You are previewing Big Data: Concepts, Methodologies, Tools, and Applications.
O'Reilly logo
Big Data: Concepts, Methodologies, Tools, and Applications

Book Description

The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. Big Data: Concepts, Methodologies, Tools, and Applications is a multi-volume compendium of research-based perspectives and solutions within the realm of large-scale and complex data sets. Taking a multidisciplinary approach, this publication presents exhaustive coverage of crucial topics in the field of big data including diverse applications, storage solutions, analysis techniques, and methods for searching and transferring large data sets, in addition to security issues. Emphasizing essential research in the field of data science, this publication is an ideal reference source for data analysts, IT professionals, researchers, and academics.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editor-in-Chief
    2. Associate Editors
    3. Editorial Advisory Board
  5. Preface
  6. Section 1: Fundamental Concepts and Theories
    1. Chapter 1: Big Data Overview
      1. ABSTRACT
      2. INTRODUCTION
      3. BIG DATA DEFINITION
      4. BIG DATA CHARACTERISTICS
      5. BIG DATA VALUE
      6. BIG DATA STRUCTURE
      7. BIG DATA ANALYTICS
      8. THE BIG DATA ECOSYSTEM
      9. REFERENCES
    2. Chapter 2: Big Data Predictive and Prescriptive Analytics
      1. ABSTRACT
      2. INTRODUCTION
      3. PREDICTIVE ANALYTICS
      4. PRESCRIPTIVE ANALYTICS
      5. BIG DATA ANALYTICS
      6. IN-DATABASE ANALYTICS
      7. ANALYTICES AS A SERVICE (AaaS)
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Big Data Computing and the Reference Architecture
      1. ABSTRACT
      2. INTRODUCTION
      3. BIG DATA ANALYTICS
      4. BIG DATA TECHNOLOGY (ECKERSON, 2012)
      5. BIG DATA ANALYSIS IN CLOUD: STORAGE, NETWORK AND SERVER CHALLENGES (SCARPATI, 2012)
      6. A WIDE RANGE OF DATA ANALYTICS
      7. THE BIG DATA REFERENCE ARCHITECTURE (BDRA)
      8. CHALLENGES AND OPPORTUNITIES WITH BIG DATA ANALYTICS AGRAWAL, BARBARA, BERNSTEIN, BERTINO, DAVIDSON, DAYAL, ET AL., 2012)
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    4. Chapter 4: A Holistic View of Big Data
      1. ABSTRACT
      2. INTRODUCTION
      3. PROCESS
      4. DATA MINING TECHNOLOGIES
      5. DATABASE PLATFORMS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    5. Chapter 5: A Review of RDF Storage in NoSQL Databases
      1. ABSTRACT
      2. INTRODUCTION
      3. RDF MODEL AND RDF DATA STORAGE
      4. ACKNOWLEDGMENT
      5. REFERENCES
      6. ADDITIONAL READING
      7. KEY TERMS AND DEFINITIONS
      8. ENDNOTES
    6. Chapter 6: On Efficient Acquisition and Recovery Methods for Certain Types of Big Data
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BIG DATA
      4. 3. EFFICIENT ACQUISITION AND RECOVERY
      5. 4. CONCLUSION
      6. REFERENCES
    7. Chapter 7: Big Data and Data Modelling for Manufacturing Information Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BIG DATA AND DATA MODELLING FOR MANUFACTURING INFORMATION SYSTEMS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    8. Chapter 8: Information Visualization and Policy Modeling
      1. ABSTRACT
      2. INTRODUCTION
      3. ABSTRACT POLICY MODELING STEPS
      4. FOUNDATIONS OF INFORMATION VISUALIZATION
      5. DATA FOUNDATIONS
      6. VISUALIZATION TECHNIQUES
      7. VISUAL INTERACTION
      8. SEMANTICS VISUALIZATION
      9. VISUALIZATION OF SOCIAL DATA AS SEMANTIC INFORMATION
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
      15. ENDNOTES
    9. Chapter 9: The Performance Mining Method
      1. ABSTRACT
      2. INTRODUCTION
      3. SOFTWARE PERFORMANCE EVALUATION
      4. RESEARCH METHODOLOGY
      5. HYPOTHESES FOR PERFORMANCE ANALYSIS
      6. PERFORMANCE VARIABLES
      7. DATA MINING FOR PERFORMANCE ANALYSIS
      8. PERFORMANCE MINING METHOD
      9. EVALUATION OF THE PERFORMANCE MINING METHOD
      10. CASE STUDY
      11. DISCUSSION AND CONCLUSION
      12. REFERENCES
    10. Chapter 10: Aspects Regarding Detection of Sentiment in Web Content
      1. ABSTRACT
      2. OVERVIEW
      3. APPLICATIONS OF SENTIMENT ANALYSIS
      4. DATA SOURCES
      5. CLASSIFICATION OF ANALYSIS
      6. METHODOLOGY
      7. CASE STUDY
      8. CONSIDERATIONS ON PERFORMED ANALYSIS
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
    11. Chapter 11: The Interoperability of US Federal Government Information
      1. ABSTRACT
      2. INTRODUCTION
      3. INTEROPERABILITY
      4. INFORMATION POLICY
      5. AUTHORITY-TRACKER MODEL
      6. DATA SOURCES
      7. METHODS
      8. AN EXAMPLE: FINANCIAL CRISIS POLICY
      9. EVALUATION
      10. RESULTS
      11. DISCUSSION
      12. CONCLUSION
      13. ACKNOWLEDGMENT
      14. REFERENCES
    12. Chapter 12: Big Data and Web Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. DEFINING THE MP VIA LOGICAL DECISION TREE
      4. LDT TO BDD CONVERSION
      5. NEW APPROACH TO REDUCE THE COMPUTATIONAL COST
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. APPENDIX
    13. Chapter 13: Big Data Security Management
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BIG DATA SECURITY MANAGEMENT
      5. RESEARCH ISSUES AND DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    14. Chapter 14: Feature-Based Uncertainty Visualization
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. AN INTERACTIVE FEATURE-BASED VISUALIZATION OF SCALAR FIELD UNCERTAINTY
      5. 4. A FEATURE-BASED FRAMEWORK FOR VISUALIZING VECTOR FIELD UNCERTAINTY
      6. 5. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    15. Chapter 15: Big Data – Small World
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA COLLECTION, BIG DATA
      4. MAKING TECHNOLOGIES
      5. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    16. Chapter 16: Philosophising Data
      1. ABSTRACT
      2. 1. INTRODUCTION AND BACKGROUND
      3. 2. BIG DATA: THE NEW OIL OR FOOLS’ GOLD?
      4. 3. BIGGER DATA = BIGGER PROBLEMS?
      5. 4. CONCLUSION
      6. REFERENCES
  7. Section 2: Development and Design Methodologies
    1. Chapter 17: Big Data Architecture
      1. ABSTRACT
      2. INTRODUCTION
      3. TECHNICAL AND TECHNOLOGICAL ADVANCEMENTS
      4. PROGRAMMING ENVIRONMENT FOR BIG DATA
      5. APACHE HADOOP
      6. HADOOP DISTRIBUTED FILE SYSTEM
      7. YARN (MAP REDUCE 2)
      8. NEO4J
      9. GraphChi
      10. TRINITY
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
    2. Chapter 18: Big Data at Scale for Digital Humanities
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. HATHITRUST RESEACH CENTER
      5. DISCOVERY AND ACCESS
      6. SERVICES MANAGEMENT
      7. DATA MANAGEMENT
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. REFERENCES
      11. ENDNOTES
    3. Chapter 19: Construct a Bipartite Signed Network in YouTube
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. HYPOTHESES AND TOPIC PARTICIPANTS
      5. SENTIMENT ANALYSIS
      6. CONSTRUCTION OF NETWORKS
      7. EXPERIMENTS AND RESULTS
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. ENDNOTES
    4. Chapter 20: Rapid Development of Service-Based Cloud Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND AND RELATED WORK
      4. MOTIVATING SCENARIO
      5. HIGH LEVEL OVERVIEW OF THE FRAMEWORK
      6. TECHNICAL DESIGN
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
    5. Chapter 21: Modeling Big Data Analytics with a Real-Time Executable Specification Language
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. REALSPEC FOR BIG DATA ANALYTICS
      5. DISCUSSION AND FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    6. Chapter 22: A Hierarchical Security Model for Multimedia Big Data
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. THE PROPOSED HIERARCHICAL SECURITY MODEL
      5. SECURITY MODEL APPLICATION AND EVALUATION
      6. CONCLUSION
      7. REFERENCES
    7. Chapter 23: Big Data Warehouse Automatic Design Methodology
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. METHODOLOGY
      5. 4. REQUIREMENT ANALYSIS
      6. 5. SOURCE ANALYSIS AND INTEGRATION
      7. 6. CONCEPTUAL DESIGN
      8. 7. CASE STUDY
      9. 8. FUTURE RESEARCH
      10. 9. CONCLUSION
      11. REFERENCES
      12. ADDITIONAL READING
      13. KEY TERMS AND DEFINITIONS
    8. Chapter 24: Visualization in Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. VISUAL PERCEPTION
      5. VISUALIZATION METHODS IN INFORMATION ARCHITECTURE
      6. VISUALIZATION IN LEARNING
      7. TRENDS IN VISUALIZATION
      8. SUMMARY AND CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
      13. ENDNOTES
    9. Chapter 25: A Cognitive Analytics Management Framework (CAM-Part 3)
      1. ABSTRACT
      2. INTRODUCTION
      3. GLOBAL EDUCATION CHALLENGES AND TRENDS
      4. COGNITIVE ANALYTICS MANAGEMENT EDUCATION TRENDS AND FUTURE
      5. CONCLUSION
      6. ACKNOWLEDGMENT
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    10. Chapter 26: Big Data Paradigm for Healthcare Sector
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. ELECTRONIC HEALTH RECORDS
      6. HEALTH DATA STREAMS
      7. SOURCES OF BIG DATA IN HEALTH CARE
      8. MHEALTH: GENERATING BIG DATA STREAMS
      9. BIG DATA PARADIGM FOR EHRs
      10. MAP-REDUCE MODELLING FOR BIG DATA WORLD
      11. QUERYING BIG DATA WORLD
      12. DATA MINING FOR EHRs
      13. PROPOSED MAP-REDUCE MODELLING FOR CLUSTERING HEALTH DATA STREAMS
      14. SOLUTIONS AND RECOMMENDATIONS
      15. FUTURE RESEARCH DIRECTIONS
      16. CONCLUSION
      17. REFERENCES
      18. KEY TERMS AND DEFINITIONS
    11. Chapter 27: Knowledge as a Service Framework for Collaborative Data Management in Cloud Environments - Disaster Domain
      1. ABSTRACT
      2. INTRODUCTION
      3. MOTIVATING SCENARIO
      4. BACKGROUND
      5. DISASTER-CDM FRAMEWORK
      6. DATA MANAGEMENT FOR FILE-STYLE DATA
      7. CASE STUDY
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
    12. Chapter 28: Building a Visual Analytics Tool for Location-Based Services
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DOMAIN PROBLEM SPECIFICATION
      5. EXPERIMENT
      6. DISCUSSION
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
  8. Section 3: Tools and Technologies
    1. Chapter 29: Data Intensive Cloud Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. PROGRAMMING ABSTRACTIONS FOR DATA INTENSIVE APPLICATIONS
      4. DATA DISTRIBUTION AND REPLICATION
      5. RESOURCE PROVISIONING
      6. SCHEDULING
      7. RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    2. Chapter 30: Techniques for Sampling Online Text-Based Data Sets
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SAMPLING AND BIG DATA SETS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 31: Sentiment Analysis for Health Care
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. HEALTH CARE SENTIMENT DATA
      5. HEALTH CARE ANALYSIS
      6. OUTCOME OF SENTIMENT ANALYSIS
      7. NLP CHALLENGES
      8. ASPECT BASED SENTIMENT ANALYSIS
      9. DISCUSSION AND RECOMMENDATIONS
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
    4. Chapter 32: Restful Web Service and Web-Based Data Visualization for Environmental Monitoring
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. CURRENT EMPLOYED SENSOR SYSTEM AND NCCP DATA
      5. 4. ARDUINO WITH RESTFUL WEBSERVICE
      6. 5. WEB SERVICE FOR SENSOR NETWORK
      7. 7. DATA VISUALIZATION
      8. 8. WEB VISUALIZATION TOOLS
      9. 9. PERFORMANCE TESTINF OF VISUALIZATION
      10. 10. CONCLUSION AND FUTURE WORKS
      11. ACKNOWLEDGMENT
      12. REFERENCES
    5. Chapter 33: Web Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. WEB INTELLIGENCE
      4. BIG DATA AND THE UNCERTAINTY
      5. FRAMEWORK OVERVIEW
      6. EVALUATION
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
      11. APPENDIX: EVALUATION QUERIES
    6. Chapter 34: Parallel Data Reduction Techniques for Big Datasets
      1. ABSTRACT
      2. INTRODUCTION
      3. GENERAL REDUCTION TECHNIQUES
      4. PARALLEL DATA REDUCTION ALGORITHMS
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    7. Chapter 35: The IT Readiness for the Digital Universe
      1. ABSTRACT
      2. INTRODUCTION
      3. ENVISIONING THE DIGITAL UNIVERSE
      4. BIG DATA IN 2020
      5. BIG DATA ANALYTICS: THE IT INFRASTRUCTURE CHARACTERISTICS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    8. Chapter 36: Big Data in Telecommunications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. NETWORK TRAFFIC STEERING WITH CROWD INTELLIGENCE
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    9. Chapter 37: Big Data Computing Strategies
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. CLOUD COMPUTING FOR BIG DATA
      4. 3. ESTABLISHED STRATEGIES TO PROCESS BIG DATA
      5. 4. BIG DATA APPLICATION IN DIFFERENT DOMAIN
      6. CASE STUDY 1
      7. CASE STUDY 2
      8. CASE STUDY 3
      9. 5. BIG DATA BIOMETRICS
      10. 6. BIG DATA ANALYTICS INSIGHT TO VALUE
      11. 7. CONCLUSION
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
    10. Chapter 38: Big Data and National Cyber Security Intelligence
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BIG DATA OPPORTUNITIES FOR NATIONAL CYBER INTELLIGENCE
      4. 3. NATIONAL SECURITY ENVIRONMENT: THREATS OF BIG DATA
      5. 4. EXPLOITING BIG DATA FOR NATIONAL SECURITY - CHALLENGES
      6. 5. BIG DATA ANALYTICS FOR CRITICAL INFRASTRUCTURE PROTECTION (CIP)
      7. 6. TOOLS AND TECHNOLOGIES
      8. 7. EXISTING BIG DATA APPROACHES FOR NATIONAL SECURITY
      9. 8. CONCLUSION AND SUMMARY
      10. REFERENCES
    11. Chapter 39: A Cloud-Aware Distributed Object Storage System to Retrieve Large Data via HTML5-Enabled Web Browsers
      1. ABSTRACT
      2. INTRODUCTION
      3. HIGH PERFORMANCE STORAGE SYSTEMS
      4. CASE STUDY: A CLOUD-AWARE OBJECT STORAGE SYSTEM (CADOS)
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    12. Chapter 40: Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 1.1 RELATED WORK
      4. 2. THE CONSIDERED VNetDC PLATFORM
      5. 3. OPTIMAL ALLOCATION OF THE VIRTUAL RESOURCES
      6. 4. ADAPTIVE IMPLEMENTATION OF THE OPTIMAL SCHEDULER
      7. 5. PROTOTYPE AND PERFORMANCE EVALUATION
      8. 6. ONGOING DEVELOPMENTS AND HINTS FOR FUTURE RESEARCH
      9. 7. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
      12. ENDNOTES
      13. APPENDIX A: DERIVATIONS OF EQUATIONS (21.1)-(23)
      14. APPENDIX B: PROOF OF PROPOSITION 5
    13. Chapter 41: Essentiality of Machine Learning Algorithms for Big Data Computation
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE SURVEY
      4. BIG DATA ANALYTICS
      5. COMPUTATIONAL FRAMEWORK
      6. APPLICATIONS OF MACHINE LEARNING IN DIFFERENT INDUSTRIES
      7. MACHINE LEARNING IN HADOOP
      8. A BIG DATA FRAMEWORK FOR CATEGORIZING TECHNICAL SUPPORT REQUESTS USING LARGE SCALE MACHINE LEARNING
      9. CONCLUSION
      10. REFERENCES
    14. Chapter 42: Web Usage Mining and the Challenge of Big Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE WEB USAGE MINING PROCESS (MAIN FOCUS)
      5. CHALLENGES AND RESEARCH ISSUES (FUTURE DIRECTIONS)
      6. FUTURE RESEARCH DIRECTION
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    15. Chapter 43: Pattern Recognition for Large-Scale Data Processing
      1. ABSTRACT
      2. INTRODUCTION
      3. LARGE SCALE AND BIG DATA RECOGNITION
      4. PATTERN RECOGNITION
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    16. Chapter 44: Expressing Data, Space, and Time with Tableau Public™
      1. ABSTRACT
      2. INTRODUCTION
      3. REVIEW OF THE LITERATURE
      4. TABLEAU PUBLIC: THE TOOL
      5. CREATING DATA VISUALIZATIONS WITH TABLEAU PUBLIC
      6. SOME REAL-WORLD EXAMPLES
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    17. Chapter 45: Graph Mining and Its Applications in Studying Community-Based Graph under the Preview of Social Network
      1. ABSTRACT
      2. INTRODUCTION
      3. DEFINITIONS AND NOTATIONS ON GRAPH THEORY
      4. ON BIG DATA
      5. ASPECTS OF GRAPH MINING
      6. ANALYSIS ON THE PROPOSED APPROACH
      7. GRAPH REPRESENTATION TECHNIQUES
      8. SEQUENTIAL REPRESENTATION
      9. APIORI AND NON-PRIORI BASED ALGORITHMS
      10. APRIORI-BASED APPROACH
      11. PATTERN GROWTH APPROACH
      12. AUTHORS PROPOSED APPROACH
      13. OUTPUT FOR THE EXAMPLE
      14. MERGING OF GRAPHS
      15. AUTHORS PROPOSED APPROACH
      16. EXAMPLE AND ANALYSIS FOR AUTHORS PROPOSED APPROACH
      17. CRITERIA FOR GROUPING
      18. NODES-CENTRIC COMMUNITY DETECTION
      19. GROUP-CENTRIC COMMUNITY DETECTION
      20. NETWORK-CENTRIC COMMUNITY DETECTION
      21. HIERARCHY-CENTRIC COMMUNITY DETECTION
      22. GROUP DETECTION
      23. TMODS EXTENSIONS TO STANDARD GRAPH MATCHING
      24. ALGORITHMS
      25. AUTHORS ANALYSIS AND WORK
      26. SOME ISSUES ON GRAPH ANALYTICS
      27. APPLICATION OF GRAPH ANALYTICS
      28. AN EXAMPLE ON USE OF GRAPH ANALYTICS
      29. AUTHORS OBSERVATIONS AND PREDICTIONS
      30. CONCLUSION
      31. REFERENCES
  9. Section 4: Utilization and Application
    1. Chapter 46: A Survey of Big Data Analytics Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. CATEGORIES OF ANALYTICAL PROCESSING SYSTEMS
      4. TRANSACTIONAL RELATIONAL DATABASE MANAGEMENT SYSTEMS
      5. HADOOP DISTRIBUTIONS
      6. NOSQL DATABASES
      7. ANALYTIC PLATFORMS
      8. BIG DATA PLATFORM
      9. SURVEY ON BIG DATA PLATFORMS
      10. IBM BIG DATA PLATFORM
      11. IBM NETEZZA ANALYTICS: HIGH PERFORMANCE ANALYTIC PLATFORM
      12. GREENPLUM UNIFIED ANALYTICS PLATFORM: THE ANSWER TO AGILE (EMC PERSPECTIVE PURSUING THE AGILE ENTERPRISE, 2012; & A WHITE PAPER FROM EMC: BIG DATA AS A SERVICE, 2013)
      13. HP VERTICA PLATFORM WITH ‘R’
      14. SAs PLATFORMS FOR HIGH PERFORMANCE
      15. SAP HANA–IN-MEMORY COMPUTING PLATFORM
      16. 1010 DATA OFFERS BIG DATA AS A SERVICE: CLOUD-BASED BIG DATA ANALYTICS PLATFORM
      17. INTELLICUS: POWER TO UNDERSTAND YOUR BUSINESS (BUSINESS INSIGHTS PLATFORM)
      18. INFORMATICA: A PLATFORM POWERED BY POWERED VBM
      19. TERADATA ASTER’S BIG ANALYTICS APPLIANCE
      20. ORACLE BIG DATA APPLIANCE
      21. KOGNITIO OFFERS THREE APPLIANCE SPEEDS AND VIRTUAL CUBES
      22. MICROSOFT APPLIANCE SCALES OUT SQL SERVER WITH PDW
      23. SAND TECHNOLOGY – COLUMNAR SYSTEMS: A WORLD’S HIGHEST PERFORMING ENTERPRISE ANALYTIC DATABASE PLATFORM
      24. INFOBRIGHT CUTS DBA LABOR AND QUERY TIMES
      25. PARACCEL COMBINES COLUMN-STORE, MPP AND IN-DATABASE ANALYTICS
      26. ALPINE DATA LAB: PREDICTIVE ANALYTICS BUILT FOR BIG DATA
      27. CONNECTING TO ORACLE
      28. SAS AND IBM ARE UNSHAKEABLE LEADERS, WHILE NEWCOMER SAP PERFORMS WELL
      29. ALTERYX DESKTOP-TO-CLOUD SOLUTIONS: THE NEW APPROACH TO STRATEGIC ANALYTICS SOLUTION
      30. TOOLS/PRODUCTS
      31. GREENPLUM IS NOW PIVOTAL-A NEW PLATFORM FOR THE NEW ERA
      32. IBM WEAVES BROCADE INTO BIG DATA FABRIC
      33. IMPETUS ECOSYSTEM
      34. GREENPLUM CHORUS: PRODUCTIVITY ENGINE
      35. ACTIAN DATARUSH: ANALYTICS ENGINE FOR PARALLEL DATA PROCESSING (ANALYTICS ENGINE FOR PARALLEL DATA PROCESSING: ACTIAN DATARUSH, 2013)
      36. SPLUNK ENGINE
      37. BIG DATA ANALYTICS IN NETWORK
      38. MAVERICK FABRIC
      39. JUNIPER NETWORKS
      40. BIG DATA ANALYTICS CAN BOOST NETWORK SECURITY
      41. WAN OPTIMIZATION FOR BIG DATA AND BIG DATA ANALYTICS
      42. VENDORS WHO ARE LESS KNOWN BUT DOES A GREAT JOB IN BIG DATA ANALYTICS
      43. CONCLUSION
      44. REFERENCES
      45. KEY TERMS AND DEFINITIONS
    2. Chapter 47: Aesthetics in Data Visualization
      1. ABSTRACT
      2. INTRODUCTION: DATA VISUALIZATION DESIGN
      3. CURRENT TOOLS AND PROBLEMS IN AESTHETICS OF DATA VISUALIZATION
      4. CASE STUDIES OF AESTHETICS IN DATA VISUALIZATION
      5. CONCLUSION
      6. REFERENCES
    3. Chapter 48: Chinese Text Sentiment Analysis Utilizing Emotion Degree Lexicon and Fuzzy Semantic Model
      1. ABSTRACT
      2. INTRODUCTION
      3. CHINESE FUZZY SEMANTIC MODEL
      4. BUILD EMOTION DEGREE LEXICON BASED ON SOCIAL COGNITIVE THEORIES
      5. CHINESE TEXT SENTIMENT ANALYSIS
      6. EXPERIMENTS
      7. REMAINING CHALLENGES
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
    4. Chapter 49: A Distributed and Scalable Solution for Applying Semantic Techniques to Big Data
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. THE M.A.R.A. FRAMEWORK
      5. PROBLEM STATEMENT
      6. PROBLEM FORMULATION
      7. SOFTWARE ARCHITECTURE
      8. TECHNOLOGICAL STACK
      9. PROOF OF CONCEPT
      10. EXPERIMENTAL RESULTS
      11. CONCLUSION AND FUTURE WORK
      12. ACKNOWLEDGMENT
      13. REFERENCES
    5. Chapter 50: Evaluating NoSQL Databases for Big Data Processing within the Brazilian Ministry of Planning, Budget, and Management
      1. ABSTRACT
      2. INTRODUCTION
      3. BIG DATA
      4. BIG DATA TECHNOLOGIES
      5. NoSQL Databases
      6. CASE STUDY
      7. RESULTS AND DISCUSSION
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    6. Chapter 51: Analysis of Genomic Data in a Cloud Computing Environment
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FUTURE RESEARCH DIRECTION: UTILIZING CLOUD COMPUTING TO ANALYZE SNP6 ARRAYS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
      11. APPENDIX
    7. Chapter 52: Applying the Fuzzy Analytical Network Process in Digital Marketing
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. FUTURE RESEARCH DIRECTIONS
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    8. Chapter 53: Application of Big Data in Healthcare
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCLUSION
      4. REFERENCES
      5. ENDNOTE
    9. Chapter 54: Shaping Content Strategies with User Analytics and Identities
      1. ABSTRACT
      2. INTRODUCTION
      3. STRATEGIES WITH USER ANALYTICS AND IDENTITIES
      4. CONCLUSION
      5. REFERENCES
      6. KEY TERMS AND DEFINITIONS
    10. Chapter 55: Improving Healthcare with Data-Driven Track-and-Trace Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. A TRACK AND TRACE SYSTEM FOR PHARMACEUTICAL SUPPLY CHAINS
      4. A TRACK AND TRACE SYSTEM FOR INDOOR MONITORING
      5. TRACK-AND TRACE SYSTEMS: DATA-RELATED CHALLENGES
      6. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    11. Chapter 56: Big Data Applications in Healthcare
      1. ABSTRACT
      2. INTRODUCTION OF BIG DATA
      3. BACKGROUND: BIG DATA ANALYTICS IN HEALTHCARE INDUSTRY
      4. MAIN FOCUS OF THE CHAPTER
      5. PENETRATION OF BIG DATA ANALYTICS IN PHARMACEUTICAL INDUSTRY
      6. FUTURE OF BIG DATA IN PHARMACEUTICAL AND HEALTH CARE
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    12. Chapter 57: Evaluation of Topic Models as a Preprocessing Engine for the Knowledge Discovery in Twitter Datasets
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. RESEARCH APPROACH
      5. PREPROCESSING FRAMEWORK FOR SENTIMENT ANALYSIS IN TWITTER
      6. EVALUATION
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    13. Chapter 58: Network Text Analysis and Sentiment Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE SOCIAL NETWORK ANALYSIS
      5. NETWORK TEXT ANALYSIS AND THE ANALYSIS OF WORD-OF-MOUTH IN THE DIGITAL MARKETPLACE
      6. THE PINK BEER CASE STUDY
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    14. Chapter 59: Big Data and Web Intelligence for Condition Monitoring
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BIG DATA IN STRUCTURAL HEALTH MONITORING
      5. PROPOSED METHODOLOGY
      6. BIG DATA AND CLOUD COMPUTING
      7. PROPOSED INFRASTRUCTURE
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    15. Chapter 60: Towards Harnessing Phone Messages and Telephone Conversations for Prediction of Food Crisis
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. THE PROPOSED STRATEGY
      5. 4. DISCUSSION
      6. 5. CONCLUSION AND FUTURE WORK
      7. REFERENCES
    16. Chapter 61: Towards Security Issues and Solutions in Cognitive Radio Networks
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. DETECTION AND MITIGATION OF SECURITY THREATS IN CRN
      5. 4. FUTURE DIRECTIONS
      6. 5. CONCLUSION
      7. REFERENCES
    17. Chapter 62: Classification of Failures in Photovoltaic Systems using Data Mining Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA MINING AND RENEWABLE ENERGIES
      4. UNDERSTANDING THE PROBLEM
      5. DATA MINING TECHNIQUES AND THE PERFORMANCE OF PHOTOVOLTAIC GENERATOR
      6. RESULTS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    18. Chapter 63: Costs of Information Services and Information Systems
      1. ABSTRACT
      2. INTRODUCTION AND BACKGROUND
      3. COST TERMINOLOGY AND CONFUSION
      4. THE SPECIFIC BUSINESS DECISION SHOULD DEFINE WHICH COSTS ARE RELEVANT AND WHICH ARE NOT
      5. TOTAL COST OF OWNERSHIP
      6. THE IMPLICATIONS OF INFORMATION SERVICES HAVING PUBLIC GOOD-LIKE CHARACTERISTICS
      7. PERISHABILITY OF SERVICES AND THE IMPLICATION FOR COSTS AND DECISIONS
      8. NETWORK EFFECTS
      9. BIG DATA
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. ACKNOWLEDGMENT
      13. REFERENCES
      14. ADDITIONAL READING
      15. KEY TERMS AND DEFINITIONS
      16. ENDNOTES
      17. APPENDIX
    19. Chapter 64: Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. PROPERTIES OF BIG DATA
      4. 3. CONVENTIONAL VOTING METHODS AND POTENTIAL PITFALLS IN SOCIOLOGY
      5. 4. IDENTIFICATION OF THE CHARACTERISTIC EQUILIBRIUM OF COLLECTIVE OPINIONS VIA BIG DATA ANALYTICS
      6. 5. FUZZY METHODOLOGY FOR COLLECTIVE OPINION ELICITATION AND ANALYSIS BASED ON BIG DATA
      7. 6. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
  10. Section 5: Organizational and Social Implications
    1. Chapter 65: Blending Technology, Human Potential, and Organizational Reality
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MANAGING DATA POWER BY BLENDING IN PEOPLE AND ORGANISATION
      5. SOLUTIONS AND RECOMMENDATIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
    2. Chapter 66: The Role of Geo-Demographic Big Data for Assessing the Effectiveness of Crowd-Funded Software Projects
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. A GEO-DEMOGRAPHIC ANALYSIS OF BIG DATA CONNECTED WITH ADVERTISING OF CROWD-FUNDING PROJECTS THROUGH THE CONTEMPORARY PRISM OF INTERNET TROLLING
      5. GENERALISING THE DATA
      6. IMPLICATIONS AND FUTURE RESEARCH DIRECTIONS
      7. DISCUSSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 67: Big Data Analytics in Retail Supply Chain
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. EARLY RESEARCH
      5. PROBLEM DEFINITION
      6. METHODOLOGY
      7. FINDINGS AND DISCUSSION
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 68: The Impact of Big Data on Security
      1. ABSTRACT
      2. INTRODUCTION
      3. “BIG DATA” TERM
      4. BIG DATA AND TECHNOLOGY INNOVATION
      5. BIG DATA AND COMMERCIAL VALUE
      6. THE ‘DATA RUSH’
      7. BIG DATA MEANING
      8. OPPORTUNITIES OF BIG DATA RESEARCH
      9. CHALLENGES OF BIG DATA RESEARCH
      10. SAMPLING AND DATA COLLECTION
      11. BIG DATA MEASUREMENT
      12. DATA ANALYSIS AND INFERENCES
      13. INTERPRETATION AND THEORETICAL IMPLICATIONS
      14. THE BIG DATA IN CLOUD COMPUTING
      15. BIG DATA IMPACTS BUSINESS ENTERPRISES
      16. TYPES AND SOURCES OF BIG DATA
      17. THE ROLE OF BIG DATA IN SECURITY
      18. SECURITY AWARENESS IN BIG DATA ENVIROMNEMTS
      19. FUNDAMENTIALS TO IMPROVE SECURITY IN BIG DATA ENVIRONMENTS
      20. BIG DATA SECURITY AND PRIVACY CHALLENGES
      21. SECURITY ISSUES IN BIG DATA
      22. USING BIG DATA FOR BUSINESS RESEARCH
      23. BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT
      24. BIG DATA AND MOBILE PHONES
      25. CONCLUSION
      26. REFERENCES
      27. KEY TERMS AND DEFINITIONS
    5. Chapter 69: The Role of Big Data in Radiation Oncology
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BIG DATA IN RADIOTHERAPY
      5. ISSUES AND RECOMMENDATIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    6. Chapter 70: Inventory Control and Big Data
      1. ABSTRACT
      2. INVENTORY CONTROL: THE CONCEPT
      3. MANUAL INVENTORY CONTROL SYSTEMS
      4. AUTOMATED INVENTORY CONTROL SYSTEM
      5. INVENTORY MANAGEMENT SOFTWARES AND GENERATION OF BIG DATA
      6. INTRODUCTION OF BIG DATA
      7. CONCLUSION
      8. REFERENCES
    7. Chapter 71: Synchronizing Execution of Big Data in Distributed and Parallelized Environments
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PROBLEMS IN DEALING WITH BIG DATA OVER DISTRIBUTED SYSTEMS
      5. SYNCHRONOUS PARALLELIZATION THROUGH LOAD BALANCING
      6. CASE STUDY
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    8. Chapter 72: Visualization of Human Behavior Data
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. WHAT IS THE QUANTIFIED SELF?
      4. 3. ROOTS OF QUANTIFIED SELF MOVEMENT
      5. 4. QUANTIFIED SELF PROMISES
      6. 5. CURRENT APPLICATION FIELD OF QUANTIFIED SELF
      7. 6. QUANTIFIED SELF FUNDAMENTAL PROBLEMS
      8. 7. TOWARD A MORE ROBUST APPROACH IN QUANTIFIED SELF
      9. 8. FUTURE RESEARCH DIRECTIONS
      10. 9. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    9. Chapter 73: Generational Differences Relative to Data-Based Wisdom
      1. ABSTRACT
      2. INTRODUCTION
      3. TECHNOLOGY: AND EVERYTHING THAT MAKES IT WORK
      4. LEADERSHIP: THE STYLES AND ROLES THEY PLAY
      5. CULTURE: THEN AND NOW
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    10. Chapter 74: Big Data Sharing Among Academics
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA SHARING PRACTICES AND TRENDS
      4. CASE STUDIES: DISCIPLINARY REPOSITORIES
      5. FUTURE RESEARCH DIRECTIONS AND CONCLUSION
      6. REFERENCES
      7. ADDITIONAL READING
      8. KEY TERMS AND DEFINITIONS
      9. ENDNOTES
    11. Chapter 75: Bringing the Arts as Data to Visualize How Knowledge Works
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PRESENTING FINDINGS FROM NAEP DATA EXPLORER, REPORTS, AND SECONDARY USE DATA
      5. SOLUTIONS AND RECOMMENDATIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    12. Chapter 76: The Role of Knowledge Management (KM) in Aged Care Informatics
      1. ABSTRACT
      2. INTRODUCTION
      3. THE AUSTRALIAN AGED CARE SECTOR
      4. AGED CARE AND KM
      5. DATA AND METHOD
      6. PROBLEMS IDENTIFIED
      7. RECOMMENDATIONS AND CONTRIBUTIONS
      8. CONCLUDE FUTURE RESEARCH AND IMPLEMENTATION CONSIDERATIONS
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
      11. APPENDIX
    13. Chapter 77: The Impact of Virtualization on High Performance Computing Clustering in the Cloud
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. VIRTUALIZATION AND CLOUD COMPUTING: THE RELATIONSHIP
      5. HPCAAS PRIVATE CLOUD DEPLOYMENT
      6. EXPERIMENTATION
      7. RESULTS AND ANALYSIS
      8. CONCLUSION AND FUTURE WORK
      9. REFERENCES
    14. Chapter 78: The Sentiment Revealed in Social Networks during the Games of the Brazilian Team in the 2014 World Cup
      1. ABSTRACT
      2. INTRODUCTION
      3. METHODOLOGY
      4. ANALYSIS FEELINGS THROUGH SOCIAL NETWORKS
      5. HOW TO CHECK THE FEELING IN THIS METHOD?
      6. PROCESS ANALYSIS, COLLECTION AND DISPLAY OF DATA
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ENDNOTES
    15. Chapter 79: Here Be Dragons
      1. ABSTRACT
      2. INTRODUCTION
      3. ENGAGING WITH DATA AS CONSTRUCT
      4. DATA, BLACK BOXES, AND ALGORITHMS
      5. ENGAGING WITH STUDENT DATA AS CONSTRUCT
      6. STUDENT DATA AND STUDENT RESPONSIBILITY
      7. STUDENT-CENTRED LEARNING ANALYTICS
      8. STUDENT DATA, AGENCY, AND PRIVACY SELF-MANAGEMENT
      9. A CASE STUDY OF OPEN UNIVERSITY POLICY
      10. GENERAL PRINCIPLES FOR A NEW DEAL ON STUDENT DATA IN LEARNING ANALYTICS
      11. CONCLUSION
      12. REFERENCES
      13. ENDNOTES
  11. Section 6: Managerial Impact
    1. Chapter 80: Business Process Improvement through Data Mining Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. RATIONALE
      4. METHODOLOGY
      5. APPLICATION
      6. RESULTS
      7. DISCUSSION
      8. REFERENCES
    2. Chapter 81: Big Collusion
      1. ABSTRACT
      2. INTRODUCTION
      3. BIG COLLUSION
      4. DIGITAL SURVEILLANCE STATE
      5. CORPORATIONS
      6. CONSUMERS
      7. BREAKING THE CYCLE
      8. CONCLUSION
      9. REFERENCES
    3. Chapter 82: 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
    4. Chapter 83: SLOD-BI
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. A SOCIAL BI DATA INFRASTRUCTURE
      5. 4. SLOD-BI DATASETS
      6. 5. DATA PROVISIONING
      7. 6. EVALUATION
      8. 7. AN EXAMPLE OF SOCIAL ANALYSIS WITH SLOD-BI
      9. 8. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
    5. Chapter 84: An Innovation Ecosystem beyond the Triple Helix Model
      1. ABSTRACT
      2. INTRODUCTION
      3. SOCIAL INNOVATION
      4. HOW TO DEVELOP AND FOSTER SOCIAL INNOVATION IN AN ICT ECOSYSTEM
      5. A NEW MODEL TO DEVELOP SOCIAL INNOVATION: THE SOCIAL INNOVATION PYRAMID
      6. TRENTINO ECOSYSTEM AND TRENTO RISE
      7. TRENTORISE FLAGSHIP PROJECTS: OPEN AND BIG DATA PROJECT IN TRENTINO, SMART CAMPUS, AND SMART CROWD
      8. CONCLUSION AND FUTURE DIRECTIONS
      9. REFERENCES
    6. Chapter 85: Digital Businesses
      1. ABSTRACT
      2. INTRODUCTION
      3. REVIEW OF CURRENT LITERATURE
      4. CHANGES IN THE SOCIETY
      5. ROLE OF ENTERPRISE INFORMATION SYSTEMS
      6. DATA MANAGEMENT
      7. EVOLUTION OF BUSINESS
      8. TECHNOLOGICAL ADVANCES
      9. ENTERPRISE 2.0 AND ROI
      10. BIG DATA
      11. RESEARCH GAP
      12. RESEARCH QUESTIONS
      13. THE FRAMEWORK
      14. PROPOSED DATA COLLECTION FOR THE STUDY
      15. ACADEMIC AND PRACTICAL IMPLICATIONS
      16. CONCLUSION
      17. LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH
      18. REFERENCES
      19. APPENDIX
    7. Chapter 86: Big Data Analytics Demystified
      1. ABSTRACT
      2. INTRODUCTION
      3. THE UNWRAPPING OF BIG DATA COMPUTING
      4. BIG DATA CHARACTERISTICS
      5. WHY BIG DATA COMPUTING?
      6. BIG DATA CONCERNS AND CHALLENGES
      7. INTRODUCING BIG DATA ANALYTICS
      8. BIG DATA ANALYTICS FRAMEWORKS AND INFRASTRUCTURE
      9. BIG DATA ANALYTICS USE CASES
      10. TRADITIONAL DW ANALYTICS VS. BIG DATA ANALYTICS
      11. MACHINE DATA ANALYTICS BY SPLUNK
      12. BIG DATA MIDDLEWARE SOLUTIONS
      13. A DETAILED LOOK ON DATA INTEGRATION
      14. BIG DATA VISUALIZATION
      15. SUMMARY
      16. REFERENCES
      17. ADDITIONAL READING
      18. KEY TERMS AND DEFINITIONS
  12. Section 7: Critical Issues
    1. Chapter 87: What Does Learning Look Like?
      1. ABSTRACT
      2. INTRODUCTION
      3. UNDERSTANDING DATA VISUALIZATION THROUGH AN ARTIST’S EYE
      4. UNDERPINNINGS AND GROUNDWORK
      5. NETWORK VISUALIZATION AND DESIGN THINKING
      6. IDEAS AND STRATEGIES FOR VISUALIZING PATTERNS OF VISUAL ART LEARNING
      7. HOPEFUL RESEARCH VISIONS OF LEARNING DATA VISUALIZATION
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
      13. ENDNOTES
    2. Chapter 88: Machine Learning Approaches for Sentiment Analysis
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. MACHINE LEARNING APPROACHES FOR SENTIMENT ANALYSIS
      4. 3. STANDARD DATASETS
      5. 4. EVALUATION MECHANISM
      6. 5. PERFORMANCE COMPARISONS OF VARIOUS METHODS
      7. 6. FUTURE RESEARCH DIRECTIONS
      8. 7. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 89: From Data Quality to Big Data Quality
      1. ABSTRACT
      2. INTRODUCTION
      3. METHODOLOGY
      4. CONCEPTUAL FRAMEWORK FOR ANALYZING THE EVOLUTION OF QUALITY IN BIG DATA
      5. ANALYSIS OF EVOLUTION LINES
      6. EVOLUTION OF QUALITY DIMENSIONS IN DATA TYPES
      7. SEMI-STRUCTURED TEXTS
      8. LINKED OPEN DATA
      9. EVOLUTION OF QUALITY DIMENSIONS IN APPLICATION DOMAINS
      10. CONCLUSION
      11. REFERENCES
    4. Chapter 90: Deconstructing Smart Cities
      1. ABSTRACT
      2. THE CONTEXT
      3. THE ROUTINE FUNCTIONING OF CITIES: BIG DATA, REAL-TIME SENSING, AND INTEGRATED SYSTEMS
      4. PLANNING THE SMART CITY IN THE SHORT AND LONG TERM: FUTURE CITIES
      5. IS THERE A SCIENCE OF SMART CITIES?
      6. ACKNOWLEDGMENT
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    5. Chapter 91: Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. LITERATURE REVIEW
      4. 3. METHODOLOGY
      5. 4. EXPERIMENTATION AND RESULTS
      6. 5. CONCLUSION AND FUTURE WORK
      7. REFERENCES
      8. ENDNOTES
    6. Chapter 92: Towards an Intelligent Integrated Approach for Clinical Decision Support
      1. ABSTRACT
      2. INTRODUCTION
      3. DECISION SUPPORT SYSTEM
      4. RESEARCH DIRECTION
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    7. Chapter 93: Issues Related to Network Security Attacks in Mobile Ad Hoc Networks (MANET)
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. COMMUNICATION IN MOBILE AD HOC NETWORK
      5. 4. ADVANTAGES OF MOBILE AD HOC NETWORK
      6. 5. APPLICATIONS OF MOBILE AD HOC NETWORK
      7. 6. GENERAL ISSUES IN MOBILE AD HOC NETWORK
      8. 7. PRESENT TRENDS AND CHALLENGES IN MANET
      9. 8. VULNERABILITIES IN MANET
      10. 9. LITERATURE SURVEY AND RELATED WORK
      11. 10. RESEARCH GAP
      12. 11. SECURITY ASPECTS IN MOBILE AD HOC NETWORKS
      13. 12. MOTIVATION
      14. 13. NEED OF STUDY
      15. 14. NEED OF SECURITY IN MANET
      16. 15. PROBLEM STATEMENT
      17. 16. OBJECTIVES AND SCOPE
      18. 17. SCOPE OF RESEARCH
      19. 18. FORMULATION OF HYPOTHESIS
      20. 19. RESEARCH METHODOLOGY
      21. 20. ATTACKS IN MOBILE AD HOC NETWORK
      22. 21. SECURITY ARCHITECTURE FOR MANETS:
      23. 22. COUNTERMEASURES
      24. 23. FUTURE DIRECTIONS
      25. 24. CONCLUSION
      26. REFERENCES
      27. KEY TERMS AND DEFINITIONS
      28. APPENDIX: LIST OF APPREVIATIONS
    8. Chapter 94: A Data Mining Approach for Risk Assessment in Car Insurance
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. REVIEW OF LITERATURE
      4. 3. METHODOLOGY
      5. 4. DATA ANALYSIS AND RESULTS
      6. 5. ANALYSIS AND DISCUSSION OF THE RESULTS
      7. 6. CONCLUSION
      8. REFERENCES
    9. Chapter 95: Scientific Data Management and Visualization
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SCIENTIFIC DATA MANAGEMENT AND VISUALIZATION
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
    10. Chapter 96: Challenges and Opportunities in Big Data Processing
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. NOSQL DATABASES
      6. BIG DATA WITH NoSQL AND HADOOP
      7. TOWARDS NEW ARCHITECTURES FOR BIG DATA PROCESSING
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    11. Chapter 97: Efficient Metaheuristic Approaches for Exploration of Online Social Networks
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BIG DATA ANALYTIC IN SOCIAL NETWORKS
      4. 3. MATHEMATICAL MODELS
      5. 4. PROPOSED METAHEURISTIC METHODS
      6. 5. COMPUTATIONAL RESULTS
      7. 6. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    12. Chapter 98: From Data to Vision
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BIG DATA IN THE PUBLIC SECTOR
      5. OPPORTUNITIES AND CHALLENGES
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
  13. Section 8: Emerging Trends
    1. Chapter 99: The New “ABC” of ICTs (Analytics + Big Data + Cloud Computing)
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE NEW “ABC” OF ICTS: (A)NALYTICS + (B)IG DATA + (C)LOUD COMPUTING
      5. THE EVOLUTION, APPLICATIONS AND EMERGING RESEARCH RELATED TO THE NEW “ABC’ OF ICTS
      6. THE MAIN PLAYERS IN THE INDUSTRY CREATED BY THE NEW “ABC” OF ICTS
      7. THE NEW “ABC” TECHNOLOGIES, THE AGG MODEL, AND THE IMPACT OF THE TRADE-OFF BETWEEN INFORMATION TECHNOLOGY (IT) AND COMMUNICATION TECHNOLOGY (CT) COSTS ON THE FIRM’S ORGANIZATION
      8. FINAL CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    2. Chapter 100: Scalable Data Mining, Archiving, and Big Data Management for the Next Generation Astronomical Telescopes
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. SCIENCE AND BIG DATA CHALLENGES IN NEXT GENERATION ASTRONOMICAL INSTRUMENTS
      4. 3. BIGDATA TECHNOLOGIES FROM THE APACHE SOFTWARE FOUNDATION
      5. 4. MAIN FOCUS OF THE CHAPTER
      6. 5. FUTURE RESEARCH DIRECTIONS AND CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
    3. Chapter 101: The Disruptive Impact of Emerging Technology
      1. ABSTRACT
      2. BIG DATA AND DIGITAL DISRUPTION
      3. WE ARE DATA STORAGE
      4. INNOVATION’S UNRULY SIDE
      5. JOURNALISM’S COMPELLING INTEREST
      6. A NOT-SO-FINAL-WORD
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    4. Chapter 102: Electronic Records Management - An Old Solution to a New Problem
      1. ABSTRACT
      2. INTRODUCTION
      3. SYSTEMATIC REVIEW OF LITERATURE
      4. LITERATURE REVIEW FINDINGS
      5. ANALYSIS OF THE LINKS BETWEEN GOV 2.0, BIG DATA, AND ERM LITERATURE
      6. DISCUSSION
      7. CONCLUSION
      8. REFERENCES
      9. ENDNOTES
      10. APPENDIX
    5. Chapter 103: On the Road to Ephesus
      1. ABSTRACT
      2. INTRODUCTION
      3. ON THE ROAD TO DATA-BASED WISDOM
      4. THE IMPORTANCE OF LEADERSHIP IN THE PURSUIT OF DATA-BASED WISDOM
      5. INSIGHT FROM HEALTHCARE PROFESSIONALS
      6. PROPOSED FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
      11. ENDNOTES
    6. Chapter 104: Big Data Literacy
      1. ABSTRACT
      2. INTRODUCTION
      3. THE CHALLENGES OF BIG DATA
      4. BIG DATA LITERACY
      5. BARRIERS IN ACQUIRING EXPERTISE IN BIG DATA
      6. BIG DATA LITERACY AND IMPLICATION TO THE SOCIETY
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    7. Chapter 105: Personalized Disease Phenotypes from Massive OMICs Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ANALYZING AND VISUALIZING BIG OMICS DATA USING SELF ORGANIZING MAPS
      5. CASE STUDY: GENE EXPRESSION AND -METHYLATION PHENOTYPES OF THE HEALTHY AND DISEASED HUMAN COLON
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    8. Chapter 106: Global Trends in Mobile
      1. ABSTRACT
      2. 1. A MOBILE WORLD: SCALING UP2
      3. 2. CHANGING PATTERNS OF PRODUCTION AND CREATION
      4. CONCLUSION: “TARZAN ECONOMICS”?
      5. REFERENCES
      6. KEY TERMS AND DEFINITIONS
      7. ENDNOTES
    9. Chapter 107: Future Research Directions in E-Tourism Studies
      1. ABSTRACT
      2. NEED OF CONCEPTUAL ANALYSIS OF HOSPITALITY USING BIG-DATA ANALYSIS METHOD
      3. POVERTY OF QUEUING DISCIPLINE IN TOURISM SERVICE
      4. MANAGERIAL AND ADMINISTRATIVE CLIMATE REGARDING FEMALE WORKFORCE
      5. MANAGERIAL AND ADMINISTRATIVE CLIMATE REGARDING FOREIGN WORKFORCE
      6. FOREIGN AND ETHNIC FOOD TOURISM
      7. RISK INFORMATION TRANSPARENCY AND THE LACK OF SAFETY PROTECTION CLIMATE FOR TOURISTS
      8. FROM POLITE AND RESPECTFUL RECEPTION TO DEFENSIVE OR OFFENSIVE REJECTION OF CUSTOMER’S CLAIMS
      9. DISASTER RECOVERY ASSISTING TOURISM WITH BOTTOM-UP NATIONAL MOVEMENT
      10. RECOMMENDATION
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
    10. Chapter 108: Advanced Dimensionality Reduction Method for Big Data
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. MAIN FOCUS OF THE CHAPTER
      5. 4. EXPERIMENTATION AND RESULT ANALYSIS
      6. 5. FUTURE RESEARCH DIRECTION
      7. 6. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    11. Chapter 109: Social Media Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. REVIEW OF SCHOLARLY ARTICLES
      4. SMM APPLICATION IN DIFFERENT INDUSTRIES
      5. CONCLUSION
      6. REFERENCES
      7. APPENDIX
    12. Chapter 110: From Personal to Mobile Healthcare
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MHEALTH: CURRENT PICTURE
      5. TOWARDS MOBILE HEALTHCARE
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    13. Chapter 111: Future Networked Healthcare Systems
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. STATE OF THE ART
      4. 3. NETWORK ARCHITECTURE AND METHODOLOGY
      5. 4. NETWORKED HEALTHCARE APPLICATIONS: SIMULATIONS AND ANALYSIS
      6. 5. CONCLUSION AND FUTURE WORK
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    14. Chapter 112: Emerging Role of Big Data in Public Sector
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BIG DATA APPLICATIONS: COMPARISON OF BUSINESSES AND GOVERNMENTS
      4. 3. BIG DATA APPLICATION AREAS IN PUBLIC SECTOR
      5. 4. UNIQUE COMPUTING REQUIREMENTS OF PUBLIC SECTOR
      6. 5. BIG-DATA APPLICATIONS IN LEADING E-GOVERNMENTS
      7. 6. PRACTICAL/MANAGERIAL IMPLICATIONS AND RECOMMENDATIONS
      8. 7. CONCLUSION
      9. REFERENCES