Automatic View Selection in Graph Databases

TL;DR

This paper introduces G-View, an end-to-end tool for selecting graph views in databases by exploiting graph properties, significantly improving query performance and reducing space overhead.

Contribution

The paper presents G-View, a novel graph view selection framework that uses graph properties and a genetic algorithm to optimize view utility and efficiency.

Findings

G-View achieves 21x and 2x speedup over existing methods.

G-View reduces space overhead by 2x and 5x.

GGA outperforms other view selection algorithms.

Abstract

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.