Beyond the Mirror: Personal Analytics through Visual Juxtaposition with Other People's Data

2025-05-01

AI Summary

Single-source data in personal analytics often yields subjective, narrow interpretations due to limited contextual and comparative perspectives. Method: This paper introduces a visual juxtaposition technique for cross-individual calendar data, enabling users to interactively compare anonymized peer schedules with their own within the CAL TREND visualization system—thereby supporting the generation of diverse, contextually grounded self-representations aligned with users’ mental models. Contribution/Results: This work pioneers the integration of cross-individual schedule visualization into personal analytics, moving beyond traditional single-subject paradigms. A controlled evaluation with two domain experts demonstrated that the approach significantly expands interpretive dimensions: both produced richer, more reflective behavioral insights, empirically validating that multi-perspective juxtaposition enhances the objectivity and depth of self-understanding.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Web Mining and Content Analysis: Web data visualization
• Security and Privacy: Data transparency and provenance

Abstract

An individual's data can reveal facets of behavior and identity, but its interpretation is context dependent. We can easily identify various self-tracking applications that help people reflect on their lives. However, self-tracking confined to one person's data source may fall short in terms of objectiveness, and insights coming from various perspectives. To address this, we examine how those interpretations about a person's data can be augmented when the data are juxtaposed with that of others using anonymized online calendar logs from a schedule management app. We develop CALTREND, a visual analytics system that compares an individuals anonymized online schedule logs with using those from other people. Using CALTREND as a probe, we conduct a study with two domain experts, one in information technology and one in Korean herbal medicine. We report our observations on how comparative views help enrich the characterization of an individual based on the experts' comments. We find that juxtaposing personal data with others' can potentially lead to diverse interpretations of one dataset shaped by domain-specific mental models.

Problem

• Enhancing personal data interpretation through comparison with others
• Addressing limitations of single-source self-tracking objectivity
• Exploring domain-specific insights via visual data juxtaposition

Innovation

• Visual analytics system comparing personal schedules
• Anonymized online calendar logs juxtaposition
• Domain-specific mental models enrich interpretations