GraphPolaris：一个用于图数据库查询、分析和可视化的系统
GraphPolaris: A System for Query, Analysis, and Visualization of Graph Databases
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中文总结 AI 辅助
研究旨在解决图数据库交互式分析支持不足问题，提出GraphPolaris系统，核心是GPQL语言，为分析关系和模式提供基础，通过案例研究和长期混合方法研究进行评估，助力用户无需编程探索分析图数据库。
中文摘要 AI 辅助
图数据库正越来越多地被用作商业智能（BI）系统中基于表格、聚焦聚合的数据模型的替代方案。它们能捕捉实体、流程和事件之间的复杂关系，使网络中的信息传播分析成为可能。现有工具对图数据库的交互式分析支持不足。我们提出了GraphPolaris，一个无需编码的可视化分析系统，核心是GRAPHPOLARIS查询语言（GPQL），它为分析关系和图模式提供了形式基础，作为用户交互和底层数据库的中介。我们通过两个实际案例研究和一个长达22个月的形成性混合方法研究对GraphPolaris进行评估。
英文摘要
Graph databases are increasingly adopted as alternatives to tabular, aggregation-focused data models used in business intelligence (BI) systems such as Tableau, Power BI, and Looker. They capture complex relationships between entities, processes, and events, enabling analysis of information propagation in networks. As a result, graph analysis is central to applications such as fraud detection, social influence analysis, and supply chain resilience. Despite these advantages, existing tools do not adequately support interactive analysis of graph databases. Tabular BI systems lack mechanisms for reasoning over nodes and edges, while graph databases require specialized query languages and fragmented workflows that hinder accessibility. We present GraphPolaris, a no-code Visual Analytics system that enables users to explore, analyze, and visualize graph databases without programming skills. At its core, GraphPolaris features the GRAPHPOLARIS QUERY LANGUAGE (GPQL), a formal query grammar that facilitates flexible and composable graph queries, providing a formal foundation for analyzing relationships and graph patterns. GPQL serves as an intermediary between user interactions and the underlying database. Its formal foundation enables no-code query construction, database-agnostic query generation, and guarantees that every interaction produces a valid executable query. Informed by a formative user study, we designed GraphPolaris' interface and visualizations to lower technical barriers and foster iterative, collaborative exploration of complex networks. We evaluate GraphPolaris through two real-world case studies in telecommunications and supply-chain analysis and a 22-month-long formative mixed-method study, including a MILC-based assessment of its fit to analysts' graph analytics workflows.
