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Extracting Semantic Location from Outdoor Positioning Systems

Extracting Semantic Location from Outdoor Positioning Systems. Juhong Liu, Ouri Wolfson, Huabei Yin University of Illinois at Chicago. Introduction - Context. Context Environment in which a user operates Location info., environmental info. (weather), social info. (who is around), etc.

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Extracting Semantic Location from Outdoor Positioning Systems

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  1. Extracting Semantic Location from Outdoor Positioning Systems Juhong Liu, Ouri Wolfson, Huabei Yin University of Illinois at Chicago

  2. Introduction - Context • Context • Environment in which a user operates • Location info., environmental info. (weather), social info. (who is around), etc. • Location information: important aspect of context • Reminders • Physician’s office: request a prescription • Movie theatre -> turn off phone • User interface • Computer store: apple (computer) • Grocery store: apple (fruit) • Places I’ve been (analogous to “Stuff I’ve Seen”) • Where was I on 8/15/02 at 2pm? • When was the last time I saw my dietician? Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  3. Introduction – location information • Location information • Physical location • Provided by positioning systems • GPS: (122.39, 239.11, 11:20am) • Unreadable by users • Semantic location • Not directly provided by positioning systems • Dominick’s grocery store, 1340 S. Canal St. • Dermatologist’s office • Home • Useful to users Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  4. Introduction – problem statement • Physical location -> semantic location • The place the user stays • Devices • Outdoor positioning systems • Internet access Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  5. Outline • Introduction • Input and output • Algorithm for determining Semantic Location • Experimental results • Related Works Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  6. Main Input and Output • Input: Trajectory: T =(x1, y1, t1), (x2, y2, t2), …, (xn, yn, tn) • Output 1: Semantic location • Location name (BestBuy) • Semantic category • Business type (electronics store), • office • home • Street address • Output 2: Semantic location log file • (date, begin_time, end_time, semantic location) Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  7. Online and offline versions • Online: determine the current location • On mobile device • Based on incomplete trip trajectory • Offline: Determine multiple past locations • At pc • Based on complete trip trajectory Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  8. Auxiliary inputs • Profile • Calendar – (event date, semantic location) • Address Book – (phone number, semantic location) • Phone Call List – (calling date, semantic location) • Web Page List - (visiting date, semantic location) • Destination List – (searching date, semantic location) • User’s Feedback • Confirmed list • Denied list Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  9. Outline • Introduction • Data Model • Algorithm for determining Semantic Location • Experiment • Related Works Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  10. Algorithm Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  11. Step1 - Stay extraction • Stay • Loss of GPS signal • To spend at least min_time in an area with the diameter no larger than d. • (stay_position, date, stay_start, stay_end) Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  12. Stay extraction details (prior work) • Extraction • Stay generation • Last min_time, the physical positions are within d. • Stay_position – center of these physical locations • Stay_start, stay_end • Stay extension and finish • A physical position p following a stay • Distance(stay_position, p) <= d/2 –> stay_end extended • Distance(stay_position, p) > d/2 –> current stay finishes • Min_time=5, d as shown • stay_postion: p4 • Stay_start: p3 • Stay_end: p8 Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  13. Step2 – Street address candidates • Reverse Geocoding • Physical location (stay_position) -> street address • Traditional geocoding method • Nearest street address • Incorrect result Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  14. Step2 – Street address candidates(2) Street address candidates: the street addresses within k meters (graph distance) from stay_position. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  15. Step3-semantic location candidates • Street address candidates -> semantic location candidates • Yellow pages • Such as switchboard • Profile • Calendar, Address Book, Phone Call List, Web Page List, Destination List, User's Feedback Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  16. At end of step 3: A set of Semantic Location candidates • Semantic location • Location name (BestBuy) • Semantic category • Business type (electronics store; theater), • office • home • Street address Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  17. Step4- three utilities calculation • For each semantic location SL in set of candidates compute: • Semantic category (SC) utility: how likely is the semantic category given user’s history • Street address (SA) utility: how likely is the street address given the stay location • Profile (P) utility: How well SL matches the profile Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  18. Step4.1- Semantic category utility • Assumption • Users visit semantic categories habitually. • Semantic category history • Time information and semantic category • format • workday_or_weekend, T1 • start time of stay, T2 • length_of_time_spent_there, T3 • semantic category C • Can be extracted from semantic log file Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  19. Step4.1- Semantic category utility for a semantic location SL • Probability of semantic category C for SL is: P(C |T1, T2, T3) • Intuitively: • the probability that a stay with the correspondent time information visits semantic category C (e.g. a theater). • P(C |T1, T2, T3) is computed by Bayes Model using the semantic location log file: • C - a semantic category • Ti - the time information • Z – for normalization Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  20. Step4.2-Street address Utility for SL • For the stay_position (x, y) the street address of the projection point p on each street has the highest probability • The utility of a street address is proportional to its smallest (route) distance from a projection point. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  21. Step4.3 – profile utility of SL • SL in SLC has a higher profile utility, if matches: • Calendar • Address book • Phone Call List • Web Page List • Destination List • User’s feedback: confirmed list • SL in SLC has a lower profile utility, if matches: • User’s feedback: denied list Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  22. Step5 - Semantic Location determination • For each SL in SLC, weighted sum of three utilities: • Weight setting (WSC, WSA, WP) • Equal weighting • Rank weighting • Ratio weighting Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  23. Initialization • At outset: • No semantic category history • No feedback history • An Initialization is necessary • Several weeks • Build initial SC history using credit card statement, with user corrections • Build feedback history Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  24. Outline • Introduction • Data Model • Determination of Semantic Location • Experimental results • Related Works Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  25. Experimental data and setting • Data • GPS • The trip of a student for 4 months • The student gives feed back every week • Weight setting (WSC, WSA, WP) • Equal weighting: (1,1,1) • Rank weighting: (1,2,3), (1,3,2),(2,1,3), (2,3,1), (3,1,2), and (3,2,1) • Initialization time • 2 weeks, 3 weeks and 4 weeks • New city simulation • Remove the information in User’s feedback Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  26. Experimental Results • 96% correctness for all stays • 76% stays: home, office • Non-frequent stay: 90% • Remove home/office stays Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  27. Outline • Introduction • Data Model • Determination of Semantic Location • Experiment • Related Works Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  28. Related Works • Indoors • Easyliving (determine meeting room, lab, etc) • Outdoors • Cyberguide • Tour guide: points of interest around the user’s location • Current semantic location not extracted • Commotion • Significant locations pick up • The user names the locations, gives to_do lists • To_do lists come out, when at correspondent location • Lachesis • Stays pick up • User provides semantic location for stay • Markov model built to predict future stay Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

  29. Conclusion Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

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