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A Hypergraph-Based Framework for Exploratory Business Intelligence

Yunkai Lou, Shunyang Li, Longbin Lai, Jianke Yu, Wenyuan Yu, Ying Zhang

TL;DR

ExBI is 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, and addresses the computational bottlenecks, while maintaining analytical accuracy.

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.

A Hypergraph-Based Framework for Exploratory Business Intelligence

TL;DR

ExBI is 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, and addresses the computational bottlenecks, while maintaining analytical accuracy.

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.
Paper Structure (29 sections, 6 theorems, 14 equations, 10 figures, 8 tables)

This paper contains 29 sections, 6 theorems, 14 equations, 10 figures, 8 tables.

Table of Contents

  1. Related Work
  2. Data Cube Models
  3. Business Intelligence Tools
  4. Approximate Query Processing
  5. Hypergraph Data Model
  6. Data Model
  7. Operators on Hypergraph Data Model
  8. $$ Source Operator
  9. $$ Join Operator
  10. $$ View Operator
  11. Adaptation to Cube Operators
  12. Framework Overview
  13. Storage Layer
  14. Computational Layer
  15. Analysis Layer
  16. ...and 14 more sections

Key Result

lemma thmcounterlemma

Given a relational table $\hat{R}$ that is uniformly sampled from an original table $R$ with sampling rate $\rho$, an unbiased estimator for COUNT is: Similarly, for computing SUM on attribute $X$, an unbiased estimator is: where $X_r$ denotes the value of attribute $X$ in row $r$.

Figures (10)

  • Figure 2: An example of a property graph, property graph schema, query graph, and the corresponding hypergraph. Pub, Org, and HAI are abbreviations of Publication, Organization, and hasAuthorInstitution, respectively. $v_i$ represents the vertex with id $i$; $u_i$ represents query vertex with id $i$.
  • Figure 3: An example of the Join operator
  • Figure 4: The relational table obtained by applying the $$View operator to the hypergraph $\mathcal{G}_{\text{f}}$
  • Figure 5: The architecture of the $$ExBI framework
  • Figure 6: $$Exploratory BI queries used in the experiments
  • ...and 5 more figures

Theorems & Definitions (29)

  • Definition 1: Property Graph
  • Definition 2: Induced Subgraph
  • Definition 3: Hypergraph Data Model
  • Example 2
  • Definition 4: Query Graph
  • Definition 5: Induced Subgraph Isomorphism
  • Example 3
  • Definition 6: Source Operator, ${ {\mathsf{Source}}}\xspace$
  • Definition 7: Hypergraph Schema
  • remark thmcounterremark: Structural Properties of Hyperedges
  • ...and 19 more