A Hypergraph-Based Framework for Exploratory Business Intelligence

2026-03-11

AI Summary

This work proposes ExBI, a novel exploratory business intelligence system that overcomes the limitations of traditional BI platforms—such as rigid schemas, high computational overhead, and reliance on expert knowledge—by introducing a hypergraph data model. ExBI features specialized Source, Join, and View operators to enable dynamic schema evolution and materialized view reuse, while integrating a sampling-based estimation algorithm with theoretical error guarantees for efficient and accurate multi-round querying. Experimental evaluation on the LDBC benchmark demonstrates that ExBI achieves an average speedup of 16.21× (up to 146.25×) over Neo4j and 46.67× (up to 230.53×) over MySQL, with an average error rate of only 0.27% for COUNT queries.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Exploratory Business Intelligence
• hypergraph
• dynamic schema evolution
• materialized view reuse
• computational bottlenecks

Innovation

• hypergraph
• exploratory business intelligence
• dynamic schema evolution
• materialized view reuse
• sampling-based estimation