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 –Interface Design 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