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

• Published in: The VLDB journal Vol. 29; no. 5; pp. 973 - 998
• Main Authors: Wang, Chaohui, Xie, Miao, Bhowmick, Sourav S., Choi, Byron, Xiao, Xiaokui, Zhou, Shuigeng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2020; Springer Nature B.V
• Subjects: Computer Science; Database Management; Graph theory; Queries; Query formulation; Regular Paper; Searching; Vaccines; Visual flight; Visual interface; Graph database; Exploratory subgraph search; Indexing framework; Human–graph interaction
• ISSN: 1066-8888; 0949-877X
• DOI: 10.1007/s00778-020-00601-0

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.