Graphs in the Real World - Graph Databases - page 102

second approach, identifying users and groups for a particular resource, focuses on the
characteristics of the resource in question; the engine then identifies similar resources,
and the users associated with those resources.
As in the social use case, making an effective recommendation depends on under‐
standing the connections between things, as well as the quality and strength of those
connections—all of which are best expressed as a property graph. Queries are primarily
graph local, in that they start with one or more identifiable subjects, whether people or
resources, and thereafter discover surrounding portions of the graph.
Taken together, social networks and recommendation engines provide key differenti‐
ating capabilities in the areas of retail, recruitment, sentiment analysis, search, and
knowledge management. Graphs are a good fit for the densely connected data structures
germane to each of these areas; storing and querying this data using a graph database
allows an application to surface end-user real-time results that reflect recent changes to
the data, rather than precalculated, stale results.
Geo
Geospatial is the original graph use case: Euler solved the Seven Bridges of Königsberg
problem by positing a mathematical theorem that later came to form the basis of graph
theory. Geospatial applications of graph databases range from calculating routes be‐
tween locations in an abstract network such as a road or rail network, airspace network,
or logistical network (as illustrated by the logistics example later in this chapter) to
spatial operations such as find all points of interest in a bounded area, find the center
of a region, and calculate the intersection between two or more regions.
Geospatial operations depend upon specific data structures, ranging from simple
weighted and directed relationships, through to spatial indexes, such as R-Trees , which
represent multidimensional properties using tree data structures. As indexes, these data
structures naturally take the form of a graph, typically hierarchical in form, and as such
they are a good fit for a graph database. Because of the schema-free nature of graph
databases, geospatial data can reside in the database beside other kinds of data—social
network data, for example—allowing for complex multidimensional querying across
several domains. 2
Geospatial applications of graph databases are particularly relevant in the areas of tel‐
ecommunications, logistics, travel, timetabling, and route planning.
2. Neo4j Spatial is an open source library of utilities that implement spatial indexes and expose Neo4j data to
geotools.