A way to represent and manage "big data" is to use a graph database. Such a database uses graph structures for semantic queries with nodes, edges and properties to represent and store data locally indexed.
 A graph database is essentially a collection of nodes and edges. Each node represents an entity (such as a person or business) and each edge represents a connection or relationship between two nodes.
Every node in a graph database is defined by a unique identifier, a set of outgoing edges and/or incoming edges and a set of properties expressed as key/value pairs.
Each edge is defined by a unique identifier, a starting-place and/or ending-place node and a set of properties.
 Graph databases are well-suited for analyzing interconnections, which is why there has been a lot of interest in using graph databases to mine data from different media. CIGESMED is testing them in a global ecology framework (graph transversal).

 More information here.

 In CIGESMED we will complement this approach by data mining in order to reveal new data patterns which are not accessible through classical approaches.

Presentations supporting the discussion (PPTX) and/or video

Complex approach with graphs and data visualization, interpretative and analysing tools, Romain David