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Handbook of Research on Organizational Transformations through Big Data Analytics

Book Description

Big data analytics utilizes a wide range of software and analytical tools to provide immediate, relevant information for efficient decision-making. Companies are recognizing the immense potential of BDA, but ensuring the data is appropriate and error-free is the largest hurdle in implementing BDA applications. The Handbook of Research on Organizational Transformations through Big Data Analytics not only catalogues the existing platforms and technologies, it explores new trends within the field of big data analytics (BDA). Containing new and existing research materials and insights on the various approaches to BDA, this publication is intended for researchers, IT professionals, and CIOs interested in the best ways to implement BDA applications and technologies.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  6. Preface
    1. CHAPTER 1
    2. CHAPTER 2
    3. CHAPTER 3
    4. CHAPTER 4
    5. CHAPTER 5
    6. CHAPTER 6
    7. CHAPTER 7
    8. CHAPTER 8
    9. CHAPTER 9
    10. CHAPTER 10
    11. CHAPTER 11
    12. CHAPTER 12
    13. CHAPTER 13
    14. CHAPTER 14
    15. CHAPTER 15
    16. CHAPTER 16
    17. CHAPTER 17
    18. CHAPTER 18
    19. CHAPTER 19
    20. CHAPTER 20
    21. CHAPTER 21
    22. CHAPTER 22
    23. CHAPTER 23
    24. CHAPTER 24
  7. Chapter 1: Business Analytics and Big Data
    1. ABSTRACT
    2. INTRODUCTION
    3. BUSINESS ANALYTICS: WHAT IS IT?
    4. BUSINESS ANALYTICS: METHODS AND TECHNIQUES
    5. BUSINESS ANALYTICS: IMPACT OF BIG DATA
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  8. Chapter 2: Predictive Analytics
    1. ABSTRACT
    2. INTRODUCTION
    3. 1. SIMPLE REGRESSION ANALYSIS
    4. 2. MULTIPLE LINEAR REGRESSION ANALYSIS
    5. 3. POLYNOMIAL REGRESSION
    6. 4. LOGISTIC REGRESSION
    7. 5. DISCRIMINANT ANALYSIS
    8. 6. MULTILEVEL MODELING
    9. FUTURE TRENDS
    10. CONCLUSION
    11. REFERENCES
    12. KEY TERMS AND DEFINITIONS
  9. Chapter 3: Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORKS
    4. 3. APPROACH
    5. 4. EXPERIMENTS AND RESULTS
    6. 5. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  10. Chapter 4: A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. STACKING ENSEMBLE LEARNING
    4. 3. GENETIC ALGORITHM BASED STACKING ENSEMBLE LEARNING
    5. 4. DATA SAMPLING AND DECOMPOSITION
    6. 5. RESULTS AND DISCUSSION
    7. 6. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  11. Chapter 5: Organizational Performance-Design Process
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. DECISION-MAKER'S MODELING ENVIRONMENT
    4. 3. CONSTRUCTION OF POLYNOMIAL LOCAL RESPONSE SURFACE MODEL BY SINGLE-RUN SIMULATION
    5. 4. TARGET-SETTING PROBLEM IN DESIGN
    6. 5. ACCURACY OF THE ESTIMATE
    7. 6. A RECURSIVE SOLUTION ALGORITHM
    8. 7. DESIGN OF A RELIABILITY SUBSYSTEM
    9. 8. SERVICE SYSTEM DESIGN
    10. 9. CONCLUSION AND FUTURE RESEARCH
    11. ACKNOWLEDGMENT
    12. REFERENCES
  12. Chapter 6: Data, Information, and Knowledge
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. BIG DATA
    5. BIG DATA AND KNOWLEDGE
    6. DISCUSSION
    7. FUTURE TRENDS
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  13. Chapter 7: Business Analytics for Business Analysts in Manufacturing
    1. ABSTRACT
    2. INTRODUCTION
    3. CONCLUSION
    4. REFERENCES
    5. KEY TERMS AND DEFINITIONS
  14. Chapter 8: Big Data Transforming Small and Medium Enterprises
    1. ABSTRACT
    2. DEFINITION AND ECONOMIC IMPACT OF SMALL AND MEDIUM ENTERPRISE
    3. THE INFORMATION TECHNOLOGY FOUNDATION FOR BIG DATA TRANSFORMING SMEs
    4. CONCLUSION
    5. REFERENCES
    6. KEY TERMS AND DEFINITIONS
  15. Chapter 9: Nonprofit Fundraising Transformation through Analytics
    1. ABSTRACT
    2. INTRODUCTION
    3. SOCIAL MEDIA ANALYTICS
    4. FINDINGS
    5. CONCLUSION
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  16. Chapter 10: The Role of Business Analytics in Performance Management
    1. ABSTRACT
    2. INTRODUCTION
    3. ROLE OF BUSINESS ANALYTICS IN PERFORMANCE MANAGEMENT
    4. OVERVIEW OF PERFORMANCE MEASUREMENT AND PERFORMANCE MANAGEMENT
    5. CONCLUSION
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  17. Chapter 11: Evaluation of Information Awareness and Understanding through Different Internal Communication Tools
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. THEORETICAL BACKGROUND
    4. 3. RESEARCH
    5. 4. DISCUSSION
    6. 5. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  18. Chapter 12: Empirical Investigation on the Evolution of BI Maturity in Malaysian Organizations
    1. ABSTRACT
    2. INTRODUCTION
    3. BUSINESS INTELLIGENCE
    4. PROPOSED BI MATURITY MODEL
    5. RESEARCH METHODOLOGY
    6. RESULTS OF DATA ANALYSIS
    7. FUTURE TRENDS
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  19. Chapter 13: Cultural Integration with Strategic Decision-Making Process in Determining Innovation Performance
    1. ABSTRACT
    2. INTRODUCTION
    3. THEORETICAL BACKGROUND
    4. HYPOTHESIS DEVELOPMENT
    5. RESEARCH METHODOLOGY
    6. RESULTS
    7. DISCUSSION OF RESULTS
    8. LIMITATIONS AND FUTURE STUDIES
    9. ACKNOWLEDGMENT
    10. REFERENCES
  20. Chapter 14: A Data Warehouse Integration Methodology in Support of Collaborating SMEs
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. DATA WAREHOUSE INTEGRATION
    5. 4. CONCLUSION
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  21. Chapter 15: Business Intelligence, Knowledge Management, and Customer Relationship Management Technological Support in Enterprise Competitive Competence
    1. ABSTRACT
    2. INTRODUCTION
    3. 1. LITERATURE REVIEW
    4. 2. BUILDING INTELLIGENCE ENTERPRISE FRAMEWORK
    5. 3. INTELLIGENCE ENTERPRISE OPERATION
    6. 4. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  22. Chapter 16: Process Improvements in Supply Chain Operations
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. SCM AND ANALYTICAL DATABASES
    4. 3. METHODOLOGY
    5. 4. COMPANY CASE STUDIES
    6. 5. CONCLUSION AND IMPLICATIONS
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  23. Chapter 17: Consumer Information Integration at Pre-Purchase
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS OF THE CHAPTER
    5. STUDY
    6. RESULTS
    7. CONCLUSION
    8. FUTURE RESEARCH DIRECTIONS
    9. REFERENCES
    10. ADDITIONAL READING
    11. KEY TERMS AND DEFINITIONS
    12. APPENDIX A
    13. APPENDIX B
  24. Chapter 18: Using Data Mining Techniques and the Choice of Mode of Text Representation for Improving the Detection and Filtering of Spam
    1. ABSTRACT
    2. INTRODUCTION
    3. STATE OF THE ART
    4. REPRESENTATION OF DATA
    5. THE PROPOSED APPROACH
    6. THE DETECTION AND BAYESIAN FILTERING OF SPAM
    7. RESULTS AND EXPERIMENTS
    8. RESULTS AND DISCUSSION
    9. ERROR RATE BY CLASS
    10. RATE OF GOOD RANKING
    11. PRECISION AND RECALL
    12. ACCURACY
    13. CONCLUSION
    14. ACKNOWLEDGMENT
    15. REFERENCES
  25. Chapter 19: Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. STATE OF THE ART
    4. 3. SYSTEM DESIGN
    5. 4. DATA PROCESSING
    6. 5. RESULTS
    7. 6. CONCLUSION
    8. REFERENCES
    9. KEY WORDS AND DEFINITIONS
  26. Chapter 20: Application of Data Mining Techniques on Library Circulation Data for Library Material Acquisition and Budget Allocation
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORKS
    4. 3. DATABASE MODEL
    5. 4. MODEL DESCRIPTION
    6. 5. RESULTS AND FINDINGS
    7. 6. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  27. Chapter 21: Some Aspects of Estimators for Variance of Normally Distributed Data
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. DIFFERENT RISK CRITERIA AND ESTIMATORS BASED ON THEM
    4. 3. COMPARISON OF THE ESTIMATORS
    5. 4. COMPARISON OF MSE AND VALUES OF DIFFERENT ESTIMATORS AS FUNCTIONS OF
    6. 5. DISCUSSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  28. Chapter 22: An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. METHODS
    4. 3. DATA AND RESULTS
    5. 4. CONCLUSION
    6. ACKNOWLEDGMENT
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  29. Chapter 23: Solving Solid Transportation Problems with Multi-Choice Cost and Stochastic Supply and Demand
    1. ABSTRACT
    2. INTRODUCTION
    3. MATHEMATICAL MODEL
    4. TRANSFORMATION OF AN EQUIVALENT MODEL WITH COST COEFFICIENTS OF OBJECTIVE FUNCTION
    5. NUMERICAL EXAMPLE
    6. RESULT AND DISCUSSION
    7. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  30. Chapter 24: Default Probability Prediction of Credit Applicants Using a New Fuzzy KNN Method with Optimal Weights
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. LITERATURE SURVEY
    4. 3. METHODOLOGY
    5. 4. EMPIRICAL ANALYSIS
    6. 5. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
    9. APPENDIX
  31. Compilation of References
  32. About the Contributors