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Chongchong Yao

dblp:183/0353 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2026
0009-0001-6450-1600ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Graph data management · 56% Information retrieval · 44%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Graph data management › graph query
subgraph query
0.712023
VisualNeo: Bridging the Gap between Visual Query Interfaces and Graph Query Engines · Proc. VLDB Endow. 2023
Information retrieval › search interfaces
visual query interface
0.712023
VisualNeo: Bridging the Gap between Visual Query Interfaces and Graph Query Engines · Proc. VLDB Endow. 2023

Methods — techniques the papers use, named apart from their topics

data-driven GUI design · 0.7canned pattern generation · 0.7
YearPublicationVenuePosition
2026 MVR: Multimodule visual reasoner-assisted small object tracking with a remote sensing-based UAV
Chongchong Yao, Nu Wen, Minmin Li
Neurocomputing1
2023 VisualNeo: Bridging the Gap between Visual Query Interfaces and Graph Query Engines
abstract
Visual Graph Query Interfaces (VQIs) empower non-programmers to query graph data by constructing visual queries intuitively. Devising efficient technologies in Graph Query Engines (GQEs) for interactive search and exploration has also been studied for years. However, these two vibrant scientific fields are traditionally independent of each other, causing a vast barrier for users who wish to explore the full-stack operations of graph querying. In this demonstration, we propose a novel VQI system built upon Neo4j called VisualNeo that facilities an efficient subgraph query in large graph databases. VisualNeo inherits several advanced features from recent advanced VQIs, which include the data-driven gui design and canned pattern generation. Additionally, it embodies a database manager module in order that users can connect to generic Neo4j databases. It performs query processing through the Neo4j driver and provides an aesthetic query result exploration.
Kai Huang 0011, Houdong Liang, Chongchong Yao, Xi Zhao 0006, Yue Cui 0001, Ruiyuan Zhang, Xiaofang Zhou 0001
Proc. VLDB Endow.3