Business Intelligence (BI) analysis is evolving towards Exploratory BI, an iterative, multi-round exploration paradigm where analysts progressively refine their understanding. However, traditional BI systems impose critical limits for Exploratory BI: heavy reliance on expert knowledge, high computational costs, static schemas, and lack of reusability. We present ExBI, a novel system that introduces the hypergraph data model with operators, including Source, Join, and View, to enable dynamic schema evolution and materialized view reuse. Using sampling-based algorithms with provable estimation guarantees, ExBI addresses the computational bottlenecks, while maintaining analytical accuracy. Experiments on LDBC datasets demonstrate that ExBI achieves significant speedups over existing systems: on average 16.21x (up to 146.25x) compared to Neo4j and 46.67x (up to 230.53x) compared to MySQL, while maintaining high accuracy with an average error rate of only 0.27% for COUNT, enabling efficient and accurate large-scale exploratory BI workflows.

翻译：商业智能（BI）分析正朝着探索式BI演进，这是一种迭代、多轮的探索范式，分析师在其中逐步深化理解。然而，传统BI系统对探索式BI施加了关键限制：高度依赖专家知识、计算成本高昂、模式静态且缺乏可重用性。我们提出了ExBI，这是一个新颖的系统，引入了带操作符（包括Source、Join和View）的超图数据模型，以实现动态模式演进和物化视图重用。通过采用具有可证明估计保证的基于采样的算法，ExBI解决了计算瓶颈，同时保持了分析准确性。在LDBC数据集上的实验表明，ExBI相较于现有系统实现了显著的加速：平均比Neo4j快16.21倍（最高达146.25倍），比MySQL快46.67倍（最高达230.53倍），同时保持高准确性，COUNT查询的平均错误率仅为0.27%，从而实现了高效且准确的大规模探索式BI工作流。