12 Graph Databases

Many-to-many databases in the real world¶

Social network datasets

• A follows B, A is a friend of B

Internet dataset

• Page A links to Page B

Road network datasets

• Road A is connected to road B

Scientific literature datasets

• Paper is written by author, cites another paper

Scientific interaction datasets

• Disease treated by drug, drug A and drug B have interactions

These (and more) motivate models of networked data, i.e., models of graph data.

Graphs consists of nodes, edges, and properties (of nodes or edges).

• Data 101 Course Notes
• 1 SQL
  • 1.1 SQL Review
  • 1.2 String Manipulation
  • 1.3 Truncating Relations
  • 1.4 Creating Tables and Views from Data
  • 1.5 Common Table Expressions (CTE)
  • 1.6 Subqueries
  • 1.7 Window Functions
  • 1.8 DML—Data Manipulation Language
  • 1.9 DDL–Data Definition Language
  • 1.10 Keys & Constraints
• 2 Relational Algebra
  • 2.1 Relational Algebra Overview
  • 2.2 Extended RA Operators
• 3 Query Performance
  • 3.1 Index Selection
  • 3.2 Query Planning
  • 3.3 Rules of Thumb
  • 3.4 PostgreSQL Schemas
• 4 Data Modeling
  • 4.1 Data Models
  • 4.2 Entity Relationship(ER) Diagrams
• 5 Data Preparation
  • 5.1 Structural
  • 5.2 Numerical, Granularity
  • 5.3 Outliers
  • 5.4 Imputation and Entity Resolution
• 6 Semistructured Data
  • 6.1 Semistructured Data
  • 6.2 MongoDB
• 7 Sampling
  • Database Sampling
• 8 Transactions
  • 8.1 Transactions
• 9 Parallel/Distributed Computing
  • Parallel and Distributed Computing
• 10 Data Operations and Pipelines
  • Data Operations and Pipelines
• 11 Business Intelligence (BI)
  • Business Intelligence (BI)
• 12 Graph Databases
  • Property Graphs
  • Triplestores/RDFs
  • Ontologies
• Spreadsheets
  • The Spreadsheet Data Model
• Appendix
  • 12.1 SQL Style Guide
  • 12.2 MongoDB Style Guide