Big Data - Edition 1 - Edited by Rajkumar Buyya, Ph.D., Rodrigo N. Calheiros and Amir Vahid Dastjerdi Elsevier Inspection Copies

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Big Data,

Edition 1 Principles and Paradigms

Edited by Rajkumar Buyya, Ph.D., Rodrigo N. Calheiros and Amir Vahid Dastjerdi

Publication Date: 03 Jun 2016

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Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications.

To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.

Key Features

Covers computational platforms supporting Big Data applications
Addresses key principles underlying Big Data computing
Examines key developments supporting next generation Big Data platforms
Explores the challenges in Big Data computing and ways to overcome them
Contains expert contributors from both academia and industry

About the author

Edited by Rajkumar Buyya, Ph.D., University of Melbourne; Rodrigo N. Calheiros, Department of Computing and Information Systems, The University of Melbourne, Australia and Amir Vahid Dastjerdi, Cloud Computing and Distributed Systems (CLOUDS) laboratory, University of Melbourne, Australia

List of contributors
About the Editors
Preface
- Organization of the Book
- Part I: Big Data Science
- Part II: Big Data Infrastructures and Platforms
- Part III: Big Data Security and Privacy
- Part IV: Big Data Applications
Acknowledgments
Part I: Big Data Science
- Chapter 1: BDA = ML + CC
  - Abstract
  - 1.1 Introduction
  - 1.2 A Historical Review of Big Data
  - 1.3 Historical Interpretation of Big Data
  - 1.4 Defining Big Data From 3Vs to 3²Vs
  - 1.5 Big Data Analytics and Machine Learning
  - 1.6 Big Data Analytics and Cloud Computing
  - 1.7 Hadoop, HDFS, MapReduce, Spark, and Flink
  - 1.8 ML + CC → BDA and Guidelines
  - 1.9 Conclusion
- Chapter 2: Real-Time Analytics
  - Abstract
  - 2.1 Introduction
  - 2.2 Computing Abstractions for Real-Time Analytics
  - 2.3 Characteristics of Real-Time Systems
  - 2.4 Real-Time Processing for Big Data — Concepts and Platforms
  - 2.5 Data Stream Processing Platforms
  - 2.6 Data Stream Analytics Platforms
  - 2.7 Data Analysis and Analytic Techniques
  - 2.8 Finance Domain Requirements and a Case Study
  - 2.9 Future Research Challenges
- Chapter 3: Big Data Analytics for Social Media
  - Abstract
  - Acknowledgments
  - 3.1 Introduction
  - 3.2 NLP and Its Applications
  - 3.3 Text Mining
  - 3.4 Anomaly Detection
- Chapter 4: Deep Learning and Its Parallelization
  - Abstract
  - 4.1 Introduction
  - 4.2 Concepts and Categories of Deep Learning
  - 4.3 Parallel Optimization for Deep Learning
  - 4.4 Discussions
- Chapter 5: Characterization and Traversal of Large Real-World Networks
  - Abstract
  - Acknowledgments
  - 5.1 Introduction
  - 5.2 Background
  - 5.3 Characterization and Measurement
  - 5.4 Efficient Complex Network Traversal
  - 5.5 k-Core-Based Partitioning for Heterogeneous Graph Processing
  - 5.6 Future Directions
  - 5.7 Conclusions
Part II: Big Data Infrastructures and Platforms
- Chapter 6: Database Techniques for Big Data
  - Abstract
  - 6.1 Introduction
  - 6.2 Background
  - 6.3 NoSQL Movement
  - 6.4 NoSQL Solutions for Big Data Management
  - 6.5 NoSQL Data Models
  - 6.6 Future Directions
  - 6.7 Conclusions
- Chapter 7: Resource Management in Big Data Processing Systems
  - Abstract
  - 7.1 Introduction
  - 7.2 Types of Resource Management
  - 7.3 Big Data Processing Systems and Platforms
  - 7.4 Single-Resource Management in the Cloud
  - 7.5 Multiresource Management in the Cloud
  - 7.6 Related Work on Resource Management
  - 7.7 Open Problems
  - 7.8 Summary
- Chapter 8: Local Resource Consumption Shaping: A Case for MapReduce
  - Abstract
  - 8.1 Introduction
  - 8.2 Motivation
  - 8.3 Local Resource Shaper
  - 8.4 Evaluation
  - 8.5 Related Work
  - 8.6 Conclusions
  - Appendix CPU Utilization With Different Slot Configurations and LRS
- Chapter 9: System Optimization for Big Data Processing
  - Abstract
  - 9.1 Introduction
  - 9.2 Basic Framework of the Hadoop Ecosystem
  - 9.3 Parallel Computation Framework: MapReduce
  - 9.4 Job Scheduling of Hadoop
  - 9.5 Performance Optimization of HDFS
  - 9.6 Performance Optimization of HBase
  - 9.7 Performance Enhancement of Hadoop System
  - 9.8 Conclusions and Future Directions
- Chapter 10: Packing Algorithms for Big Data Replay on Multicore
  - Abstract
  - 10.1 Introduction
  - 10.2 Performance Bottlenecks
  - 10.3 The Big Data Replay Method
  - 10.4 Packing Algorithms
  - 10.5 Performance Analysis
  - 10.6 Summary and Future Directions
Part III: Big Data Security and Privacy
- Chapter 11: Spatial Privacy Challenges in Social Networks
  - Abstract
  - Acknowledgments
  - 11.1 Introduction
  - 11.2 Background
  - 11.3 Spatial Aspects of Social Networks
  - 11.4 Cloud-Based Big Data Infrastructure
  - 11.5 Spatial Privacy Case Studies
  - 11.6 Conclusions
- Chapter 12: Security and Privacy in Big Data
  - Abstract
  - 12.1 Introduction
  - 12.2 Secure Queries Over Encrypted Big Data
  - 12.3 Other Big Data Security
  - 12.4 Privacy on Correlated Big Data
  - 12.5 Future Directions
  - 12.6 Conclusions
- Chapter 13: Location Inferring in Internet of Things and Big Data
  - Abstract
  - Acknowledgements
  - 13.1 Introduction
  - 13.2 Device-based Sensing Using Big Data
  - 13.3 Device-free Sensing Using Big Data
  - 13.4 Conclusion
Part IV: Big Data Applications
- Chapter 14: A Framework for Mining Thai Public Opinions
  - Abstract
  - Acknowledgments
  - 14.1 Introduction
  - 14.2 XDOM
  - 14.3 Implementation
  - 14.4 Validation
  - 14.5 Case Studies
  - 14.6 Summary and Conclusions
- Chapter 15: A Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather
  - Abstract
  - Acknowledgments
  - 15.1 Background
  - 15.2 Big Data System Components
  - 15.3 Machine-Learning Methodology
  - 15.4 System Implementation
  - 15.5 Key Findings
  - 15.6 Summary and Conclusions
- Chapter 16: Dynamic Uncertainty-Based Analytics for Caching Performance Improvements in Mobile Broadband Wireless Networks
  - Abstract
  - 16.1 Introduction
  - 16.2 Background
  - 16.3 Related Work
  - 16.4 VoD Architecture
  - 16.5 Overview
  - 16.6 Data Generation
  - 16.7 Edge and Core Components
  - 16.8 INCA Caching Algorithm
  - 16.9 QoE Estimation
  - 16.10 Theoretical Framework
  - 16.11 Experiments and Results
  - 16.12 Synthetic Dataset
  - 16.13 Conclusions and Future Directions
- Chapter 17: Big Data Analytics on a Smart Grid: Mining PMU Data for Event and Anomaly Detection
  - Abstract
  - Acknowledgments
  - 17.1 Introduction
  - 17.2 Smart Grid With PMUs and PDCs
  - 17.3 Improving Traditional Workflow
  - 17.4 Characterizing Normal Operation
  - 17.5 Identifying Unusual Phenomena
  - 17.6 Identifying Known Events
  - 17.7 Related Efforts
  - 17.8 Conclusion and Future Directions
- Chapter 18: eScience and Big Data Workflows in Clouds: A Taxonomy and Survey
  - Abstract
  - 18.1 Introduction
  - 18.2 Background
  - 18.3 Taxonomy and Review of eScience Services in the Cloud
  - 18.4 Resource Provisioning for eScience Workflows in Clouds
  - 18.5 Open Problems
  - 18.6 Summary
Index

ISBN: 9780128053942

Page Count: 494

Retail Price : £62.99

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