Extracting Places from Traces of Locations - ia Tech：从地点提取地点--佐治亚州理工学院.ppt

Extracting Places from Traces Extracting Places from Traces of Locationsof LocationsPaper AuthorsPaper AuthorsJongJongHeeHee Kang KangBenjamin StewartBenjamin StewartWilliam William WelbourneWelbourneGaetanoGaetanoBorrielloBorrielloPowerPoint AuthorPowerPoint AuthorMichael CookMichael CookMichael CookMichael Cook??44thth puter Science (Junior) puter Science (Junior)??Co-Co-opingoping at at SynovusSynovus??InterestsInterests??DatabasesDatabases??working??Web DevelopmentWeb Development??Twin brotherTwin brotherThe ProblemThe Problem??Location aware systems today are limitingLocation aware systems today are limiting??Place: An area of importance to a userPlace: An area of importance to a user??Usage Example:Usage Example:??Cell phone goes to Cell phone goes to ““silentsilent”” mode when mode when entering a classroomentering a classroomIdeal 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 placesTracking User MovementTracking User Movement??Place Lab access Place Lab access pointspoints??Works indoorsWorks indoorsPopular Clustering AlgorithmsPopular Clustering AlgorithmsK-meansK-meansGaussian mixture modelGaussian mixture modelLarge amounts putationLarge amounts putationTime-Based ClusteringTime-Based Clustering??Streaming putation??Small clusters ignoredSmall clusters ignored??Time threshold and Time threshold and distance threshold distance threshold can be changedcan be changedTime-Based Clustering ResultTime-Based Clustering ResultChanging Distance and TimeChanging Distance and TimeChanging Distance and TimeChanging Distance and Timed=30m t=300secd=30m t=300secd=50m t=300secd=50m t=300secd=300m t=600secd=300m t=600sec