A Hypergraph-Based Framework for Exploratory Business Intelligence
This addresses the problem of inefficient and rigid exploratory data analysis for business analysts, representing a strong specific gain in performance.
The paper tackles the computational and flexibility limitations of traditional Business Intelligence systems for exploratory analysis by introducing ExBI, a hypergraph-based system that achieves average speedups of 16.21x over Neo4j and 46.67x over MySQL with an average error rate of 0.27% for COUNT queries.
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.