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Enterprise Big Data Engineering, Analytics, and Management

Book Description

The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board
  6. Foreword
  7. Preface
    1. SECTION 1: FOUNDATIONAL ISSUES
    2. SECTION 2: TOOLS AND METHODS
    3. SECTION 3: CASE STUDIES AND APPLICATION AREAS
    4. CONCLUSION
  8. Section 1: Foundational Issues
    1. Chapter 1: How Big Does Big Data Need to Be?
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE OPTIMAL SAMPLE SIZE
      5. CASE STUDY
      6. CONCLUSION
      7. REFERENCES
    2. Chapter 2: Strategic Management of Data and Challenges for Organizations
      1. ABSTRACT
      2. BIG DATA: A NEW PUBLIC DISCOURSE
      3. DATA: A NEW INFORMING RESOURCE
      4. A LITTLE HISTORY: THE EVOLUTION OF TECHNOLOGY
      5. FACING THE FUTURE AND DEVELOPING PRACTICE
      6. REFLECTIONS ON THE CONTEMPORARY SITUATION
      7. REFERENCES
    3. Chapter 3: Data Stream Mining of Event and Complex Event Streams
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA STREAMS
      4. DATA STREAM MINING TECHNIQUES
      5. EVENTS
      6. BIG DATA ANALYTICS ON EVENT STREAMS
      7. DISCUSSION AND CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    4. Chapter 4: Research Challenges in Big Data Analytics
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. BIG DATA ARCHITECTURE
      5. BIG DATA CORE TECHNOLOGIES
      6. BIG DATA ANALYTICS
      7. RESEARCH CHALLENGES IN BIG DATA
      8. POTENTIAL SOLUTIONS TO BIG DATA CHALLENGES
      9. CONCLUSION
      10. REFERENCES
  9. Section 2: Tools and Methods
    1. Chapter 5: Descriptive and Predictive Analytical Methods for Big Data
      1. ABSTRACT
      2. INTRODUCTION
      3. DESCRIPTIVE ANALYTICS
      4. PREDICTIVE ANALYTICS (MODELING)
      5. MULTIPLE LINEAR REGRESSION ANALYSIS
      6. CONCLUSION
      7. REFERENCES
    2. Chapter 6: A Framework to Analyze Enterprise Social Network Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. POSSIBLE DATA ANALYSIS DIMENSIONS AND METRICS
      5. DATA ANALYSIS PROCESS
      6. DATA ANALYTICS FEATURES PROVIDED BY SELECTED ESN PLATFORMS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    3. Chapter 7: Big Data Analytics Using Local Exceptionality Detection
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHOD
      5. RESULTS
      6. CONCLUSION
      7. FUTURE RESEARCH DIRECTIONS
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ENDNOTES
    4. Chapter 8: Statistical Features for Extractive Automatic Text Summarization
      1. ABSTRACT
      2. INTRODUCTION
      3. GENERAL CHALLENGES IN TEXT SUMMARIZATION
      4. BACKGROUND AND ISSUES
      5. FEATURE ENGINEERING
      6. GENERIC SUMMARIZATION PROCESS
      7. RESULTS AND DISCUSSIONS ON DUC 2002 DATASET
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
  10. Section 3: Case Studies and Application Areas
    1. Chapter 9: Data Modeling and Knowledge Discovery in Process Industries
      1. ABSTRACT
      2. INTRODUCTION
      3. A TYPICAL DATA ANALYTICS PROJECT IN PROCESS INDUSTRIES
      4. KNOWLEDGE DISCOVERY OF INDUSTRIAL PLANT DATA
      5. CONCLUSION AND FURTHER RESEARCH
      6. ACKNOWLEDGMENT
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
      9. ENDNOTE
    2. Chapter 10: Data Preparation for Big Data Analytics
      1. ABSTRACT
      2. INTRODUCTION
      3. CONTEXT
      4. METHODS
      5. EXPERIENCES AND LESSONS LEARNED
      6. CONCLUSION
      7. FUTURE RESEARCH DIRECTIONS
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
      11. ENDNOTE
    3. Chapter 11: Semantification of Large Corpora of Technical Documentation
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PREREQUISITES
      5. SEMANTIFICATION OF TECHNICAL DOCUMENTS
      6. SOLUTIONS AND RECOMMENDATIONS (CASE STUDIES)
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
      11. ENDNOTE
    4. Chapter 12: Application of Complex Event Processing Techniques to Big Data Related to Healthcare
      1. ABSTRACT
      2. INTRODUCTION/BACKGROUND
      3. COMPLEX EVENT PROCESSING (CEP)
      4. EVENT QUERY LANGUAGES
      5. RESEARCH APPROACH
      6. SUMMARY OF FINDINGS:
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    5. Chapter 13: Using Big Data in Collaborative Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. BIG DATA
      4. COLLABORATIVE LEARNING
      5. ANALYTICS
      6. SYSTEMATIC REVIEW OF CASE STUDIES
      7. ANALYSIS OF RESULTS
      8. DISCUSSION
      9. CONCLUSION
      10. REFERENCES
  11. Compilation of References
  12. About the Contributors