Databases Reference
In-Depth Information
• Which resource can an end user access?
• Given a particular resource, who can modify its access settings? (Bottom-up)
Graph database access control and authorization solutions are particularly applicable
in the areas of content management, federated authorization services, social networking
preferences, and software as a service (SaaS) offerings, where they realize minutes to
milliseconds increases in performance over their hand-rolled, relational predecessors.
Real-World Examples
In this section we describe three example use cases in detail: social and recommenda‐
tions, authorization and access control, and logistics. Each use case is drawn from one
or more production applications of a graph database (specifically in these cases, Neo4j).
Company names, context, data models, and queries have been tweaked to eliminate
accidental complexity and to highlight important design and implementation choices.
Social Recommendations (Professional Social Network)
Talent.net is a social recommendations application that enables users to discover their
own professional network, and identify other users with particular skill sets. Users work
for companies, work on projects, and have one or more interests or skills. Based on this
information, Talent.net can describe a user's professional network by identifying other
subscribers who share his or her interests. Searches can be restricted to the user's current
company, or extended to encompass the entire subscriber base. Talent.net can also
identify individuals with specific skills who are directly or indirectly connected to the
current user; such searches are useful when looking for a subject matter expert for a
current engagement.
Talent.net illustrates how a powerful inferential capability can be developed using a
graph database. Although many line-of-business applications are deductive and precise
—calculating tax or salary, or balancing debits and credits, for example—a new seam
of end-user value opens up when we apply inductive algorithms to our data. This is
what Talent.net does. Based on people's interests and skills, and their work history, the
application can suggest likely candidates for including in one's professional network.
These results are not precise in the way a payroll calculation must be precise, but they
are extremely useful nonetheless.
Talent.net infers connections between people. Contrast this with LinkedIn, for example,
where users explicitly declare they know or have worked with someone. This is not to
say that LinkedIn is solely a precise social networking capability, because it too applies
inductive algorithms to generate further insight. But with Talent.net even the primary
tie,
(A)-[:KNOWS]->(B)
, is inferred, rather than volunteered.
