A Hypergraph-Based Framework for Exploratory Business Intelligence

TL;DR

This paper introduces ExBI, a hypergraph-based system that enhances exploratory Business Intelligence by enabling dynamic schemas, reusability, and efficient sampling algorithms, significantly improving speed and maintaining accuracy.

Contribution

ExBI's novel hypergraph data model and operators facilitate dynamic schema evolution and view reuse, addressing computational and static schema limitations in exploratory BI.

Findings

ExBI achieves up to 146.25x speedup over Neo4j.

ExBI maintains an average error rate of 0.27% for COUNT queries.

Sampling algorithms provide provable estimation guarantees.

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.