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SIGMOD2023顶会

Theories and Principles Matter: Towards Visually Appealing and Effective Abstraction of Property Graph Queries

Jiebing Ma, Sourav S. Bhowmick, Byron Choi, Lester Tay

2023年份
8被引次数

摘要

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.

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