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Place Discovery for Location-aware Wireless Mobile Applications

Place Discovery for Location-aware Wireless Mobile Applications. “Smartphones overtook sales of PDAs late last year.”

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Place Discovery for Location-aware Wireless Mobile Applications

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  1. Place Discovery for Location-aware Wireless Mobile Applications “Smartphones overtook sales of PDAs late last year.” “Just slip in a memory card loaded with the necessary software, and it will automatically load the road maps of an entire country and hook up to the wirelessly connected GPS module that pinpoints the location to a few yards (meters).” Students: Changqing Zhou, Pam LudfordStaff Scientist: Dan Frankowski Faculty: Loren Terveen, Shashi Shekhar Algorithm: DJ-Clustering Problem Certain mobile applications will need a list of places users commonly go to. However: * Can an algorithm accurately discover users’ places?* How do people’s concepts of place relate to physical locations? * How do people describe places? Cluster A & B Density- joinable(p, o, q are GPSlocations) A Cluster is Formed where density is high Precision = |BD| / |D| Recall = |BD| / |B| SurpriseFactor = |DM| / |D| DJ-Clustering Concept Using DJ-Clustering to Discover a User’s Places DJ-Clustering Evaluation Results Empirical Experiment • Our algorithm can accurately discover people’s places: • Subjects: 28 subjects • Different life stages: 20 year old college students, 40 year old working parents, or 60 year old retirees. • Different travel modes: walking, biking, bus, or car. • Data collection: 3 weeks • GPS-enabled Motorola i88s cell phone with Nextel service. • Take a GPS reading every minute and send it to a server. • Subjects kept a diary of the places they visited each day. • Interview preparation: • Ran our discovery algorithm on each subject’s location dataset. • Print out each subject’soverview maps showing discovered places • Print out a table of each subject’s baseline places • Interviews: semi-structured • Subjects matched baseline places to the discovered places. • Subject rated the importance of their places, • How subjects conceived of the shape of their places • How subjects described their places • People give different names to places depending on contextual variables: Evaluation Metrics for DJ-Clustering Algorithm • The places people go to can’t always be represented in systems by a single latitude/longitude point: A subject says he doesn’t always go to the same grocery store: he chooses the one that’s most convenient when he needs to shop. To him, grocery stores are one place with multiple locations. (D, C, E).Multiple Dot A Subject's Neighborhood Region A Subject's Path for walking his dog For more information, contact Changqing Zhou, czhou@cs.umn.edu, Computer Science, University of Minnesota. This work was partially supported by grants from the NSF(IIS 03-07459 and IIS 03-08018) Excerpt from the interview table

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