VINCENT: Towards Efficient Exploratory Subgraph Search in Graph Databases

Kai Huang, Qingqing Ye, Jing Zhao, Xi Zhao, Haibo Hu, Xiaofang Zhou

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

Exploratory search is a search paradigm that plays a vital role in databases, data mining, and information retrieval to assist users to get familiar with the underlying databases. It supports iterative query formulation to explore the data space. Despite its growing importance, exploratory search on graph-structured data has not received adequate attention in the literature. In this paper, we demonstrate a novel system called Vincent that facilitates an efficient exploratory subgraph search in a graph database containing a large collection of small or medium-sized graphs. By automatically generating the content for panels in gui and diversified patterns from databases and providing a visual result explorer, Vincent supports data-driven visual query formulation, incremental subgraph processing, and efficient query result summarization.

Original languageEnglish
Pages (from-to)3634-3637
Number of pages4
JournalProceedings of the VLDB Endowment
Volume15
Issue number12
DOIs
Publication statusPublished - Aug 2022
Event48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australia
Duration: 5 Sep 20229 Sep 2022

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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