Index
A
- A* algorithm, The A* Algorithm
- access control, Authorization and Access Control (Communications)
- ACID transactions, Why Organizations Choose Graph Databases, ACID versus BASE-ACID versus BASE
- administrator(s)
- aggregate stores, Graph Databases Embrace Relationships, Query versus Processing in Aggregate Stores
- aggregates, relationships between, NOSQL Databases Also Lack Relationships
- agility of graph databases, Agility
- Amazon, Key-Value Stores
- Amazon Web Services (AWS), Global clusters
- anti-patterns, avoiding, Avoiding Anti-Patterns
- Apache Cassandra, Column Family
- Apache Hadoop, NOSQL Databases Also Lack Relationships, Query versus Processing in Aggregate Stores
- APIs (application programming interfaces), Agility, Embedded Neo4j, Server extensions, Programmatic APIs-Traversal Framework
- application architecture, Application Architecture-Read your own writes
- application performance tests, Application performance tests
- application(s), graph database
- application architecture, Application Architecture-Read your own writes
- building, Building a Graph Database Application-Summary
- capacity planning, Capacity Planning-Load
- clustering, Clustering
- data modeling for, Data Modeling-Iterative and Incremental Development
- fine-grained vs. generic relationships for, Fine-Grained versus Generic Relationships
- importing/bulk loading data, Importing and Bulk Loading Data-Batch Import
- iterative/incremental development, Iterative and Incremental Development
- modeling facts as nodes, Model Facts as Nodes-Reviewing
- nodes vs. relationships for, Nodes for Things, Relationships for Structure
- representing complex value types, Represent Complex Value Types as Nodes
- testing, Testing-Testing with representative data
- time modeling, Time-Versioning
- atomic transactions, ACID versus BASE
- Atomic, Consistent, Isolated, Durable (ACID) transactionality, Why Organizations Choose Graph Databases, ACID versus BASE-ACID versus BASE
- authorization and access control, Authorization and Access Control (Communications), Authorization and Access Control-Finding administrators for an account
- availability, Availability-Availability
- average request time, Load
- AWS (Amazon Web Services), Global clusters
B
- balanced triadic closures, Structural Balance
- balancing, load, Load Balancing-Read your own writes
- BASE transactions, ACID versus BASE-ACID versus BASE
- basic availability, ACID versus BASE
- batch import (data), Batch Import-Batch Import
- BigTable, Column Family
- Blueprints SAIL API, Triples
- bound nodes, Beginning a Query
- breadth-first search algorithm, Depth- and Breadth-First Search
- brute-force processing, NOSQL Databases Also Lack Relationships
- buffer writes, Buffer writes using queues
- bulk loading (data), Importing and Bulk Loading Data-Batch Import
- business responsiveness, Why Organizations Choose Graph Databases
C
- cache sharding, Cache sharding
- capacity planning, Capacity Planning-Load
- capacity, scale and, Capacity
- Cassovary, Graph Compute Engines
- CEP (Complex Event Processing), Testing the Model
- Charland, Gary, Introduction
- Christakis, Nicholas, Social
- clauses, Cypher, Cypher Philosophy-Other Cypher Clauses
- clustering, Clustering
- column family stores, Column Family-Column Family
- column-oriented NOSQL databases, NOSQL Databases Also Lack Relationships
- communications (authorization and access control), Authorization and Access Control (Communications)
- Complex Event Processing (CEP), Testing the Model
- complex transactions, Server extensions
- complex value types, representing, Represent Complex Value Types as Nodes
- concurrent requests, Load
- Connected (Christakis and Fowler), Social
- connected data
- consistent hashing, Key-Value Stores
- consistent stores, Initial Import
- consistent transactions, ACID versus BASE
- constraints, Relational Databases Lack Relationships, Beginning a Query
- core API, Core API, Traversal Framework
- core data types (CRDTs), Key-Value Stores
- cost optimization, Optimization Criteria
- cost(s)
- CouchDB, Document Stores
- CRDTs (core data types), Key-Value Stores
- CREATE clause, Other Cypher Clauses
- CREATE CONSTRAINT command, Beginning a Query
- CREATE INDEX command, Beginning a Query, Batch Import
- CREATE UNIQUE clause, Other Cypher Clauses
- create, read, update, and delete (CRUD) methods, Graph Databases, Graph Databases
- cross-domain models, Cross-Domain Models-Query Chaining
- CRUD (create, read, update, and delete) methods, Graph Databases
- Cypher
- advantages/disadvantages, Traversal Framework
- beginning a query in, Beginning a Query-Beginning a Query
- clauses in, Cypher Philosophy-Cypher Philosophy
- constraining matches in, Constraining Matches
- CREATE clause, Other Cypher Clauses
- CREATE CONSTRAINT command, Beginning a Query
- CREATE INDEX command, Beginning a Query, Batch Import
- CREATE UNIQUE clause, Other Cypher Clauses
- declaring information patterns to find, Declaring Information Patterns to Find
- DELETE clause, Other Cypher Clauses
- DISTINCT clause, Processing Results
- FOREACH clause, Other Cypher Clauses
- indexes and constraints in, Beginning a Query
- MATCH clause, MATCH, Declaring Information Patterns to Find, Constraining Matches
- MERGE clause, Other Cypher Clauses, Batch Import
- PERIODIC COMMIT functionality, Batch Import
- philosophy of, Cypher Philosophy-Other Cypher Clauses
- processing results in, Processing Results
- query chaining in, Query Chaining
- querying graphs with, Querying Graphs: An Introduction to Cypher-Other Cypher Clauses
- RETURN clause, RETURN, RETURN, Processing Results
- SET clause, Other Cypher Clauses
- START clause, Other Cypher Clauses
- UNION clause, Other Cypher Clauses
- WHERE clause, Other Cypher Clauses, Constraining Matches
- WITH clause, Other Cypher Clauses, Query Chaining
D
- data
- data center management, Network and Data Center Management
- data mining, A High-Level View of the Graph Space
- data modeling
- and complex value types, Represent Complex Value Types as Nodes
- avoiding anti-patterns, Avoiding Anti-Patterns
- common pitfalls, Common Modeling Pitfalls-Evolving the Domain
- cross-domain models, Cross-Domain Models-Query Chaining
- describing in terms of applications needs, Describe the Model in Terms of the Application’s Needs
- email provenance problem domain, Email Provenance Problem Domain-Evolving the Domain
- evolving the domain, Evolving the Domain-Evolving the Domain
- fine-grained vs. generic relationships for, Fine-Grained versus Generic Relationships
- for applications, Data Modeling-Iterative and Incremental Development
- Global Post, Global Post data model-Global Post data model
- graph modeling in systems management domain, Graph Modeling in a Systems Management Domain-Graph Modeling in a Systems Management Domain
- identifying nodes and relationships, Identifying Nodes and Relationships
- iterative/incremental development, Iterative and Incremental Development
- labeled property graph for, The Labeled Property Graph Model
- modeling facts as nodes, Model Facts as Nodes-Reviewing
- models and goals, Models and Goals
- nodes vs. relationships for, Nodes for Things, Relationships for Structure
- querying graphs with Cypher, Querying Graphs: An Introduction to Cypher-Other Cypher Clauses
- relational modeling in systems management domain, Relational Modeling in a Systems Management Domain-Relational Modeling in a Systems Management Domain
- relational modeling vs. graph modeling, A Comparison of Relational and Graph Modeling-Testing the Model
- Talent.net, Talent.net data model
- TeleGraph Communications, TeleGraph data model-TeleGraph data model
- test-driven development, Test-Driven Data Model Development-Testing server extensions
- testing the domain model, Testing the Model-Testing the Model
- time, Time-Versioning
- with graphs, Data Modeling with Graphs-Summary
- database life cycle, Embedded Neo4j
- database refactorings, Relational Modeling in a Systems Management Domain
- DELETE clause, Other Cypher Clauses
- denormalization, Relational Modeling in a Systems Management Domain-Relational Modeling in a Systems Management Domain
- depth-first search algorithm, Depth- and Breadth-First Search
- development cycles, drastically accelerated, Why Organizations Choose Graph Databases
- development, test-driven, Test-Driven Data Model Development-Testing server extensions
- Dijkstras algorithm, Implementing route calculation with the Traversal Framework
- DISTINCT clause, Processing Results
- distributed graph compute engines, Graph Compute Engines
- document stores, Document Stores-Document Stores
- document-NOSQL databases, NOSQL Databases Also Lack Relationships
- domain modeling
- Domain-Driven Design notion, Query versus Processing in Aggregate Stores
- domains, highly connected, Relational Databases Lack Relationships
- doubly linked lists, Native Graph Storage
- drastically accelerated development cycles, Why Organizations Choose Graph Databases
- drawing data, MATCH
- durable transactions, ACID versus BASE
- Dynamo database, Key-Value Stores
E
- Easley, David, Introduction
- edges, What Is a Graph?
- email, provenance problem domain, Email Provenance Problem Domain-Evolving the Domain
- embedded mode
- encapsulation, server extensions and, Server extensions
- end node, The Labeled Property Graph Model
- enterprise ready graph databases, Why Organizations Choose Graph Databases
- ETL (extract, transform, and load) jobs, Graph Compute Engines
- Euler, Leonhard, Preface, Geo
- eventual consistency storage, ACID versus BASE
- evolution, domain, Evolving the Domain-Evolving the Domain
- expensive joins, Relational Databases Lack Relationships
- explicit transactions, Embedded Neo4j
- extensions, server, Server extensions-Server extensions
- extract, transform, and load (ETL) jobs, Graph Compute Engines
F
- Facebook, Social, Triadic Closures
- facts, modeling as nodes, Model Facts as Nodes-Reviewing
- fine-grained relationships, Fine-Grained versus Generic Relationships, TeleGraph data model
- FOREACH clause, Other Cypher Clauses
- foreign key constraints, Relational Databases Lack Relationships
- Fowler, James, Social
G
- Gartner, What Is a Graph?
- Gatling, Application performance tests
- GC (garbage collection) behavior, Embedded Neo4j
- generating load, Application performance tests
- generic relationships, Fine-Grained versus Generic Relationships
- geospatial applications, Geo, Geospatial and Logistics-Implementing route calculation with the Traversal Framework
- Giraph, Graph Compute Engines
- global clusters, Global clusters
- Global Post, Geospatial and Logistics-Implementing route calculation with the Traversal Framework
- goals, data modeling, Models and Goals
- Google, Avoiding Anti-Patterns, Column Family, Query versus Processing in Aggregate Stores
- graph analytics, offline, A High-Level View of the Graph Space
- graph components, Local Bridges
- graph compute engines, A High-Level View of the Graph Space, Graph Compute Engines, Graph Compute Engines
- graph databases (graph database management systems)
- and relationships, Graph Databases Embrace Relationships-Graph Databases Embrace Relationships
- application building, Building a Graph Database Application-Summary
- defined, Graph Databases
- hypergraphs, Hypergraphs
- implementation, Graph Database Internals-Summary
- in NOSQL, Graph Databases-Triples
- internals, Graph Database Internals-Summary
- nonfunctional characteristics, Nonfunctional Characteristics-Throughput
- performance costing, Calculating the cost of graph database performance
- power of, The Power of Graph Databases-Agility
- properties, Graph Databases
- property graphs, Property Graphs
- reasons for choosing, Why Organizations Choose Graph Databases
- triple stores, Triples-Triples
- uses for, Preface
- graph matches, constraining, Constraining Matches
- graph modeling
- graph space
- graph theory, Preface
- graph(s)
- Gremlin, Querying Graphs: An Introduction to Cypher
- Grinder, Application performance tests
- grouping nodes, Graph Databases Embrace Relationships
I
- identifiers, Cypher Philosophy
- idiomatic queries
- implicitly connected data, Graph Databases Embrace Relationships
- importing data, Importing and Bulk Loading Data-Batch Import
- in-memory graph compute engines, Graph Compute Engines
- incremental development, Iterative and Incremental Development
- index-free adjacency, Graph Databases, NOSQL Databases Also Lack Relationships, Native Graph Processing, Native Graph Processing, Graph Databases
- indexes, constraints with, Beginning a Query
- information patterns, declaring, Declaring Information Patterns to Find
- informed depth-first search algorithm, Depth- and Breadth-First Search
- inlining, Native Graph Storage
- Introduction To Graph Theory (Trudeau), Introduction
- Introductory Graph Theory (Chartrand), Introduction
- isolated transactions, ACID versus BASE
- iterative development, Iterative and Incremental Development
L
- label(s)
- labeled property graph, What Is a Graph?, The Labeled Property Graph Model
- latency, Embedded Neo4j, Latency
- LFU (least frequently used) cache policy, Native Graph Storage
- link(s)
- linked lists, Linked lists
- LinkedIn, Social, Triadic Closures
- lists
- load balancing, Load Balancing-Read your own writes, Separate read traffic from write traffic
- load optimization, Optimization Criteria, Load
- local bridges, Local Bridges-Local Bridges
- LRU-K page cache, Native Graph Storage
M
- MapReduce, Key-Value Stores, Query versus Processing in Aggregate Stores
- master data management, Master Data Management
- MATCH clause, MATCH, Declaring Information Patterns to Find, Constraining Matches
- matches, constraining, Constraining Matches
- MERGE clause, Other Cypher Clauses, Batch Import
- migration, Relational Modeling in a Systems Management Domain
- minimum point cut, Throughput
- MongoDB, Throughput
N
- native graph processing, Graph Databases, Native Graph Processing-Native Graph Processing, Graph Databases
- native graph storage, Graph Databases, Native Graph Storage-Native Graph Storage, Graph Databases
- Neo4j
- availability, Availability-Availability
- capacity, Capacity
- clustering, Clustering
- core API, Core API
- embedded mode, Embedded Neo4j
- implementation, Graph Database Internals-Summary
- index-free adjacency and low-cost joins, Native Graph Processing
- inlining and optimizing property store utilization, Native Graph Storage
- kernel API, Kernel API
- native graph storage, Native Graph Storage-Native Graph Storage
- nonfunctional characteristics, Nonfunctional Characteristics-Throughput
- programmatic APIs, Programmatic APIs-Traversal Framework
- recoverability, Recoverability
- scale, Scale-Throughput
- server mode, Server mode
- transactions, Transactions
- Traversal Framework, Graph Databases Embrace Relationships
- various replication options in, Availability
- Neo4j in Action (Partner and Vukotic), Graph Databases Embrace Relationships
- network management, Network and Data Center Management
- network overhead, Server mode
- Networks, Crowds, and Markets (Easley and Kleinberg), Introduction
- nodes, What Is a Graph?
- add new, Graph Databases Embrace Relationships
- for data modeling, Nodes for Things, Relationships for Structure
- grouping, Graph Databases Embrace Relationships
- identifying, Identifying Nodes and Relationships
- labels and, The Labeled Property Graph Model
- modeling facts as, Model Facts as Nodes-Reviewing
- relationships and, The Labeled Property Graph Model
- relationships vs, Nodes for Things, Relationships for Structure
- representing complex value types as, Represent Complex Value Types as Nodes
- tagging, The Labeled Property Graph Model
- nonfunctional characteristics, Nonfunctional Characteristics-Throughput
- NOSQL data storage
- ACID vs. BASE, ACID versus BASE-ACID versus BASE
- column family stores, NOSQL Databases Also Lack Relationships, Column Family-Column Family
- document stores, NOSQL Databases Also Lack Relationships, Document Stores-Document Stores
- drawbacks of, NOSQL Databases Also Lack Relationships-NOSQL Databases Also Lack Relationships
- graph databases in, Graph Databases-Triples
- hypergraphs, Hypergraphs
- key-value stores, NOSQL Databases Also Lack Relationships, Key-Value Stores-Key-Value Stores
- overview, NOSQL Overview-Triples
- property graphs, Property Graphs
- quadrants, The NOSQL Quadrants-Query versus Processing in Aggregate Stores
- query vs. processing in aggregate stores, Query versus Processing in Aggregate Stores
- rise of, The Rise of NOSQL
- triple stores, Triples-Triples
O
- O algorithms, NOSQL Databases Also Lack Relationships
- O-notation, NOSQL Databases Also Lack Relationships
- offline graph analytics, A High-Level View of the Graph Space
- OLAP (online analytical processing), A High-Level View of the Graph Space
- OLTP (online transactional processing) databases, A High-Level View of the Graph Space, Graph Databases
- online analytical processing (OLAP), A High-Level View of the Graph Space
- online graph persistence, A High-Level View of the Graph Space
- online transactional processing (OLTP) databases, A High-Level View of the Graph Space, Graph Databases
- opacity, access to subelements inside structured data and, Key-Value Stores
- optimization
P
- page caches, Native Graph Storage
- path-finding with Dijkstras algorithm, Path-Finding with Dijkstra’s Algorithm-Path-Finding with Dijkstra’s Algorithm
- paths, Cypher Philosophy
- Pegasus, Graph Compute Engines
- performance
- performance optimization, Optimization Criteria
- performance testing, Performance Testing-Testing with representative data
- PERIODIC COMMIT functionality, Batch Import
- pitfalls, data modeling, Common Modeling Pitfalls-Evolving the Domain
- platforms, Server mode
- power of graph databases, The Power of Graph Databases-Agility
- predictive analysis
- predictive modeling, graph theory and, Graph Theory and Predictive Modeling-Structural Balance
- Pregel, Graph Compute Engines
- processing
- processing engine, Graph Databases, Graph Databases
- professional social network, social recommendations case example, Social Recommendations (Professional Social Network)-Adding WORKED_WITH relationships
- programmatic APIs, Programmatic APIs-Traversal Framework
- properties, relationships with, TeleGraph data model
- property graphs
- property store utilization, Native Graph Storage
Q
- quadrants, NOSQL data storage, The NOSQL Quadrants-Query versus Processing in Aggregate Stores
- queries
- chaining in cross-domain models, Query Chaining
- choosing method for, Traversal Framework
- for cross-domain models, Beginning a Query-Beginning a Query
- idiomatic, Availability
- in aggregate stores, Query versus Processing in Aggregate Stores
- performance tests for, Query performance tests
- reciprocal, Relational Databases Lack Relationships
- unidiomatic, Availability
- various languages, Querying Graphs: An Introduction to Cypher
- with Cypher, Querying Graphs: An Introduction to Cypher-Other Cypher Clauses, Beginning a Query-Beginning a Query, Query Chaining
- query chaining, Query Chaining
- query language(s), Querying Graphs: An Introduction to Cypher
- queues, buffer writes using, Buffer writes using queues
R
- R-Tree, Graph Databases Embrace Relationships
- Rails, Relational Modeling in a Systems Management Domain
- RDF (Resource Description Framework) triples, A High-Level View of the Graph Space
- read traffic, separating write traffic from, Separate read traffic from write traffic
- real-world applications, Graphs in the Real World-Summary
- authorization and access control, Authorization and Access Control (Communications), Authorization and Access Control-Finding administrators for an account
- case examples, Real-World Examples-Implementing route calculation with the Traversal Framework
- common use cases, Common Use Cases-Authorization and Access Control (Communications)
- data center management, Network and Data Center Management
- geospatial applications, Geo, Geospatial and Logistics-Implementing route calculation with the Traversal Framework
- master data management, Master Data Management
- network management, Network and Data Center Management
- recommendation algorithms, Recommendations
- social data, Social
- social recommendations (professional social network case example), Social Recommendations (Professional Social Network)-Adding WORKED_WITH relationships
- why organizations choose graph databases, Why Organizations Choose Graph Databases
- reciprocal queries, Relational Databases Lack Relationships
- recommendation algorithms, Recommendations
- recoverability, Recoverability
- redundancy
- relational databases, Relational Databases Lack Relationships-Relational Databases Lack Relationships, Graph Databases Embrace Relationships
- relational modeling
- relationship chains, Native Graph Storage
- relationship store, Native Graph Storage
- relationship(s), What Is a Graph?
- add new, Graph Databases Embrace Relationships
- and graph databases, Graph Databases Embrace Relationships-Graph Databases Embrace Relationships
- and NOSQL databases, NOSQL Databases Also Lack Relationships-NOSQL Databases Also Lack Relationships
- and relational databases, Relational Databases Lack Relationships-Relational Databases Lack Relationships
- fine-grained vs. generic, Fine-Grained versus Generic Relationships
- fine-grained vs. relationships with properties, TeleGraph data model
- for data modeling, Nodes for Things, Relationships for Structure
- identifying, Identifying Nodes and Relationships
- labels and, Cross-Domain Models
- nodes and, The Labeled Property Graph Model
- nodes vs., Nodes for Things, Relationships for Structure
- strong vs. weak, Triadic Closures
- with properties, TeleGraph data model
- replication
- representative data, testing with, Testing with representative data-Testing with representative data
- Resource Description Framework (RDF) triples, A High-Level View of the Graph Space
- REST API, Server mode, Server mode
- results processing in cross-domain models, Processing Results
- RETURN clause, RETURN, Processing Results
- Riak, NOSQL Databases Also Lack Relationships
- route calculation, Route calculation-Implementing route calculation with the Traversal Framework
S
- SaaS (software as a service) offerings, Authorization and Access Control (Communications)
- scale, Scale-Throughput
- scaling, Server mode
- search algorithms, depth- and breadth-first, Depth- and Breadth-First Search
- server extensions, Server extensions-Server extensions
- server mode
- SET clause, Other Cypher Clauses
- Seven Bridges of Konigsberg problem, Geo
- Shakespeare graph (cross-domain modeling), Creating the Shakespeare Graph
- sharding, Throughput, Document Stores
- shortest weighted path calculation, Route calculation
- single machine graph compute engines, Graph Compute Engines
- social data, Social
- social graphs, Social
- social networks
- social recommendations (professional social network), Social Recommendations (Professional Social Network)-Adding WORKED_WITH relationships
- social relations, inferring, Inferring social relations-Inferring social relations
- soft-state storage, ACID versus BASE
- software as a service (SaaS) offerings, Authorization and Access Control (Communications)
- solid state disks (SSDs), Native Graph Storage
- SOR databases, Graph Compute Engines
- specification by example, Cypher Philosophy
- START clause, Other Cypher Clauses
- start node, The Labeled Property Graph Model
- storage
- store files, Native Graph Storage
- strong relationships, Triadic Closures
- strong triadic closure property, Triadic Closures
- structural balance, Structural Balance-Structural Balance, Structural Balance
- super column, Column Family
- system of record (SOR) databases, Graph Compute Engines
- systems management domain
T
- tagging nodes, The Labeled Property Graph Model
- Talent.net, Social Recommendations (Professional Social Network)-Adding WORKED_WITH relationships
- TeleGraph Communications, Authorization and Access Control-Finding administrators for an account
- test-driven data model development, Test-Driven Data Model Development-Testing server extensions
- testing
- throughput, Throughput
- time
- timeline trees, Timeline trees
- transaction commit, Transactions
- transaction event handlers, Kernel API
- transaction state, Server mode
- transaction(s), Transactions
- transactional systems, Graph Databases
- Traversal Framework, Traversal Framework
- traversing links, NOSQL Databases Also Lack Relationships
- traversing relationships, Adding WORKED_WITH relationships
- triadic closures, Triadic Closures-Triadic Closures
- triple stores, Triples-Triples
- Trudeau, Richard J., Introduction
- Twitter, What Is a Graph?-What Is a Graph?, Triadic Closures
V
- values, complex, Represent Complex Value Types as Nodes
- variable length paths, Testing the Model
- variety, The Rise of NOSQL
- Velocity, The Rise of NOSQL
- verbing, Avoiding Anti-Patterns
- versioned graphs, Versioning
- versioning, Versioning
- vertices, What Is a Graph?
- volume, The Rise of NOSQL
W
- W3C, triple store support by, Triples
- walking skeletons, Application performance tests
- walking, links and, NOSQL Databases Also Lack Relationships
- weak relationships, Triadic Closures
- WHERE clause, Other Cypher Clauses, Constraining Matches-Constraining Matches, Constraining Matches
- WITH clause, Other Cypher Clauses, Query Chaining
- Write Ahead Log, Transactions
- write traffic, separating read traffic from, Separate read traffic from write traffic
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