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arXiv · 2603.10625

A Hypergraph-Based Framework for Exploratory Business Intelligence

Yunkai Lou·Shunyang Li·Longbin Lai·Jianke Yu·Wenyuan Yu·Ying Zhang
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Abstract

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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Yunkai Lou, Shunyang Li, Longbin Lai, Jianke Yu, Wenyuan Yu, Ying Zhang. 2026-03-11. A Hypergraph-Based Framework for Exploratory Business Intelligence. https://arxiv.org/abs/2603.10625

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