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A Literature-based Visualization Task Taxonomy for Gantt Charts

Sayef Azad Sakin, Katherine E. Isaacs

TL;DR

The paper tackles the challenge of scaling Gantt-chart visualizations for large-scale event sequences with inter-event dependencies. It proposes a literature-based, multi-layer task taxonomy (29 tasks across 8 groups) and links these tasks to a set of data queries (Q1–Q13) necessary to support interactive analysis. The methodology combines a systematic literature review of 137 papers, filtering to 35 interactive Gantt studies, and a two-round qualitative coding process to derive the taxonomy. Key contributions include the taxonomy, the data-query mapping, and a heatmap illustrating task coverage across papers, offering design guidance and a foundation for scalable, multi-view Gantt visualizations in domains like manufacturing and parallel computing.

Abstract

Gantt charts are a widely-used idiom for visualizing temporal discrete event sequence data where dependencies exist between events. They are popular in domains such as manufacturing and computing for their intuitive layout of such data. However, these domains frequently generate data at scales which tax both the visual representation and the ability to render it at interactive speeds. To aid visualization developers who use Gantt charts in these situations, we develop a task taxonomy of low level visualization tasks supported by Gantt charts and connect them to the data queries needed to support them. Our taxonomy is derived through a literature survey of visualizations using Gantt charts over the past 30 years.

A Literature-based Visualization Task Taxonomy for Gantt Charts

TL;DR

The paper tackles the challenge of scaling Gantt-chart visualizations for large-scale event sequences with inter-event dependencies. It proposes a literature-based, multi-layer task taxonomy (29 tasks across 8 groups) and links these tasks to a set of data queries (Q1–Q13) necessary to support interactive analysis. The methodology combines a systematic literature review of 137 papers, filtering to 35 interactive Gantt studies, and a two-round qualitative coding process to derive the taxonomy. Key contributions include the taxonomy, the data-query mapping, and a heatmap illustrating task coverage across papers, offering design guidance and a foundation for scalable, multi-view Gantt visualizations in domains like manufacturing and parallel computing.

Abstract

Gantt charts are a widely-used idiom for visualizing temporal discrete event sequence data where dependencies exist between events. They are popular in domains such as manufacturing and computing for their intuitive layout of such data. However, these domains frequently generate data at scales which tax both the visual representation and the ability to render it at interactive speeds. To aid visualization developers who use Gantt charts in these situations, we develop a task taxonomy of low level visualization tasks supported by Gantt charts and connect them to the data queries needed to support them. Our taxonomy is derived through a literature survey of visualizations using Gantt charts over the past 30 years.
Paper Structure (18 sections, 2 figures)

This paper contains 18 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Gantt Chart Terminology and Related Work
  3. Gantt Chart Task Taxonomy and Data Queries
  4. Methodology
  5. Gantt chart task taxonomy
  6. Event Overviews
  7. Analyze Derived Data
  8. Search
  9. Manipulating Tracks
  10. Filtering
  11. Exploring dependencies
  12. Modifying the data
  13. Exporting
  14. Data queries for Gantt chart
  15. Event-Related Fetch Queries
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Gantt chart showing a window of time and three tracks.
  • Figure 2: Mapping between data queries and interactive visualization tasks for Gantt charts. Each heatmap cell denotes in how many papers we observed the given visualization task. Tasks observed in each paper are supplemental materials. These counts suggest common tasks to support and possibilities for handling scale. The counts indicate which queries are the most common and may be considered for optimization.