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The paper introduces ExBI, a novel business intelligence system leveraging a hypergraph data model with operators like Source, Join, and View to facilitate dynamic schema evolution and materialized view reuse for exploratory BI. To address computational costs, ExBI employs sampling-based algorithms with provable estimation guarantees, ensuring analytical accuracy. Experiments on LDBC datasets demonstrate that ExBI achieves significant speedups (16.21x over Neo4j and 46.67x over MySQL) with minimal error (0.27% for COUNT).
Hypergraphs and sampling can speed up exploratory business intelligence queries by over 16x compared to Neo4j, while maintaining high accuracy.
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.
