Approximating Graph Pattern Queries Using Views
Jia Li, Yang Cao, Xudong Liu · 2016
This paper studies approximation of graph pattern queries using views. Given a pattern query Q and a set V of views, we propose to find a pair of queries Qu and Ql, referred to as the upper and lower approximations of Q w.r.t. V, such that (a) for any data graph G, answers to (part of) Q in G are contained in Qu(G) and contain Ql(G); and (b) both Qu and Ql can be answered by using views in V. We consider pattern queries based on both graph simulation and subgraph isomorphism. We study fundamental problems about approximation using views. Given Q and V, (1) we study whether there exist upper and lower approximations of Q w.r.t. V. (2) How to find approximations that are closest to Q w.r.t. V if exist? (3) How to answer upper and lower approximations using views in V? We give characterizations of the problems, study their complexity and approximation-hardness, and develop algorithms with provable bounds. Using real-life datasets, we verify the effectiveness and efficiency of approximating simulation and subgraph queries using views.