Efficient Answering of Why-Not Questions in Similar Graph Matching

Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph and a set of missing ( why-not) graphs, we aim to modify into a new query graph such that the missing graphs are included in the new answer set of . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for . Then, we present a two-phase algorithm to find the new query . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.