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

2. Introduction - 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?
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

3. Introduction – location information
Physical location
Provided by positioning systems
GPS: (122.39, , 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
11/19/2018
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
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

5. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
Outline
Introduction
Input and output
Algorithm for determining Semantic Location
Experimental results
Related Works
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

6. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
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)
11/19/2018
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
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

8. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
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
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

9. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
Outline
Introduction
Data Model
Algorithm for determining Semantic Location
Experiment
Related Works
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

10. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
Algorithm
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

11. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
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)
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

12. Stay extraction details (prior work)
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
11/19/2018
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
11/19/2018
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.
11/19/2018
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
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

16. At end of step 3: A set of Semantic Location candidates
Location name (BestBuy)
Semantic category
Business type (electronics store; theater),
office
home
Street address
11/19/2018
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
11/19/2018
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
11/19/2018
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
11/19/2018
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.
11/19/2018
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
11/19/2018
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
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

23. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
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
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

24. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
Outline
Introduction
Data Model
Determination of Semantic Location
Experimental results
Related Works
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

25. Experimental data and setting
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
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

26. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
Experimental Results
96% correctness for all stays
76% stays: home, office
Non-frequent stay: 90%
Remove home/office stays
WSA WSC WP
Original city
New city
2 weeks
3 weeks
4 weeks
(1, 1, 1)
82.3
86.1
77.5
(1, 2, 3)
89.2
91.9
79.4
(1, 3, 2)
78.1
79.7
81.4
76.9
(2, 1, 3)
86.2
89.4
72.2
(2, 3, 1)
76.3
(3, 1, 2)
78.8
81.9
82.5
68.4
(3, 2, 1)
76.4
77.4
76.1
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

27. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
Outline
Introduction
Data Model
Determination of Semantic Location
Experiment
Related Works
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

28. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
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
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

29. Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
Conclusion
11/19/2018
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC