  Recently, several works have studied the problem of view selection in graph
databases. However, existing methods cannot fully exploit the graph properties
of views, e.g., supergraph views and common subgraph views, which leads to a
low view utility and duplicate view content. To address the problem, we propose
an end-to-end graph view selection tool, G-View, which can judiciously generate
a view set from a query workload by exploring the graph properties of candidate
views and considering their efficacy. Specifically, given a graph query set and
a space budget, G-View translates each query to a candidate view pattern and
checks the query containment via a filtering-and-verification framework. G-View
then selects the views using a graph gene algorithm (GGA), which relies on a
three-phase framework that explores graph view transformations to reduce the
view space and optimize the view benefit. Finally, G-View generates the
extended graph views that persist all the edge-induced subgraphs to answer the
subgraph and supergraph queries simultaneously. Extensive experiments on
real-life and synthetic datasets demonstrated G-View achieved averagely 21x and
2x query performance speedup over two view-based methods while having 2x and 5x
smaller space overhead, respectively. Moreover, the proposed selection
algorithm, GGA, outperformed other selection methods in both effectiveness and
efficiency.
