Ian Robinson takes a look at how size, structure and connectedness have converged to change the way we work with data, showing some new opportunities with connected data illustrated with graph search.
Volker Pacher, Sam Phillips present key differences between relational databases and graph databases, and how they use the later to model a complex domain and to gain insights into their data.
Ian Robinson discusses graphs data structures, some of the queries that can extract data from them, and tools and techniques to work with graph data.
Mridula Jayaraman shares from her experience developing a next generation sequencing solution used to customize cancer treatment based on patient's genetic makeup.
Paul King presents working with databases in Groovy, covering datasets, GMongo, Neo4J, raw JDBC, Groovy-SQL, CRUD, Hibernate, caching, Spring Data technologies, etc.
Volker Pacher explains why Shutl chose Neo4j when faced with the need of building a new API meant to support business growth, the challenges met during implementation and solutions applied.
Ian Robinson discusses the complexity of highly connected data and how graph databases can help, illustrating the talk with practical examples implemented using Neo4j.
Stefan Armbruster discusses building a Grails application with a graph data store based on Neo4j and sharing insight based on his own experience using such a system in production.
Jim Webber explores graph data analytic techniques using social graph properties inspired by anthropology and sociology, extracting online business intelligence from graph matching.
Peter Bell presents several patterns for modeling and retrieving data from graph databases using Neo4j in his examples.
Axel Morgner compares different open source CMS’s and outlines the benefits of implementing one using a graph database.
Peter Bell introduces 4 NoSQL categories –Key-Value, Document, Column, Graph - and explains how one can use Spring Data to work with such data stores.