Graph databases /

Discover how graph databases can help you manage and query highly connected data. With this practical book, you will learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user quer...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Robinson, Ian, 1969- (Autor), Eifrem, Emil (Autor)
Otros Autores: Webber, James
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, Inc., ©2013.
Sebastopol, Calif. : O'Reilly Media, 2013.
Edición:First edition.
Temas:
Database management.
Databases.
Database design.
Graph theory > Data processing.
Bases de données > Gestion.
Bases de données > Conception.
COMPUTERS > Database Management > General.
COMPUTERS > Desktop Applications > Databases.
COMPUTERS > System Administration > Storage & Retrieval.
Database design
Database management
Databases
Graph theory > Data processing
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Introduction
  • - Options for storing connected data
  • - Data modeling with graphs
  • - Building a graph database application
  • - Graphs in the real world
  • - Graph database internals
  • - Predictive analysis with graph theory
  • - NOSQL overview.
  • What Is a Graph?
  • A High-Level View of the Graph Space
  • Graph Databases
  • Graph Compute Engines
  • The Power of Graph Databases
  • Performance
  • Flexibility
  • Agility
  • Summary
  • Relational Databases Lack Relationships
  • NOSQL Databases Also Lack Relationships
  • Graph Databases Embrace Relationships
  • Summary
  • Models and Goals
  • The Property Graph Model
  • Querying Graphe-An Introduction to Cypher
  • Cypher Philosophy
  • START
  • MATCH
  • RETURN
  • Other Cypher Clauses
  • A Comparison of Relational and Graph Modeling
  • Relational Modeling in a Systems Management Domain
  • Graph Modeling in a Systems Management Domain
  • Testing the Model
  • Cross-Domain Models
  • Creating the Shakespeare Graph
  • Beginning a Query
  • Declaring Information Patterns to Find
  • Constraining Matches
  • Processing Results
  • Query Chaining
  • Common Modeling Pitfalls
  • Email Provenance Problem Domain
  • A Sensible First Iteration?
  • Second Time's the Charm
  • Evolving the Domain
  • Avoiding Anti-Patterns
  • Summary
  • Data Modeling
  • Describe the Model in Terms of the Application's Needs
  • Nodes for Things, Relationships for Structure
  • Fine-Grained versus Generic Relationships
  • Model Facts as Nodes
  • Represent Complex Value Types as Nodes
  • Time
  • Iterative and Incremental Development
  • Application Architecture
  • Embedded Versus Server
  • Clustering
  • Load Balancing
  • Testing
  • Test-Driven Data Model Development
  • Performance Testing
  • Capacity Planning
  • Optimization Criteria
  • Performance
  • Redundancy
  • Load
  • Summary
  • Why Organizations Choose Graph Databases
  • Common Use Cases
  • Social
  • Recommendations
  • Geo
  • Master Data Management
  • Network and Data Center Management
  • Authorization and Access Control (Communications)
  • Real-World Examples
  • Social Recommendations (Professional Social Network)
  • Authorization and Access Control
  • Geo (Logistics)
  • Summary
  • Native Graph Processing
  • Native Graph Storage
  • Programmatic APIs
  • Kernel API
  • Core (or "Beans") API
  • Traversal API
  • Nonfunctional Characteristics
  • Transactions
  • Recoverability
  • Availability
  • Scale
  • Summary
  • Depth- and Breadth-First Search
  • Path-Finding with Dijkstra's Algorithm
  • The A* Algorithm
  • Graph Theory and Predictive Modeling
  • Triadic Closures
  • Structural Balance
  • Local Bridges
  • Summary.

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