A Hypergraph-Based Framework for Exploratory Business Intelligence

Mar 11, 2026·
Yunkai Lou
,
Shunyang Li
,
Longbin Lai
Jianke Yu
Jianke Yu
,
Wenyuan Yu
,
Ying Zhang
· 1 min read
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.
Type
Publication
arXiv preprint
Status
Open access
publications

arXiv preprint, 2026

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.

Jianke Yu
Authors
Jianke Yu (he/him)
PhD (2026) in Graph Machine Learning & Databases
I completed my PhD at the University of Technology Sydney (UTS) in 2026, supervised by Prof. Ying Zhang, Prof. Lu Qin and Dr Hanchen Wang. My research develops machine learning algorithms for graph-structured data and database systems — graph neural network architectures, and learning-based methods that improve database and data-mining algorithms (fraud detection, graph similarity, supergraph search, malware detection). My work has appeared in KDD and IEEE TKDE. I am joining Zhejiang Gongshang University as a faculty member in 2027.