Serving as a flagship driver towards advance research in the area of Big Data platforms and applications, this book provides a platform for the dissemination of advanced topics of theory, research efforts and analysis, and implementation oriented on methods, techniques and performance evaluation. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These chapters discuss the research of members from the ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). This volume is ideal as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp the key concerns and their potential solutions.

Provides a comprehensive overview of the development of RMS for big data platforms and applications, covering theory, methodologies, experimentation, and real-world applications

Performance Modeling of Big Data Oriented Architectures.- Workflow Scheduling Techniques for Big Data Platforms.- Cloud Technologies: A New Level for Big Data Mining.- Agent Based High-Level Interaction Patterns for Modeling Individual and Collective Optimizations Problems.- Maximize Profit for Big Data Processing in Distributed Datacenters.- Energy and Power Efficiency in the Cloud.- Context Aware and Reinforcement Learning Based Load Balancing System for Green Clouds.- High-Performance Storage Support for Scientific Big Data Applications on the Cloud.- Information Fusion for Improving Decision-Making in Big Data Applications.- Load Balancing and Fault Tolerance Mechanisms for Scalable and Reliable Big Data Analytics.- Fault Tolerance in MapReduce: A Survey.- Big Data Security.- Big Biological Data Management.- Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms.- Feature Dimensionality Reduction for Mammographic Report Classification.- Parallel Algorithms for Multi-Relational Data Mining: Application to Life Science Problems.- Parallelization of Sparse Matrix Kernels for Big Data Applications.- Delivering Social Multimedia Content with Scalability.- A Java-Based Distributed Approach for Generating Large-Scale Social Network Graphs.- Predicting Video Virality on Twitter.- Big Data uses in Crowd Based Systems.- Evaluation of a Web Crowd-Sensing IoT Ecosystem Providing Big Data Analysis.- A Smart City Fighting Pollution by Efficiently Managing and Processing Big Data from Sensor Networks.