Utilizing Provenance as an Attribute for Visual Data Analysis: A Design Probe with ProvenanceLens

2025-05-17

AI Summary

In existing visualization systems, provenance is typically hard-coded and non-controllable, hindering users’ ability to retrospectively trace analytical actions and engage in metacognitive reflection. Method: We explicitly model provenance as a user-perceivable and manipulable visual attribute. In ProvenanceLens, we dynamically encode provenance along two dimensions—temporality and frequency—using color and size, and support interactive data transformations (e.g., filtering, sorting). Contribution/Results: This work establishes the first paradigm shift from system-embedded provenance mechanisms to user-controllable analytical attributes. Through probe-based design and an exploratory user study (N=16), we demonstrate that users accurately and confidently reconstruct analysis paths; provenance encoding discrepancies with users’ mental models effectively trigger metacognitive reflection; and the approach significantly enhances provenance intuitiveness, usability, and reflective capacity.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Analytic provenance can be visually encoded to help users track their ongoing analysis trajectories, recall past interactions, and inform new analytic directions. Despite its significance, provenance is often hardwired into analytics systems, affording limited user control and opportunities for self-reflection. We thus propose modeling provenance as an attribute that is available to users during analysis. We demonstrate this concept by modeling two provenance attributes that track the recency and frequency of user interactions with data. We integrate these attributes into a visual data analysis system prototype, ProvenanceLens, wherein users can visualize their interaction recency and frequency by mapping them to encoding channels (e.g., color, size) or applying data transformations (e.g., filter, sort). Using ProvenanceLens as a design probe, we conduct an exploratory study with sixteen users to investigate how these provenance-tracking affordances are utilized for both decision-making and self-reflection. We find that users can accurately and confidently answer questions about their analysis, and we show that mismatches between the user's mental model and the provenance encodings can be surprising, thereby prompting useful self-reflection. We also report on the user strategies surrounding these affordances, and reflect on their intuitiveness and effectiveness in representing provenance.

Problem

• Visual encoding of analytic provenance for tracking analysis trajectories
• Modeling provenance as a user-controllable attribute in analysis
• Investigating user strategies for provenance-tracking in decision-making

Innovation

• Model provenance as user-accessible analysis attribute
• Track interaction recency and frequency visually
• Map provenance to encoding channels for reflection