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

ISBN-13: 9783319831558 / Angielski / Miękka / 2018 / 516 str.

Florin Pop; Joanna Kolodziej; Beniamino Di Martino

Spis treści

Performance Modeling of Big Data Oriented Architectures
Marco Gribaudo, Mauro Iacono, and Francesco Palmieri

Workflow Scheduling Techniques for Big Data Platforms
Mihaela-Catalina Nita, Mihaela Vasile, Florin Pop, and Valentin Cristea

Cloud Technologies: A New Level for Big Data Mining
Viktor Medvedev and Olga Kurasova

Agent Based High-Level Interaction Patterns for Modeling Individual and Collective Optimizations Problems
Rocco Aversa and Luca Tasquier

Maximize Profit for Big Data Processing in Distributed Datacenters
Weidong Bao, Ji Wang, and Xiaomin Zhu

Energy and Power Efficiency in the Cloud
Michał Karpowicz, Ewa Niewiadomska-Szynkiewicz, Piotr Arabas, and Andrzej Sikora

Context Aware and Reinforcement Learning Based Load Balancing System for Green Clouds
Ionut Anghel, Tudor Cioara, and Ioan Salomie

High-Performance Storage Support for Scientific Big Data Applications on the Cloud
Dongfang Zhao, Akash Mahakode, Sandip Lakshminarasaiah, and Ioan Raicu

Information Fusion for Improving Decision-Making in Big Data Applications
Nayat Sanchez-Pi, Luis Martí, José Manuel Molina, and Ana C. Bicharra Garca

Load Balancing and Fault Tolerance Mechanisms for Scalable and Reliable Big Data Analytics
Nitin Sukhija, Alessandro Morari, and Ioana Banicescu<

Fault Tolerance in MapReduce: A Survey
Bunjamin Memishi, Shadi Ibrahim, María S. Pérez, and Gabriel Antoniu

Big Data Security
Agnieszka Jakóbik

Big Biological Data Management
Edvard Pedersen and Lars Ailo Bongo

Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms
Suejb Memeti, Sabri Pllana, and Joanna Kołodziej

Feature Dimensionality Reduction for Mammographic Report Classification
Agnello Luca, Comelli Albert, and Vitabile Salvatore

Parallel Algorithms for Multi-Relational Data Mining: Application to Life Science Problems
Rui Camacho, Jorge G. Barbosa, Altino Sampaio, João Ladeiras, Nuno A. Fonseca and Vítor S. Costa

Parallelization of Sparse Matrix Kernels for Big Data Applications
Oguz Selvitopi, Kadir Akbudak, and Cevdet Aykanat

Delivering Social Multimedia Content with Scalability
Irene Kilanioti and George A. Papadopoulos

A Java-Based Distributed Approach for Generating Large-Scale Social Network Graphs
Vlad Serbanescu, Keyvan Azadbakht, and Frank de Boer

Predicting Video Virality on Twitter
Irene Kilanioti and George A. Papadopoulos

Big Data uses in Crowd Based Systems
Cristian Chilipirea, Andreea-Cristina Petre, and Ciprian Dobre

Evaluation of a Web Crowd–Sensing IoT Ecosystem Providing Big Data Analysis
Ioannis Vakintis, Spyros Panagiotakis, George Mastorakis, and Constandinos X. Mavromoustakis

A Smart City Fighting Pollution by Efficiently Managing and Processing Big Data from Sensor Networks
Voichiţa Iancu, Silvia Cristina Stegaru, and Dan Ştefan Tudose

O autorze

Dr. Florin Pop is an Associate Professor in the Distributed Systems Laboratory of the Computer Science Department at the University Politehnica of Bucharest, Romania.

Dr. Joanna Kołodziej is a Professor in the Department of Computer Science at Cracow University of Technology, Poland. Amongst her recent publications are the Springer titles Intelligent Agents in Data-intensive Computing and Evolutionary Based Solutions for Green Computing.

Dr. Beniamino Di Martino is a full Professor of Information Systems at the Second University of Naples, Italy. His publications include the Springer titles Cloud Portability and Interoperability and Smart Organizations and Smart Artifacts.

Blurb

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.