FERRARI: an efficient framework for visual exploratory subgraph search in graph databases

Abstract

Exploratory search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data space. Query formulation evolves iteratively in this paradigm as a user becomes more familiar with the content. Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. An early effort for building exploratory subgraph search framework on graph databases suffers from efficiency and scalability problems. In this paper, we present a visual exploratory subgraph search framework called ferrari, which embodies two novel index structures called vaccine and advise, to address these limitations. vaccine is an offline, feature-based index that stores rich information related to frequent and infrequent subgraphs in the underlying graph database, and how they can be transformed from one subgraph to another during visual query formulation. advise, on the other hand, is an adaptive, compact, on-the-fly index instantiated during iterative visual formulation/reformulation of a subgraph query for exploratory search and records relevant information to efficiently support its repeated evaluation. Extensive experiments and user study on real-world datasets demonstrate superiority of ferrari to a state-of-the-art visual exploratory subgraph search technique.