augmenting shared personal calendars joe tullio jeremy goecks elizabeth d. mynatt david h. nguyen

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

Thanks.

http://www.cc.gatech.edu/fce/ecl

jtullio@cc.gatech.edu

jeremy@cc.gatech.edu