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Concepts Covered in This Course

This document lists all concepts covered in the Graph Data Modeling course, organized in logical learning order. Each label is kept under 32 characters for display in learning network graph nodes.

Foundation Concepts (Chapter 1-2)

  1. Graph Data Model
  2. World Models
  3. GraphRAG Pattern
  4. Vector Stores
  5. Semantic Indexes
  6. Real-Time Systems
  7. Knowledge Triangle
  8. Data Layer
  9. Information Layer
  10. Knowledge Layer
  11. Sensor Data
  12. Page Rank Algorithm
  13. Relational Models
  14. Analytical Models
  15. Key-Value Stores
  16. Document Models
  17. ISO GQL Standard
  18. Graph and LLM Integration
  19. Nodes
  20. Edges
  21. Properties
  22. Simple Data Types
  23. Complex Data Types
  24. Paths
  25. Dependencies
  26. Graph Quality
  27. Quality Measures
  28. Graph Constraints
  29. Quality Assertions
  30. Quality Scores
  31. Quality Dashboards

Customer Modeling (Chapter 3)

  1. Customer Definition
  2. Individual Customers
  3. Customer Locations
  4. Customer Addresses
  5. Household Modeling
  6. Family Relationships
  7. Corporate Customers
  8. Organization Customers
  9. License Modeling
  10. Abuse Detection
  11. IP Address Tracking

Product Modeling (Chapter 4)

  1. Product Lists
  2. Product Groupings
  3. Product Taxonomies
  4. Multiple Taxonomies
  5. Classification Tools
  6. Product Similarity
  7. Product Embeddings
  8. Product Metadata

Spatial Modeling (Chapter 5)

  1. Spatial Challenges
  2. Location Modeling
  3. Longitude and Latitude
  4. Distance Calculations
  5. Address Modeling
  6. City/County/State Model
  7. Metropolitan Regions
  8. Sales Regions
  9. Urban/Rural Regions
  10. Social Determinants
  11. Road Modeling
  12. Shortest Path
  13. Traveling Salesperson
  14. Bus Routes
  15. Provider Distance
  16. Geospatial Models

Temporal Modeling (Chapter 6)

  1. DateTime Structure
  2. Time Trees
  3. Year/Month/Day Hierarchy
  4. Hour/Minute/Second
  5. Millisecond Precision
  6. Financial Time
  7. Organization Calendar
  8. Time Exceptions
  9. Daylight Savings Time

Concept Modeling (Chapter 7)

  1. Knowledge Graphs
  2. The Semantic Spectrum
  3. SKOS Standard
  4. SKOS in LPG
  5. Acronyms and Abbreviations
  6. Business Glossaries
  7. Taxonomies
  8. Ontologies
  9. Concept Nodes
  10. Concept Labels
  11. Preferred Labels
  12. Alternate Labels
  13. Broader/Narrower
  14. Semantic Variability
  15. Concept Schemas
  16. GraphRAG Challenges

Language Modeling (Chapter 8)

  1. Language in Graphs
  2. Words in Graphs
  3. WordNet
  4. NLP Basics
  5. Entity Extraction
  6. Sentence Modeling
  7. Paragraph Modeling
  8. Document Modeling
  9. Document Pipelines
  10. Synonyms
  11. Synonym Rings
  12. Antonyms

Fraud Detection (Chapter 9)

  1. Fraud/Waste/Abuse
  2. Anti-Money Laundering
  3. Unusual Relationships
  4. Investigations

Healthcare Modeling (Chapter 10)

  1. Patient Modeling
  2. Clinical Data
  3. Claims
  4. Providers
  5. Population Health
  6. Disease Spread
  7. Benefit Plans
  8. Plan Coverage
  9. Healthcare Costs
  10. Value-Based Care
  11. Patient Similarity
  12. Care Paths
  13. FHIR
  14. Clinical Decision Support
  15. Modeling Healthcare Knowledge
  16. FHIR Approach
  17. FHIR Four Level Model
  18. L1: Narrative
  19. L2: Semi-Structures
  20. L3: Structure
  21. L4: Executable
  22. Clinical Quality Language

Entity Resolution (Chapter 11)

  1. Data Connection
  2. Identical Attributes
  3. Similar Attributes
  4. SoundEx Algorithm
  5. Nickname Matching
  6. Similarity Metrics
  7. Embeddings
  8. Vector Stores
  9. Graph Advantage for ER

Digital Twins (Chapter 12)

  1. Digital Twins
  2. Building Models
  3. Manufacturing Models
  4. Supply Chain Models
  5. Real-time Updates
  6. Graph Advantages DT

Scene Modeling (Chapter 13)

  1. Scene Graphs
  2. Pixel Classification
  3. Movement Prediction
  4. Autonomous Vehicles
  5. Robotics Applications

Business Rules (Chapter 14)

  1. Rule Engines
  2. Context Transfer Challenge
  3. In-Graph Rules
  4. Rules and Workflows
  5. Rule Modeling
  6. BPMN Notation
  7. Decision Trees
  8. Validation Rules
  9. Data Mining
  10. Process Mining
  11. Rule Execution
  12. Rule Exchange
  13. Performance Advantage

Code Modeling (Chapter 15)

  1. Code Graphs
  2. Nodes as Functions
  3. Edges as Dependencies
  4. Directed Graphs
  5. Undirected Graphs
  6. Weighted Edges
  7. Call Graphs
  8. Dead Code Detection
  9. Recursive Functions
  10. Circular Dependencies
  11. Text Coverage Graphs
  12. Test-Code Mapping
  13. Hybrid Graphs
  14. Cyclomatic Complexity
  15. Code Churn
  16. Code Hotspots
  17. Static Analysis
  18. Dynamic Analysis
  19. CI/CD Integration

Security Modeling (Chapter 16)

  1. Server Modeling
  2. Network Modeling
  3. Threat Modeling
  4. Threat Prioritization
  5. Vulnerability Assessment
  6. Person Roles
  7. Role-Based Access Control
  8. RBAC and Performance
  9. Monitoring Agents

Process Modeling (Chapter 17)

  1. Event Modeling
  2. Event Mining
  3. Events and Workflows
  4. Event Logs
  5. Event Dashboards

Learning Modeling (Chapter 18)

  1. Learning Graphs
  2. Concept Dependencies
  3. Concept Models
  4. Content Models
  5. Learning Paths
  6. Path Recommendations
  7. Content Recommendations
  8. Intelligent Textbooks

Causality Modeling (Chapter 19)

  1. Correlation vs Causation
  2. Causal Graphs
  3. Systems Thinking
  4. Causality Data Flows
  5. Causal Loop Diagrams
  6. Consumer Preference Models
  7. Bayesian Network Analysis

Lineage Modeling (Chapter 20)

  1. Lineage and Provenance
  2. Entity Definition
  3. Activity Definition
  4. Agent Definition
  5. DM-Prov Model
  6. W3C Provenance

Metadata Modeling (Chapter 21)

  1. Metadata Definition
  2. Data Governance
  3. Data Stewards
  4. Data Domains
  5. Data Matching
  6. Schema Matching
  7. Data Mapping

Supply Chain Modeling (Chapter 22)

  1. Inventory Management
  2. Alternate Suppliers
  3. Supply Chain Disruption
  4. Transportation Networks

Bitemporal Modeling (Chapter 23)

  1. Bitemporal Graphs
  2. Real World Time
  3. System Time

Advanced Topics (Chapter 24)

  1. Tradeoff Analysis
  2. Graph Training
  3. Graph Performance
  4. Transactional Integrity
  5. Scalability
  6. Sustainability
  7. Graph Storytelling

Model Evolution (Chapter 25)

  1. Model Expansion
  2. Complex Adaptive Systems
  3. Edge of Chaos
  4. Model Complexity
  5. Cost-Benefit Analysis
  6. Initial Project Costs
  7. Ongoing Project Costs
  8. Network Effects

Brain Modeling (Chapter 26)

  1. Brain Architecture
  2. 1000 Brains Theory
  3. Cortical Columns
  4. Grid Cells
  5. Place Cells
  6. Multiple Representations
  7. Voting Mechanisms
  8. Sensory-Motor Networks
  9. Reference Frames
  10. Monty Framework
  11. Graph AI Future

Future of Graph in the Age of AI (Chapter 27)

  1. World Models
  2. AI and Graph Future
  3. Graph Query Language

Total Concepts: 259

Note: All labels are under 32 characters for network graph display.