Forecasting Presence and Availability

Joe Tullio

CS8803

Overview

Why do this? Survey of projects

Precursors/influences Coordinate Awarenex/Work rhythms Learning locations using GPS “Lighter” applications

Augur Current incarnation Evaluation/future plans

Motivation

Why do this kind of prediction?

Why now?

Precursors

Media spaces (CRUISER system) Portholes Beard et al – assigned priorities to events

Priority was accorded a level of transparency So meeting scheduling involved overlaying calendars

Worked well enough in the lab, but saw less success in the workplace. Why?

Automatic meeting scheduling tools IM status – focus on current state of availability

Coordinate (Horvitz et al)

Preceded by Priorities Prioritize incoming notifications Relay to a mobile device if important enough

Location was first determined by idle time Later added input from other sources Calendar, vision, audio levels

Coordinate (continued)

Intent: Answer broad range of queries“When will X return?”

“When will X be available?”

“Will X attend the meeting?”

“When will X have access to a desktop machine?”

Coordinate (continued)

Method: collect lots of dataCalendar, computer activity, devices used, email

contents, meeting information, 802.11 location tracking

Estimates of attendance augmented with hand-labeling when necessary

Employee directory establishes professional relationships between users

Construct custom Bayesian networks appropriate to the query

Example

Rhythm modeling (Begole et al)

Idea: people exhibit rhythms in their day-to-day work

Capture those rhythms by recording email, IM, phone activity, computer use

Visualize them and attempt to build models representing them

Example

Building the models

Expectation maximizationDiscover transitions in activity

Cluster similar periods of inactivity

Refine

Label transitions through simple matchingAround 12 or 1 is lunch

Recurring transitions named after calendar events, if they exist

Location changes named after location, duh

Other visualizations

Compressed

Gradient

Probabilities

Privacy

How much to display, and to whom? Ideas:

Expose more over time to simulate familiarization

Expose only what is needed to answer a given question

But how to explain or give context?

Location Modeling Using GPS (Ashbrook and Starner) Location modeling as opposed to availability Uses?

Encourage serendipitous meetings Intelligent interruption Meeting scheduling

Step 1: find places

Can’t just give people raw GPS coordinates

Define a place as any location where one spends time t

t chosen arbitrarily here

Places become locationsUse a clustering algorithm to group nearby

places

Also concept of sublocationsRun clustering alg. On points within locations

Example

Adding time

All these locations are time-stamped, so…Can identify order of places visited and predict

transitions between places

Markov model – one for each location, transitions to every other location

Currently can predict where one will go next, but not when

Can variance in arrival/departure indicate importance?

Machine learning

Most of these projects require a large corpus of data with discernable patterns of activityWhat happens when those patterns deviate or

change?

Incorporate learning or user interaction

Broaden classes in accordance with their current fit to the data

Coordinate – include more cases that are ‘relevant’

Rhythms/GPS – Weigh recent data more heavily

Predicting interruptibility using sensors

Hudson et al

Goal: determine good time to interrupt

Method: record people in their offices(A/V)Self-report interruptibility using ESM (~2/hr)

Manually code situations (602 hours)

Hypothesize which sensors would provide the most information about interruptibility

Results

Building models

Simple 2-class classification problem

Try:Decision trees (78.1%)

Naïve Bayes (75.0%)

Adaboost w/decision stumps (76.9%)

Support-vector machines (77.8%)

Predictions improve when tested per-subject as opposed to across subjects

First few sensors account for most of the accuracy:Phone, talk, # of guests, sitting, writing, keyboard