  The property graph data model of modern graph database systems is
increasingly adapted for storing and processing heterogeneous datasets like
networks. Many challenging applications with near real-time requirements --
e.g. financial fraud detection, recommendation systems, and on-the-fly
validation -- can be captured with graph queries, which are evaluated
repeatedly. To ensure quick response time for a changing data set, these
applications would benefit from applying incremental view maintenance (IVM)
techniques, which can perform continuous evaluation of queries and calculate
the changes in the result set upon updates. However, currently, no graph
databases provide support for incremental views. While IVM problems have been
studied extensively over relational databases, views on property graph queries
require operators outside the scope of standard relational algebra. Hence,
tackling this problem requires the integration of numerous existing IVM
techniques and possibly further extensions. In this paper, we present an
approach to perform IVM on property graphs, using a nested relational algebraic
representation for property graphs and graph operations. Then we define a chain
of transformations to reduce most property graph queries to flat relational
algebra and use techniques from discrimination networks (used in rule-based
expert systems) to evaluate them. We demonstrate the approach using our
prototype tool, ingraph, which uses openCypher, an open graph query language
specified as part of an industry initiative. However, several aspects of our
approach can be generalised to other graph query languages such as G-CORE and
PGQL.
