用超图模型解决探索式商业智能的动态分析难题
A Hypergraph-Based Framework for Exploratory Business Intelligence
- 采用超图数据模型与动态算子实现灵活的模式演化
- 采样算法使查询速度提升16.21倍（最高146倍）且误差仅0.27%
- 适合需要快速迭代分析的大规模企业数据场景
探索式商业智能（Exploratory BI）是一种迭代式、多轮次的数据分析范式，但传统BI系统存在依赖专家知识、计算开销大、模式静态、复用性差等局限。本文提出ExBI系统，引入超图数据模型与源、连接、视图等操作符，支持动态模式演进与物化视图复用。通过具有可证明估计保证的采样算法，有效缓解计算瓶颈，同时保持分析精度。在LDBC数据集上的实验表明，ExBI相比Neo4j平均提速16.21倍（最高146.25倍），相比MySQL平均提速46.67倍（最高230.53倍），COUNT查询平均误差率仅为0.27%，显著提升了大规模探索式BI的工作效率与准确性。
原文摘要 · Abstract (English)
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.
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