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Index
Cover
Title Page
Copyright Page
Book Series
Mission
Coverage
Editorial Advisory Board and List of Reviewers
List of Reviewers
Preface
INTRODUCTION
MOTIVATION
ORGANIZATION OF THE BOOK
REFERENCES
Acknowledgment
Chapter 1: What Is Open Source Software (OSS) and What Is Big Data?
ABSTRACT
INTRODUCTION: HOW OPEN SOURCE SOFTWARE, FREE SOFTWARE, AND FREEWARE DIFFER
INTRODUCTION: WHAT IS BIG DATA?
CHARACTERISTICS OF BIG DATA
THE INTERFACE OF COMPONENTS OF BIG DATA STACK
OPEN SOURCE SOFTWARE PLATFORMS FOR CLOUD AND FOG COMPUTING
BIG DATA ANALYTICS SOFTWARE AND PLATFORMS
BIG DATA VISUALIZATION
BIG DATA VISUALIZATION SOFTWARE
BIG DATA VISUALIZATION FOR STREAMING DATA
BIG DATA VISUALIZATION SOFTWARE FOR MOBILE DEVICES AND TABLETS
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
KEY TERMS AND DEFINITIONS
APPENDIX
Chapter 2: Open Source Software (OSS) for Big Data
ABSTRACT
OPEN SOURCE SOFTWARE AND TECHNOLOGY FOR BIG DATA
OPEN SOURCE HADOOP-RELATED PROJECTS
BIG DATA SMACK
OPPORTUNITIES FOR BIG DATA AND OPEN SOURCE SOFTWARE
STATISTICAL OPEN SOURCE SOFTWARE (OSS) FOR BIG DATA
CHALLENGES FOR BIG DATA AND OPEN SOURCE SOFTWARE (OSS)
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 3: Introduction to the Popular Open Source Statistical Software (OSSS)
ABSTRACT
INTRODUCTION
BACKGROUND
PYTHON
GRETL (GNU REGRESSION, ECONOMETRICS, AND TIME-SERIES LIBRARY)
SOFA (STATISTICS OPEN FOR ALL) STATISTICS
OCTAVE
KNIME (KONSTANZ INFORMATION MINER)
SCILAB
CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 4: Cluster Analysis in R With Big Data Applications
ABSTRACT
INTRODUCTION
BACKGROUND
CLUSTERING WITH RNA-SEQUENCING DATA
FUTURE RESEARCH DIRECTIONS
CONCLUSION
REFERENCES
ADDITIONAL READING
KEY TERMS AND DEFINITIONS
Chapter 5: Generalized Linear Model for Automobile Fatality Rate Prediction in R
ABSTRACT
INTRODUCTION
BACKGROUND
R WORKING ENVIRONMENT
DESCRIPTIVE ANALYSIS
MODEL CONSTRUCTION
MODEL VALIDATION
CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 6: Introduction to Python and Its Statistical Applications
ABSTRACT
BACKGROUND: HISTORY OF PYTHON
IDEs AND CODE EDITORS
PYTHON LIBRARIES FOR STATISTICAL ANALYSIS
APPLICATION: COLLISION DATA ANALYSIS
CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
APPENDIX 1
Chapter 7: A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python
ABSTRACT
INTRODUCTION
BACKGROUND AND LITERATURE REVIEW
DATA EXPLORATION AND PRE-PROCESSING
BASE MODELS
MACHINE LEARNING MODELS
EVALUATION METRICS
BASE MODEL EVALUATION
MACHINE LEARNING MODEL EVALUATION
CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
Conclusion
Related Readings
About the Contributors
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