Resource Management for Big Data Platforms Algorithms, Modelling, and High-Performance Computing Techniques /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Pop, Florin (Επιμελητής έκδοσης), Kołodziej, Joanna (Επιμελητής έκδοσης), Di Martino, Beniamino (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Σειρά: Computer Communications and Networks,
• Θέματα: Computer science.; Computer software > Reusability.; Computer communication systems.; Database management.; Computer simulation.; Computer Science.; Computer Communication Networks.; Simulation and Modeling.; Performance and Reliability.; Database Management.

 This book constitutes a flagship driver towards presenting and supporting advance research in the area of Big Data platforms and applications. Extracting valuable information from raw data is especially difficult considering the velocity of growing data from year to year and the fact that 80% of data is unstructured. In addition, data sources are heterogeneous (various sensors, users with different profiles, etc.) and are located in different situations or contexts. Successful contributions may range from advanced technologies, applications and innovative solutions to global optimization problems in scalable large-scale computing systems to development of methods, conceptual and theoretical models related to Big Data applications and massive data storage and processing. The book provides, in this sense, a platform for the dissemination of advanced topics of theory, research efforts and analysis and implementation for Big Data platforms and applications being oriented on methods, techniques and performance evaluation. This book presents new ideas, analysis, implementations and evaluation of next-generation Big Data platforms and applications. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These subjects represent the main objectives of ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) and the research presented in these chapters was performed by joint collaboration of members from this action. This volume will serve 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 potential solutions for the selected topics. 

 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.