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An Introduction to Doctor Who (and Neo4j)

Posted by Ian Robinson  on  Mar 23, 2012 1

Ian Robinson introduces Neo4J, a graph database, discussing how it can be used to store and work with data associated with Doctor Who.

Using MongoDB on Mono

Posted by Justin Dearing  on  Jan 19, 2012 1

Justin Dearing presents a brief introduction to MongoDB, and focuses on interacting with it in Mono via the official 10gen driver. Techniques for handling business logic in application code, such as LINQ are discussed. This is a very code centric talk.

Introduction to Spring Data Neo4j

Posted by Michael Hunger  on  Dec 08, 2011

Michael Hunger discusses graph databases and the need for them in the larger context of NoSQL data stores, introducing Spring Data, Neo4j, and Spring Data Neo4j.

Using A Graph Database To Power The “Web of Things”

Posted by Rick Bullotta and Emil Eifrem  on  Oct 11, 2011 2

Rick Bullotta and Emil Eifrem discuss how to use graph databases to model the real world, people, systems and things, talking advantage of the relationships between various data elements.

HyperGraphDB - Data Management for Complex Systems

Posted by Borislav Iordanov  on  Dec 15, 2010 2

Borislav Iordanov presents the architecture of HyperGraphDB, a special type of store based on hypergraphs – graphs with edges pointing to an arbitrary number of nodes and to other edges, comparing it with other graphs databases and its relationship to other NoSQL stores.

Neo4j: NOSQL and the Benefits of Graph Databases

Posted by Emil Eifrem  on  Jul 14, 2010 5

Emil Eifrem overviews the trends leading to NOSQL (Not Only SQL), and the four emerging NOSQL solutions: key-value stores, plus column, document and graph databases. He also explains the internals of a graph database and an example of using Neo4j - a graph database - in production.

Neo4j - The Benefits of Graph Databases

Posted by Emil Eifrem  on  Jul 01, 2009 1

This presentation covers the definition of a graph database (information structured as mathematical graphs with nodes, relationships and properties) and their advantages when dealing with data that is difficult to fit in static tables, is rapidly evolving, or that has a lot of optional attributes. The flexibility of graph databases better support agile development and schema evolution.

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