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Data Insights as Data: Quick Overview and Exploration of Automated Data Insights

Shangxuan Wu, Wendi Luan, Yong Wang, Dan Zeng, Qiaomu Shen, Bo Tang

TL;DR

This work addresses the challenge of exploring and discovering data insights by bridging automated insight mining with interactive visualization. It introduces InsightMap, a map-based visual analytics system that combines automated extraction via QuickInsights with a three-view interface (Data Distribution, Insight Map, and Individual Insight) and a novel similarity metric based on insight subspaces. Insights are represented as a five-tuple and embedded into a 2D space using a two-vector embedding (instance and attribute coverage) to reveal relationships among insights, with a KDE-based density viz to convey concentration. The approach is validated through a real-world NBA dataset case study and expert interviews, showing that InsightMap can provide quick overviews and enable detailed, relation-aware exploration, while also highlighting limitations related to explainability, visual clutter, and subspace configurability. The work offers an open-source implementation and a solid foundation for expanding automated insight types and their interrelations in exploratory data analysis.

Abstract

Automated data insight mining and visualization have been widely used in various business intelligence applications (e.g., market analysis and product promotion). However, automated insight mining techniques often output the same mining results to different analysts without considering their personal preferences, while interactive insight discovery requires significant manual effort. This paper fills the gap by integrating automated insight mining with interactive data visualization and striking a proper balance between them to facilitate insight discovery and exploration. Specifically, we regard data insights as a special type of data and further present InsightMap, a novel visualization approach that uses the map metaphor to provide a quick overview and in-depth exploration of different data insights, where a metric is proposed to measure the similarity between different insights. The effectiveness and usability of InsightMap are demonstrated through extensive case studies and in-depth user interviews.

Data Insights as Data: Quick Overview and Exploration of Automated Data Insights

TL;DR

This work addresses the challenge of exploring and discovering data insights by bridging automated insight mining with interactive visualization. It introduces InsightMap, a map-based visual analytics system that combines automated extraction via QuickInsights with a three-view interface (Data Distribution, Insight Map, and Individual Insight) and a novel similarity metric based on insight subspaces. Insights are represented as a five-tuple and embedded into a 2D space using a two-vector embedding (instance and attribute coverage) to reveal relationships among insights, with a KDE-based density viz to convey concentration. The approach is validated through a real-world NBA dataset case study and expert interviews, showing that InsightMap can provide quick overviews and enable detailed, relation-aware exploration, while also highlighting limitations related to explainability, visual clutter, and subspace configurability. The work offers an open-source implementation and a solid foundation for expanding automated insight types and their interrelations in exploratory data analysis.

Abstract

Automated data insight mining and visualization have been widely used in various business intelligence applications (e.g., market analysis and product promotion). However, automated insight mining techniques often output the same mining results to different analysts without considering their personal preferences, while interactive insight discovery requires significant manual effort. This paper fills the gap by integrating automated insight mining with interactive data visualization and striking a proper balance between them to facilitate insight discovery and exploration. Specifically, we regard data insights as a special type of data and further present InsightMap, a novel visualization approach that uses the map metaphor to provide a quick overview and in-depth exploration of different data insights, where a metric is proposed to measure the similarity between different insights. The effectiveness and usability of InsightMap are demonstrated through extensive case studies and in-depth user interviews.

Paper Structure

This paper contains 20 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Design Considerations
  4. System Overview
  5. Insight Mining and Processing
  6. Insight Extraction
  7. Insight Similarity
  8. Visualization
  9. Data View
  10. Insight Map View
  11. Insight Map
  12. Subspace List
  13. Individual Insight View
  14. Interaction Design
  15. Case Study
  16. ...and 5 more sections

Figures (3)

  • Figure 1: InsightMap consists of two major components: insight mining and visualization. The insight mining module extracts 8 types of different insights from the multi-dimensional dataset, while the visualization module provides an integrated view of overall distribution and details.
  • Figure 2: The user interface of InsightMap . It consists of (A) Drop-down Menus for parameter and visualization settings, (B) Data Distribution View for overall attribute distributions, (C) Subspace Filtering View for filtering subspaces via attribute axes, (D) Insight Score View for insight significance and impact, (E) Insight Map View for visualizing insights as glyphs and dots, and (F) Individual Insight View for detailed insight inspection. This figure showcases automated data insights of the NBA dataset (Section \ref{['sec-case-study']}).
  • Figure 3: The glyph design for individual insights.