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Augmenting Shared Personal Calendars

Augmenting Shared Personal Calendars. Joe Tullio Jeremy Goecks Elizabeth D. Mynatt David H. Nguyen. Motivation. Domain: Electronic (Shared) Calendars Studies: Palen, L. (1999) "Social, Individual & Technological Issues for Groupware Calendar Systems", CHI'99 .

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Augmenting Shared Personal Calendars

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  1. Augmenting Shared Personal Calendars Joe Tullio Jeremy Goecks Elizabeth D. Mynatt David H. Nguyen

  2. Motivation Domain: Electronic (Shared) Calendars Studies: Palen, L. (1999) "Social, Individual & Technological Issues for Groupware Calendar Systems", CHI'99. Grudin, J. and Palen, L. (1997) "Emerging Groupware Successes in Major Corporations: Studies of Adoption and Adaptation", WWCA'97. “Calendar work” + • Locating colleagues • Assessing availability • Regulating privacy

  3. Calendars: Three Interacting Perspectives Single-user calendar • Calendar work Interpersonal communication • Assessing availability • Meeting scheduling Socio-technical evolution • Privacy and defaults

  4. Calendars: Three Interacting Perspectives Single-user calendar • Calendar work Interpersonal communication • Assessing availability • Meeting scheduling Socio-technical evolution • Privacy and defaults

  5. Calendars: Three Interacting Perspectives Single-user calendar • Calendar work Interpersonal communication • Assessing availability • Meeting scheduling Socio-technical evolution • Privacy and defaults

  6. Additional practices Single-user calendar • Ad-hoc naming • Inaccurate calendars Interpersonal communication • “Ambush” vs. “waylay” • Media choice • Awareness Socio-technical evolution • Privacy and accountability • Social norms

  7. Augur System: Goals Support personal calendaring practices (ad hoc naming) “Improve” calendar accuracy through predictive models Enable informal communication practices (“ambushing”, awareness) Facilitate privacy management by visualizing access history

  8. Overview Motivations: Calendar studies and perspectives Augur Design Setting Architecture Component Technologies InterfaceDesign Calendar browser and visualizations Access count Future Work Conclusion

  9. Setting University setting (Students, faculty, staff) • Single workgroup at Georgia Tech College of Computing Numerous public meetings/courses across multiple buildings Rapid schedule turnover (term changes) 9 participants (7 students, 1 faculty, 1 staff) 3 months, 2600+ events

  10. Augur System Architecture

  11. Bayesian network Compact means of encoding uncertainty • Nodes represent variables • Links represent relationships between them Probabilistic inference • Known variables serve as evidence • Bayesian updating generates predictions for unknown variables For more details: • Mynatt, E. and Tullio, J. Inferring Calendar Event Attendance, IUI’2001.

  12. Augur Bayesian Network

  13. Extracting context with support-vector machines (SVMs) Classifier – finds hyperplane that maximizes distance between two classes Application: text classification Augur: Apply SVMs to calendar text to identify role, location, event type. Results: • Event Type 80% • Location 82% • Role: not enough data yet

  14. Event matching Task: Find co-scheduled events Individual calendaring styles make this difficult • (e.g., “GVU brown bag” vs “GVU bb”) TF/IDF algorithm • Documents represented as weighted word vectors • Dot product measures document similarity Threshold on temporally synchronized events Correctly identified 94% of matches • 14% false positive, 6% false negative

  15. Calendar app Web-based (JSP) shared calendar Can browse own calendar or those of colleagues Attendance predictions represented as color coding Colleagues represented iconically within co-scheduled events; details available as tooltips Allows side-by-side comparison

  16. Augmented Personal Calendar

  17. Augmented Colleague Calendar

  18. Access history • Glance/look/interact paradigm • Glance: Border color indicates access frequency • Look: Actual number of accesses • Interact: Detailed info on accesses • Work in progress

  19. Related work: Modeling/Prediction: • Ambush (Mynatt & Tullio, IUI 2001) • Tempus Fugit (Ford et al, CIKM 2001) • GPS (Ashbrook & Starner, CHI 2002) • Coordinate (Horvitz et al, UAI 2002) • Work rhythms (Begole et al, CSCW 2002) More to come! Learn models from data or construct by hand?

  20. Related work: Calendar Visualization: • Fisheye view (Bederson et al, 2000) • 3D Calendars (Mackinlay et al, 1994) • Transparency (Beard et al, 1990) Accountability: • Social translucence (Erickson et al, 2000) • History-enriched objects (Hill et al, 1993)

  21. Future work Deployment • Participants among several research groups/occupations at the College of Computing • Measure model accuracy over time • Determine when/how predictions are used Interactive models • Address learning time • Control, trust promote adoption • Sensitivity to social environment • Heuristics vs. training Bayes?

  22. Augur: A probabilistic shared calendar Calendars shared from personal mobile devices A probabilistic model drives predictions of attendance at future events Text processing identifies co-scheduled events Visualize predictions in a browsable calendar Reporting accesses promotes accountability

  23. Thanks. http://www.cc.gatech.edu/fce/ecl jtullio@cc.gatech.edu jeremy@cc.gatech.edu

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