniques, and performance evaluation. The explosive growth in the volume, speed, and variety of data being produced every day requires a continuous increase in the processing speeds of servers and of entire network infrastructures, as well as new resource management models. This poses significant challenges (and provides striking development opportunities) for data intensive and high-performance computing, i.e., how to efficiently turn extremely large datasets into valuable information and meaningful knowledge. Features: * Presents a comprehensive review of the latest developments in big data platforms * Proposes state-of-the-art technological solutions for important issues in big data processing, resource and data management, fault tolerance, and monitoring and controlling * Covers basic theory, new methodologies, innovation trends, experimental results, and implementations of real-world applications The task of context data management is further complicated by the variety of sources such data derives from, resulting in different data formats, with varying storage, transformation, delivery, and archiving requirements. At the same time rapid responses are needed for real-time applications. With the emergence of cloud infrastructures, achieving highly scalable data management in such contexts is a critical problem, as the overall application performance is highly dependent on the properties of the data management service. Dr. Florin Pop is a professor at the Department of Computer Science and Engineering at the University Politehnica of Bucharest, Romania, and a senior researcher (1st degree) at the Department of Intelligent and Distributed Data Intensive Systems at the National Institute for Researchand Development in Informatics, Bucharest, Romania. Dr. Gabriel Neagu is a senior researcher (1st degree) at the Department of Intelligent and Distributed Data Intensive Systems at the National Institute for Research and Development in Informatics, Bucharest, Romania.