Mining personally important places from GPS tracks

Zhou Changqing, Nupur Bhatnagar, Shashi Shekhar, Loren G Terveen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Scopus citations

Abstract

The discovery of a person's personally important places involves obtaining the physical locations for a person's places that matter to his daily life, and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, e.g., "home", "work"' or "Northwest Health Club". It is a challenge, to map from physical locations to personally meaningful places because GPS tracks are continuous data both spatially and temporally, while most existing data mining techniques expect discrete data. Previous work has explored algorithms to discover personal places from location data. However, they all have limitations. Our work proposes a two-step approach that discretized continuous GPS data into places and learns important places from the place features. Our approach was validated using real user data and shown to have good accuracy when applied in predicting not only important and frequent places, but also important and not so frequent places.

Original languageEnglish (US)
Title of host publicationWorkshops in Conjunction with the International Conference on Data Engineering - ICDE' 07
Pages517-526
Number of pages10
DOIs
StatePublished - Dec 1 2007
EventWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007 - Istanbul, Turkey
Duration: Apr 15 2007Apr 20 2007

Other

OtherWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007
CountryTurkey
CityIstanbul
Period4/15/074/20/07

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