Same Data, Different Audiences: Using Personas to Scope a Supercomputing Job Queue Visualization

2025-07-23

AI Summary

Existing supercomputing job queue visualization tools struggle to simultaneously satisfy the heterogeneous requirements of scientists, machine learning researchers, and system operators. Method: This paper proposes Guidepost, a notebook-embedded visualization framework that innovatively integrates HCI-inspired persona analysis into visualization design. It systematically identifies cross-role shared tasks (e.g., wait-time assessment, trend detection) and role-specific tasks (e.g., prediction accuracy analysis), enabling a dual-mode paradigm: interactive visualizations for common tasks and programmable Python APIs for customized analyses. Built upon real job logs, it supports dynamic visual analytics, chart interaction, and script-based extensibility. Contribution/Results: Evaluation with nine domain experts demonstrates that all three user groups efficiently complete shared tasks while flexibly tailoring personalized analytical workflows. The results validate Guidepost’s practicality and scalability in multi-audience, multi-objective visualization scenarios.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Domain-specific visualizations sometimes focus on narrow, albeit important, tasks for one group of users. This focus limits the utility of a visualization to other groups working with the same data. While tasks elicited from other groups can present a design pitfall if not disambiguated, they also present a design opportunity -- development of visualizations that support multiple groups. This development choice presents a trade off of broadening the scope but limiting support for the more narrow tasks of any one group, which in some cases can enhance the overall utility of the visualization. We investigate this scenario through a design study where we develop extit{Guidepost}, a notebook-embedded visualization of supercomputer queue data that helps scientists assess supercomputer queue wait times, machine learning researchers understand prediction accuracy, and system maintainers analyze usage trends. We adapt the use of personas for visualization design from existing literature in the HCI and software engineering domains and apply them in categorizing tasks based on their uniqueness across the stakeholder personas. Under this model, tasks shared between all groups should be supported by interactive visualizations and tasks unique to each group can be deferred to scripting with notebook-embedded visualization design. We evaluate our visualization with nine expert analysts organized into two groups: a "research analyst" group that uses supercomputer queue data in their research (representing the Machine Learning researchers and Jobs Data Analyst personas) and a "supercomputer user" group that uses this data conditionally (representing the HPC User persona). We find that our visualization serves our three stakeholder groups by enabling users to successfully execute shared tasks with point-and-click interaction while facilitating case-specific programmatic analysis workflows.

Problem

• Design visualizations for multiple user groups with same data
• Balance broad scope and narrow task support in visualization
• Evaluate visualization utility across diverse stakeholder personas

Innovation

• Personas categorize tasks for visualization design
• Notebook-embedded visualization supports multiple user groups
• Interactive and scripted tasks balance utility scope