  As techniques for graph query processing mature, the need for optimization is
increasingly becoming an imperative. Indices are one of the key ingredients
toward efficient query processing strategies via cost-based optimization. Due
to the apparent absence of a common representation model, it is difficult to
make a focused effort toward developing access structures, metrics to evaluate
query costs, and choose alternatives. In this context, recent interests in
covering-based graph matching appears to be a promising direction of research.
In this paper, our goal is to formally introduce a new graph representation
model, called Minimum Hub Cover, and demonstrate that this representation
offers interesting strategic advantages, facilitates construction of candidate
graphs from graph fragments, and helps leverage indices in novel ways for query
optimization. However, similar to other covering problems, minimum hub cover is
NP-hard, and thus is a natural candidate for optimization. We claim that
computing the minimum hub cover leads to substantial cost reduction for graph
query processing. We present a computational characterization of minimum hub
cover based on integer programming to substantiate our claim and investigate
its computational cost on various graph types.
