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Extracting Semantic Location from Outdoor Positioning Systems

Explore how semantic location information can enhance user experiences by extracting meaningful data from outdoor positioning systems. This study delves into the concept of semantic location and its importance to users in various contexts. Discover the methodology and algorithms used to determine semantic locations and how auxiliary inputs like profiles and calendars contribute to refining the data.

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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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