Rajkumar Buyya - Rodrigo Calheiros - Amir Vahid Dastjerdi
Publisher
Morgan Kaufmann
Volume
Copyright
2016
ISBN13
9780128093467
Release
Format
eBook
Grade Level
11th Grade - College Senior
DDC
TBD
Overview
This book explains the key algorithms, tools, technologies, and best practices that enable Big Data platforms for numerous scientific, business, and consumer applications.
Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Contents.
List of Contributors.
About the Editors.
Preface.
Acknowledgments.
Big Data Science.
1: BDA = ML + CC.
2: Real-Time Analytics.
3: Big Data Analytics for Social Media.
4: Deep Learning and Its Parallelization.
5: Characterization and Traversal of Large Real-World Networks.
Big Data Infrastructures and Platforms.
6: Database Techniques for Big Data.
7: Resource Management in Big Data Processing Systems.
8: Local Resource Consumption Shaping: A Case for MapReduce.
9: System Optimization for Big Data Processing.
10: Packing Algorithms for Big Data Replay on Multicore.
Big Data Security and Privacy.
11: Spatial Privacy Challenges in Social Networks.
12: Security and Privacy in Big Data.
13: Location Inferring in Internet of Things and Big Data.
Big Data Applications.
14: A Framework for Mining Thai Public Opinions.
15: A Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather.
16: Dynamic Uncertainty-Based Analytics for Caching Performance Improvements in Mobile Broadband Wireless Networks.
17: Big Data Analytics on a Smart Grid: Mining PMU Data for Event and Anomaly Detection.
18: eScience and Big Data Workflows in Clouds: A Taxonomy and Survey.
