Skill Cloud – Part 3

One of the obvious way to store a skill cloud is through a graph database.  Neo4j or Titan are two possible choices.  For no reason in particular, I’ll go with Neo4j, and use the embedded version with Java simple for a way to get up and running quickly.  Adding the Maven dependencies into my pom.xml is the easiest way to get started.  The Neo4j HelloWorld sample is probably the easiest thing to borrow as a skeleton for a quick start.

Modelling a person, their skills and their team is obvious an important part of this proof of concept (PoC).  Skill’s and team members would be modelled as Node’s (obviously), with appropriate relationships to capture linkage to a persons skills cloud (node’s) coupled with who working with who in a team – team is clearly a node in itself.

This nicely leads to “Representing time dependent graphs in Neo4j ” on GitHub.  What’s possibly interesting is that ideally you want a bi-temporal view of the nodes and relationships, since people, and teams change over time.


~ by mdavey on June 3, 2014.

4 Responses to “Skill Cloud – Part 3”

  1. […] interested in modelling the trade lifecycle in Neo4j (as discussed here) and also Skill Cloud (here) and avoiding the “current” view graphs that most applications/samples use.  Primarily […]

  2. Any idea how we can add/accumulate data to this system? purely manual?

  3. and what could be the best temporal db for this skill cloud initiative?

  4. […] Taking this a step further, I wonder what predictions are achievable if I took the skill cloud, married with other interesting “data” as per the Cloud Skill postings. […]

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