Traditional access control relies on the identity of a user, their role or their group memberships. This can become awkward to manage, particularly when other factors such as time of day, or network location come into play. These additional factors, or attributes, require a different approach, the US National Institute of Standards and Technology (NIST) have published a
draft special paper (NIST 800-162) on Attribute-Based Access Control (ABAC).
This post, and the accompanying Graph Gist, explore the suitability of using a graph database to support policy decisions.
In my first job I was working for a company that developed a management information system for UK Police Forces; this system produced the statutory HMIC (Her Majesty’s Inspectorate of Constabulary) reports and allowed OLAP exploration of the datasets loaded into cubes from the data warehouse tables.
One of the areas that I implemented was the key performance indicators for Road Traffic Collisions, so I was intrigued to discover that the fuller, anonymised STATS19 dataset was now available on data.gov.uk. If you’re interested in the STATS19 form you can see it here.
This article provides a practical introduction to graph databases with a focus on the Groovy ecosystem. It explores Grails GORM support for Neo4j as well as query comparisons between the Neo4j Cypher language, SQL and the Gremlin graph DSL.
This article originally appeared in the May 2013 issue of GroovyMag.