Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jiebing Ma

dblp:350/5758 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2023
0009-0009-6199-8277ORCID · reported

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

Databases, data management, data science and information retrieval · 1 · 1 first-author · 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
Information retrieval · 77% Data models and query languages · 23%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

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

TopicWeightPapersLastEvidence papers
Information retrieval › query formulation
visual query formulation
0.712023
Theories and Principles Matter: Towards Visually Appealing and Effective Abstraction of Property Graph Queries · Proc. ACM Manag. Data 2023
Data models and query languages › graph query language
property graph query language
0.212023
Theories and Principles Matter: Towards Visually Appealing and Effective Abstraction of Property Graph Queries · Proc. ACM Manag. Data 2023

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

user study · 1.3psychology theories · 1.3HCI principles · 1.3
YearPublicationVenuePosition
2023 Theories and Principles Matter: Towards Visually Appealing and Effective Abstraction of Property Graph Queries
abstract
Existing visual abstraction of a property graph query by representing it as a labeled atomic graph (LAG) has great potential to democratize the usage of property graph databases as it enables user-friendly visual query formulation without demanding the need to learn a property graph query language e.g., Cypher. Unfortunately, existing LAG-based query interfaces do not embrace HCI principles and psychology theories to inform their design and as a result may have adverse impact on their usability and aesthetics. In this paper, we depart from the classical theory- and principles-oblivious LAG abstraction to present a novel theory-informed visual abstraction called labeled composite graph (LCG) to address this limitation. It realizes a novel and extensible visual shape definition language called VEDA to create and maintain an LCG systematically, guided by a variety of theories and principles from HCI, visualization and psychology. We build a novel LCG-based visual property graph query interface for Cypher called SIERRA and demonstrate through a user study its superiority to an industrial-strength LAG-based query interface for property graphs w.r.t. usability, aesthetics and efficient query formulation.
Jiebing Ma, Sourav S. Bhowmick, Byron Choi, Lester Tay
Proc. ACM Manag. Data1