Extracting Places from Extracting Places from Traces of LocationsTraces of Locations

Paper AuthorsPaper AuthorsJong Hee KangJong Hee Kang

Benjamin StewartBenjamin StewartWilliam WelbourneWilliam WelbourneGaetano BorrielloGaetano Borriello

PowerPoint AuthorPowerPoint AuthorMichael CookMichael Cook

Michael CookMichael Cook

44thth Year Computer Science (Junior) Year Computer Science (Junior) Co-oping at SynovusCo-oping at Synovus InterestsInterests

DatabasesDatabases NetworkingNetworking Web DevelopmentWeb Development

Twin brotherTwin brother

The ProblemThe Problem

Location aware systems today are Location aware systems today are limitinglimiting

Place: An area of importance to a Place: An area of importance to a useruser

Usage Example:Usage Example: Cell phone goes to “silent” mode when Cell phone goes to “silent” mode when

entering a classroomentering a classroom

Ideal SituationIdeal Situation

Requires little user interactionRequires little user interaction All important places are locatedAll important places are located No false positivesNo false positives Works for indoor and outdoor placesWorks for indoor and outdoor places

Tracking User MovementTracking User Movement

Place Lab access Place Lab access pointspoints

Works indoorsWorks indoors

Popular Clustering Popular Clustering AlgorithmsAlgorithms

K-meansK-means Gaussian mixture modelGaussian mixture model

Large amounts of computationLarge amounts of computation

Time-Based ClusteringTime-Based Clustering

Streaming Streaming computationcomputation

Small clusters Small clusters ignoredignored

Time threshold and Time threshold and distance threshold distance threshold can be changedcan be changed

Time-Based Clustering Time-Based Clustering ResultResult

Changing Distance and Changing Distance and TimeTime

Changing Distance and Changing Distance and TimeTime

d=30m t=300secd=30m t=300sec d=50m t=300secd=50m t=300sec d=300m t=600secd=300m t=600sec

Frequently Visited PlacesFrequently Visited Places

Not much time is spent at the place, Not much time is spent at the place, but frequently visitedbut frequently visited

Different time threshold neededDifferent time threshold needed How to differentiate the place and in-How to differentiate the place and in-

transit motion?transit motion?

Future WorkFuture Work

Automatic labeling of placesAutomatic labeling of places Can use user’s calendarCan use user’s calendar

Learn proper distance and time Learn proper distance and time thresholds automaticallythresholds automatically

CritiqueCritique

Easy to read and Easy to read and understandunderstand

Cool idea with Cool idea with practical practical applicationsapplications

WiFi hotspots not WiFi hotspots not always availablealways available

Trying to do too Trying to do too much at oncemuch at once Long duration Long duration

placesplaces Short duration, Short duration,

frequent placesfrequent places

Questions?Questions?