augmenting shared personal calendars joe tullio jeremy goecks elizabeth d. mynatt david h. nguyen
TRANSCRIPT
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.
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
Calendars: Three Interacting Perspectives
Single-user calendar– Calendar work
Interpersonal communication– Assessing availability
– Meeting scheduling
Socio-technical evolution– Privacy and defaults
Calendars: Three Interacting Perspectives
Single-user calendar– Calendar work
Interpersonal communication– Assessing availability
– Meeting scheduling
Socio-technical evolution– Privacy and defaults
Calendars: Three Interacting Perspectives
Single-user calendar– Calendar work
Interpersonal communication– Assessing availability
– Meeting scheduling
Socio-technical evolution– Privacy and defaults
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
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
Overview
Motivations: Calendar studies and perspectivesAugur Design
–Setting–Architecture
•Component Technologies
–Interface Design•Calendar browser and visualizations•Access count
Future WorkConclusion
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
Augur System
Architecture
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.
Augur Bayesian Network
Extracting context with support-vector machines (SVMs)
Classifier – finds hyperplane that maximizes distance between two classes
Application: text classificationAugur: Apply SVMs to calendar text to identify role,
location, event type.Results:
– Event Type 80%– Location 82%– Role: not enough data yet
Event matching
Task: Find co-scheduled eventsIndividual 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 eventsCorrectly identified 94% of matches
– 14% false positive, 6% false negative
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
Augmented Personal Calendar
Augmented Colleague Calendar
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
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?
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)
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?
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
