Big Data Quantification for Complex Decision-Making

✍ Scribed by Zhang Chao

• Publisher: Engineering Science Reference
• Year: 2024
• Tongue: English
• Leaves: 328
• Series: premier reference source
• Category: Library

✦ Synopsis

Many professionals are facing a monumental challenge: navigating the intricate landscape of information to make impactful choices. The sheer volume and complexity of big data have ushered in a shift, demanding innovative methodologies and frameworks. Big Data Quantification for Complex Decision-Making tackles this challenge head-on, offering a comprehensive exploration of the tools necessary to distill valuable insights from datasets. This book serves as a tool for professionals, researchers, and students, empowering them to not only comprehend the significance of big data in decision-making but also to translate this understanding into real-world decision making. The central objective of the book is to examine the relationship between big data and decision-making. It strives to address multiple objectives, including understanding the intricacies of big data in decision-making, navigating methodological nuances, managing uncertainty adeptly, and bridging theoretical foundations with real-world applications. The book's core aspiration is to provide readers with a comprehensive toolbox, seamlessly integrating theoretical frameworks, practical applications, and forward-thinking perspectives. This equips readers with the means to effectively navigate the data-rich landscape of modern decision-making, fostering a heightened comprehension of strategic big data utilization. Tailored for a diverse audience, this book caters to researchers and academics in data science, decision science, machine learning, artificial intelligence, and related domains.

✦ Table of Contents

Cover
Title Page
Copyright Page
Book Series
Mission
Coverage
Preface
Chapter 1: Group-Oriented Multi-Attribute Decision-Making Method Based on Dominance Rough Set Theory
ABSTRACT
INTRODUCTION
BASIC KNOWLEDGE
GOMADMMADD
GMADMDRS
CASE ANALYSIS
CONCLUSION
REFERENCES
Chapter 2: A Multi-Granularity Triangular Fuzzy Approach for Diabetes Blood Glucometer Selection Using PROMETHEE and Three-Way Decision
ABSTRACT
INTRODUCTION
BASIC KNOWLEDGE
ADJUSTABLE MG TF PRS
THE SELECTION OF GLUCOMETERS BASED ON THE ADJUSTABLE MG TF PRS MODEL
A TF MAGDM BASED ON MG TWD
CASE ANALYSIS
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter 3: Prediction of Parkinson's Disease Severity Based on Feature Optimization
ABSTRACT
INTRODUCTION
RELATED WORKS
PROPOSED METHOD
EXPERIMENT
CONCLUSION AND FUTURE WORKS
REFERENCES
Chapter 4: Creating a Data Lakehouse for a South African Government-Sector Learning Control Enforcing Quality Control for Incremental Extract-Load-Transform Pipe
ABSTRACT
INTRODUCTION
LITERATURE REVIEW
RESEARCH METHODOLOGY
RESULTS AND ANALYSIS
CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 5: Decision-Making Based on Partition Order Product Space
ABSTRACT
INTRODUCTION
PARTITION ORDER PRODUCT SPACE
SEARCH APPROACH
THREE-WAY DECISION BASED ON PARTITION ORDER PRODUCTION SPACE
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter 6: Strategy Selection and Outcome Evaluation of Change-Based Three-Way Decisions Based on Reinforcement Learning
ABSTRACT
INTRODUCTION
BASIC KNOWLEDGE
STRATEGY SELECTION AND UTILITY MEASUREMENT BASED ON REINFORCEMENT LEARNING
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter 7: The Extension of MAIRCA Based on Fuzzy Number
ABSTRACT
INTRODUCTION
RELATED WORKS
MAIN FOCUS OF THE CHAPTER
SOLUTIONS AND RECOMMENDATIONS
FUTURE RESEARCH DIRECTIONS
CONCLUSION
REFERENCES
APPENDIX
Chapter 8: Review of Chinese Text Mining in Agriculture
ABSTRACT
INTRODUCTION
CORE TECHNOLOGUES FOR CHINESE CAT MINING
CAT MINING APPLICATIONS
CONCLUSION AND FUTURE WORK
REFERENCES
Chapter 9: A Snapshot Survey of Data Acquisition Forms in Multi-Attribute Decision-Making Studies
ABSTRACT
INTRODUCTION
LITERATURE REVIEW
SPECIFIC METHODS
QUESTIONS AND EXPECTATIONS
CONCLUSION
REFERENCES
Chapter 10: Research on Knowledge Representation and Reasoning Based on Decision Implication
ABSTRACT
INTRODUCTION
BASIC NOTIONS AND PROPERTIES IN FCA
A COMPARATIVE STUDY OF DECISION IMPLICATION, CONCEPT RULE AND GRANULAR RULE
DECISION IMPLICATION-BASED KNOWLEDGE RE-REPRESENTATION AND REASONING WITHIN INCOMPLETE FORMAL CONTEXT
CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES
ENDNOTES
Compilation of References
About the Contributors