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The essential criteria of graph databases /

This book provides a comprehensive exploration of graph databases, covering their history, architecture, and applications. It delves into the fundamental concepts of graph computing and storage, the evolution of graph query languages, and the design of scalable and highly available graph database sy...

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Bibliographic Details
Main Author: Sun, Ricky
Corporate Author: ScienceDirect (Online service)
Format: eBook
Language:English
Published: Amsterdam : Elsevier, 2024.
Subjects:
Graph databases.
Bases de données orientées graphe.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • The Essential Criteria of Graph Databases
  • Copyright
  • Contents
  • Chapter 1: History of graph computing and graph databases
  • 1.1. What exactly is a graph?
  • 1.1.1. The forgotten art of graph thinking, part I
  • 1.1.2. The forgotten art of graph thinking II
  • 1.1.3. A brief history of graph technology development
  • 1.2. The evolution of big data and database technologies
  • 1.2.1. From data to big data to deep data
  • 1.2.2. Relational database vs graph database
  • 1.3. Graph computing in the Internet of things (IoT) era
  • 1.3.1. Unprecedented capabilities
  • 1.3.2. Differences between graph computing and graph database
  • Chapter 2: Graph database basics and principles
  • 2.1. Graph computing
  • 2.1.1. Basic concepts of graph computing
  • 2.1.2. Applicable scenarios of graph computing
  • 2.2. Graph storage
  • 2.2.1. Basic concepts of graph storage
  • 2.2.2. Graph storage data structure and modeling
  • 2.3. Evolution of graph query language
  • 2.3.1. Basic concepts of database query language
  • 2.3.2. Graph query language
  • Chapter 3: Graph database architecture design
  • 3.1. High-performance graph storage architecture
  • 3.1.1. Key features of high-performance storage systems
  • 3.1.2. High-performance storage architecture design ideas
  • 3.2. High-performance graph computing architecture
  • 3.2.1. Real-time graph computing system architecture
  • 3.2.2. Graph database schema and data model
  • 3.2.3. How the core engine handles different data types
  • 3.2.4. Data structure in graph computing engine
  • 3.2.5. How to partition (shard) a large graph
  • 3.2.6. High availability and scalability
  • 3.2.7. Failure and recovery
  • 3.3. Graph query and analysis framework design
  • 3.3.1. Graph database query language design ideas
  • Path query
  • K neighbor query
  • Metadata query
  • Graph query language compiler
  • 3.3.2. Graph visualization
  • Schema management and metadata display
  • Various display methods of query results
  • Graph algorithm visualization
  • Chapter 4: Graph algorithms
  • 4.1. Degree
  • 4.2. Centrality
  • 4.2.1. Graph centrality algorithm
  • 4.2.2. Closeness centrality
  • 4.2.3. Betweenness centrality
  • 4.3. Similarity
  • 4.3.1. Jaccard similarity
  • 4.3.2. Cosine similarity
  • 4.4. Connectivity calculation
  • 4.4.1. Connected component
  • 4.4.2. Minimum spanning tree
  • 4.5. Ranking
  • 4.5.1. PageRank
  • 4.5.2. SybilRank
  • 4.6. Propagation computing
  • 4.6.1. Label propagation algorithm
  • 4.6.2. HANP algorithm
  • 4.7. Community computing
  • 4.7.1. Triangle counting
  • 4.7.2. Louvain community recognition
  • 4.8. Graph embedding computing
  • 4.8.1. Complete random walk algorithm
  • 4.8.2. Struc2Vec algorithm
  • 4.9. Graph algorithms and interpretability
  • Chapter 5: Scalable graphs
  • 5.1. Scalable graph database design
  • 5.1.1. Vertical scalability
  • 5.1.2. Horizontal scalability