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Inferring Calendar Event Attendance

This study by Elizabeth Mynatt and Joe Tullio at Georgia Institute of Technology delves into the challenges of event scheduling amid irregular personal schedules and overwhelmed communication channels, proposing a Bayesian model for better attendance predictions. The research explores the implications of conflicting appointments, privacy concerns, and the push-pull dynamics in calendar usage. By integrating learning through attendance logs and visualizations, the system aims to simplify calendar management. Future work includes automatic attendance sensing and expanding application domains to boost system efficiency.

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Inferring Calendar Event Attendance

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  1. Inferring Calendar Event Attendance Elizabeth Mynatt and Joe Tullio Graphics, Visualization, and Usability Center College of Computing Georgia Institute of Technology http://www.cc.gatech.edu/fce/ecl

  2. Informal Communication • Brief face-to-face conversation • Regular meetings • Irregular personal schedules • Overwhelmed communication channels • “Ambushing” • “I’ll ask my advisor during the seminar” • “You can find him at the faculty meeting”

  3. Problems with Calendars • Accuracy • Recurring appointments never removed • Conflicting appointments – which will user attend? • All-day events supercede routine ones • Privacy • Push vs. Pull • Primarily for personal use

  4. Overview • A Probabilistic Calendar • Bayesian model • System implementation • Learning through attendance logs • Application: Group calendar display • Visualizations • Related Work • Future Work – Calendars as Sensors

  5. Ambush System Diagram

  6. Attendance Model

  7. Attendance Model • Priority • Recurring? • Role? • Type of event? • Availability • Location • Length • All-day event?

  8. Ambush Experiences • Evaluation • Single user • 2.5 months, 200 appointments • Routine events stabilize • Novel events a problem • Hard to parse into node evidence • No prior knowledge

  9. Simple Visualizations

  10. Inside the Black Box • Graphical explanation of Bayesian model • Skeptical of output • Diagnosis of model • Build sense of trust with inference system • Provide quick analysis of relative node influences

  11. Black Box Visualization

  12. Related Work • Office Studies: • Groupware calendars: Palen, 1999 • Office communication: Ehrlich 1987 • Applications • Context-Aware Office Assistant: Yan, 2000 • Attention-sensitive Alerting: Horvitz et al, 1999 • Visualizing uncertainty/explanations • Visual Calendar: Beard et al, 1990 • Explanation: Druzdel, 1996 • Graphical Explanations: Madigan et al, 1996

  13. Future Work • Automatic sensing of attendance • Active badges • iButtons • More training, more users • Formal studies - iterate visualizations • Calendars as Sensors • Other application domains? • Ubicomp, Context-aware computing

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