Applied Big Data Analytics in Operations Management - Applied Big Data Analytics in Operations Management -

Preface Big Data in Operation Management LITERATURE SURVEY RESEARCH ROADMAP INTRODUCTION TO BIG DATA BIG DATA OPERATION MANAGEMENT Real Time Data Collection Real Time Event Processing Comprehensive Data Collection Deterministic Data Collection VARIOUS APPROACHES AND USE CASES TO BIG DATA OPERATION MANAGEMENT Splunk Approach to Big Data Operations Management Reflex System Approach to Big Data Operation Management The Cloud Physics Approach to Big Data Operation Management Xangati Approach to Big Data Operation Management COMPARATIVE ANALYSES OF VARIOUS APPROACHES CONCLUSION REFERENCES ADDITIONAL READING KEY TERMS AND DEFINITIONS Application of Artificial Neural Networks in Predicting the Degradation of Tram Tracks Using Maintenance Data INTRODUCTION Background Statement of Problem Research Objectives TRAJECTORY DATA Inspection Data Load Data Impact of Repair DATA ANALYSIS Track Deterioration Profile Over Time Impact of Repair on Deterioration Profile Impact of Other Factors on Deterioration Continuous Variables Categorical Variables RESEARCH METHODOLOGY Artificial Neural Networks A Model to Predict Gauge Widening RESULTS CONCLUSION AND RECOMMENDATIONS FUTURE RESEARCH DIRECTIONS REFERENCES ADDITIONAL READING KEY TERMS AND DEFINITIONS ZAMREN Big Data Management (ZAMBiDM) Envisaging Efficiency and Analytically Manage IT Resources Statement of the Problem Objective of the ZAMBiDM Organisation of the Chapter LITERATURE REVIEW Big Data Data Management Big Data Management Big Data Strategies Big Data Analytics Big Data Virtualisation Zambia Research Education Network (ZAMREN)THE PROPOSED ZAMBiDM MODEL The ZAMREN Big Data and Data Management Component ZAMBiDM Virtualisation ZAMBiDM Strategies and Operational Processes/Nodes Analytic Tools Data Quality Tools Elevate Data to Executive Level Road Map Manage Big Data IMPLEMENTATION OF ZAMBiDM CONCLUSION REFERENCES Predictive Analytics in Operations Management PREDICTIVE ANALYTICS TYPE Predictive Models Descriptive Models Decision Models PREDICTION IN OPERATIONS MANAGEMENT RELATED WORK ANALYTICAL TECHNIQUES Regression Techniques Machine Learning Techniques MAPREDUCE REGRESSION TECHNIQUES Linear Regression Model MACHINE LEARNING TECHNIQUES Parallel Backpropagation Implementation Parallel Support Vector Machine RECENT RESEARCHES CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS Pros and Cons of Applying Opinion Mining on Operation Management: A Big Data Perspective PROS AND CONS FRAMEWORK Fake Reviews, Leading to Opinion Frauds Threats from Illegitimate Data SENTIMENT ANALYSIS IN OPERATION MANAGEMENT USING BIG DATA CONCLUSION AND FUTURE RESEARCH REFERENCES ADDITIONAL READING KEY TERMS AND DEFINITIONS A Conceptual Framework for Educational System Operation Management Synchronous with Big Data Approach OPERATIONAL MANAGEMENT IN EDUCATIONAL SYSTEM ADOPTION OF ICT OPERATIONS IN EDUCATIONAL SYSTEM Operations Based on Educational Management Information System Operations Based on Learning Management System COMPLEXITIES IN EXISTING EDUCATIONAL OPERATIONAL MANAGEMENT Complexities in Data Management EXISTING OPERATIONAL TECHNIQUES TO STORE/PROCESS BIGDATA About BigData Storage Benefits of BigData Storage Obstructions towards BigData Usage Existing Operational Tools in Educational BigData SIGNIFICANT ISSUES IN EXISTING OPERATIONAL TECHNIQUES CONCEPTUALIZED FRAMEWORK OF NOVEL EDUCATIONAL OPERATIONAL MANAGEMENT Proposed Methodology Possible Advantages of Conceptual Framework Research Implication Benefits and Disadvantage of Conceptual Framework in Operation Management CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS Management of SME’s Semi Structured Data Using Semantic Technique BACKGROUND Characteristics of Big Data SEMANTIC WEB LANGUAGES SYNTHETIC SEMANTIC DATA MANAGEMENT Properties Inference Rule SPARQL SSDM Implementation Step Development of Core Ontology Finding of Synthesis Data and Convert to RDB Development of Domain Ontology a. Mapping Relational Schema to Ontology b. Mapping Attributes c. Mapping Constraints d. Data Extraction SOLUTION CONCLUSION REFERENCES An Overview of Big Data Security with Hadoop Framework INTRODUCTION BIG DATA TECHNOLOGIES WORKING OF HADOOP FRAMEWORK PAST RESEARCHES ON BIG DATA SECURITY SECURITY ISSUES AND CHALLENGES RELATED TO BIG DATA SECURITY SOLUTIONS NEEDED FOR BIG DATA a. Authentication b. Authorization c. Accountability d. Data Protection e. Integrity Verification SUMMARY RESEARCH SCOPE REFERENCES ADDITIONAL READING KEY TERMS AND DEFINITIONS