TY - JOUR
T1 - VINCENT: Towards Efficient Exploratory Subgraph Search in Graph Databases
AU - Huang, Kai
AU - Ye, Qingqing
AU - Zhao, Jing
AU - Zhao, Xi
AU - Hu, Haibo
AU - Zhou, Xiaofang
N1 - Funding Information:
The research work described in this paper was partially conducted in the JC STEM Lab of Data Science Foundations funded by The Hong Kong Jockey Club Charities Trust. This work was also supported by the National Natural Science Foundation of China (Grant No: 62072390 and 62102334), and the Research Grants Council, Hong Kong SAR, China (Grant No: 15222118, 15218919, 15203120, 15226221, 15225921 and C2004-21GF).
Publisher Copyright:
© 2022, VLDB Endowment. All rights reserved.
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85138007568&partnerID=8YFLogxK
U2 - 10.14778/3554821.3554862
DO - 10.14778/3554821.3554862
M3 - Conference article
AN - SCOPUS:85138007568
SN - 2150-8097
VL - 15
SP - 3634
EP - 3637
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
T2 - 48th International Conference on Very Large Data Bases, VLDB 2022
Y2 - 5 September 2022 through 9 September 2022
ER -