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 language | English (US) |
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Title of host publication | Workshops in Conjunction with the International Conference on Data Engineering - ICDE' 07 |
Pages | 517-526 |
Number of pages | 10 |
DOIs | |
State | Published - Dec 1 2007 |
Event | Workshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007 - Istanbul, Turkey Duration: Apr 15 2007 → Apr 20 2007 |
Other
Other | Workshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007 |
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Country | Turkey |
City | Istanbul |
Period | 4/15/07 → 4/20/07 |