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Big Data
Principles and Paradigms
- 1st Edition - June 3, 2016
- Editors: Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 5 3 9 4 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 9 3 4 6 - 7
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… Read more
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Request a sales quoteBig 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.
- 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
Data Scientists, Data Architects, DevOps Engineers, Cloud developers and more. Graduate Data Science students and other academic researchers
- 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
- Chapter 1: BDA = ML + CC
- 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
- Chapter 6: Database Techniques for Big Data
- 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
- Chapter 11: Spatial Privacy Challenges in Social Networks
- 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
- Chapter 14: A Framework for Mining Thai Public Opinions
- Index
- No. of pages: 494
- Language: English
- Edition: 1
- Published: June 3, 2016
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780128053942
- eBook ISBN: 9780128093467
RB
Rajkumar Buyya
Dr. Rajkumar Buyya is Redmond Barry Distinguished Professor and Director of the Cloud Computing and
Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is also serving as the
founding CEO of Manjrasoft, a spin-off company of the University, commercializing its innovations in Cloud
Computing. He has authored over 650 publications and seven textbooks including Mastering Cloud Computing
from McGraw Hill, China Machine Press, and Morgan Kaufmann for Indian, Chinese and international markets
respectively. Dr. Buyya is one of the most highly-cited authors in Computer Sience and Software Engineering
worldwide. “A Scientometric Analysis of Cloud Computing Literature” by German scientists ranked Dr. Buyya
as the World's Top-Cited Author and the World's Most-Productive Author in Cloud Computing. He has been
recognized as a Web of Science “Highly Cited Researcher” for four consecutive years since 2016. Dr. Buyya
was recognized as Scopus Researcher of the Year 2017 with Excellence in Innovative Research Award from
Elsevier; "Lifetime Achievement Awards" from two Indian universities, and the “Best of the World,” in the
Computing Systems field, by The Australian 2019 Research Review. Software technologies for Grid, Cloud, and
Fog computing developed under Dr. Buyya's leadership have gained rapid acceptance and are in use at several
academic institutions and commercial enterprises in 40 countries around the world. Dr. Buyya has led the
establishment and development of key community activities, including serving as foundation Chair of the IEEE
Technical Committee on Scalable Computing and five IEEE/ACM conferences. These contributions and the
international research leadership of Dr. Buyya are recognized through the award of the “2009 IEEE Medal for
Excellence in Scalable Computing” from the IEEE Computer Society TCSC. Manjrasoft's Aneka Cloud
technology developed under his leadership has received the "Frost & Sullivan New Product Innovation Award."
Dr. Buyya served as founding Editor-in-Chief of the IEEE Transactions on Cloud Computing. He is currently
serving as Editor-in-Chief of Software: Practice and Experience, a long-standing journal in the field, established
more than 50 years ago.
Affiliations and expertise
University of Melbourne, AustraliaRC
Rodrigo N. Calheiros
Dr. Rodrigo Calheiros is a Research Fellow in the Department of Computing and Information Systems, The University of Melbourne, Australia. He has been doing contribution in the fields of big data and cloud computing since 2009. He designed and developed CloudSim, an Open Source tool for simulation of cloud platforms used research centers, universities, and companies worldwide.
Affiliations and expertise
Department of Computing and Information Systems, The University of Melbourne, AustraliaAV
Amir Vahid Dastjerdi
Dr. Amir Vahid Dastjerdi is a research fellow with the Cloud Computing and Distributed Systems (CLOUDS) laboratory at the University of Melbourne. He received his PhD in computer science from the University of Melbourne and his areas of interest include Internet of Things, Big data, and cloud computing. He is a technology enthusiast who has over a decade experience in distributed systems.
Affiliations and expertise
Cloud Computing and Distributed Systems (CLOUDS) laboratory, University of Melbourne, AustraliaRead Big Data on ScienceDirect