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：探索式商业智能是商业智能分析的重要发展方向，其典型特征为分析者通过多轮迭代式探索逐步深化对数据的理解。然而，传统商业智能系统在支持探索式分析时面临关键瓶颈：高度依赖专家知识、计算开销高、模式结构固化以及缺乏可复用性。本文提出的ExBI系统通过引入包含Source、Join、View三类算子的超图数据模型，实现了动态模式演化和物化视图复用。系统采用基于采样的算法，在提供可证明估计保证的同时，解决了计算瓶颈问题并维护分析准确性。在LDBC数据集上的实验表明，ExBI相比现有系统取得了显著加速：与Neo4j相比平均加速16.21倍（最高146.25倍），与MySQL相比平均加速46.67倍（最高230.53倍）。同时，系统在COUNT操作上保持高精度，平均错误率仅为0.27%，实现了高效准确的大规模探索式商业智能工作流。