Database Reference
In-Depth Information
° A business expert who thoroughly understands the domain of the
graph database application will use this understanding to determine
useful recommendations. For example, the supervisor of a "do-it-
yourself" retail outlet would understand a particular pattern. Suppose
that if someone came in to buy multiple pots of paint, they would
probably also benefit from getting a gentle recommendation for a
promotion of high-end brushes. The store has that promotion going
on right then, so the recommendation would be very timely. This
process would be a discovery process where the pattern that the
business expert has discovered would be applied and used in graph
databases in real time as part of a sophisticated recommendation.
° A visual discovery of a specific pattern in the graph representation
of the business domain. We have found that in many different
projects, business users have stumbled upon these kinds of patterns
while using graph visualizations to look at their data. Specific
patterns emerge, unexpected relations jump out, or, in more
advanced visualization solutions, specific clusters of activity all
of a sudden become visible and require further investigation.
Graph databases such as Neo4j, and the visualization solutions
that complement it, can play a wonderfully powerful role in
this process.
° An algorithmic discovery of a pattern in a dataset of the business
domain using machine learning algorithms to uncover previously
unknown patterns in the data. Typically, these processes require
an iterative, raw number-crunching approach that has also been
used on non-graph data formats in the past. It remains part-art
part-science at this point, but can of course yield interesting
insights if applied correctly.
Recommender System
Pattern
Application System
Pattern Discovery System
Domain
Expertise-
based
Discovery
Visual
Discovery
Algorithmic
Discovery
Batch-
oriented
Real time
An overview of recommender systems
