1. iDiary: From GPS Signals to a Text-Searchable Diary
Den Feldman, Andrew Sugaya, Cynthia Sung, Daniela Rus
CSAIL LAB, MIT
Sensys '13

2. Goals of the paper
Provides "text-searchable" database of locations and activities by taking signals generated from GPS streams

3. Scenarios Possible scenarios For a user, For a groups of users,
What are the restaurants I visited during the last weekend?
What are the roads I drove on today?
For a groups of users,
Where did I meet with X during the last weekend?
When did Y and X meet for the first time?

4. Challenges
Translating GPS data into useful human-understandable information
(a) The data is huge
1Gbyte of data per day for a single smartphone
(b) The user interface of existing GPS applications target map-navigation
(c) There were very little studies, both in theory and practice

5. Related work Systems for analyzing location data
collecting and storing mobile sensor data
ex) determine transportation modes and environmental impact
ex) show a list of previously visited locations
Textual description of activity
recognizing human activities from raw data streams
ex) determine user's activity based on location information and accelerometer data
manually labeled activities
Semantic Compression
Compressing GPS data
Coresets are frequently used
Text Search
coresets.

6. iDiary Bob's semantic map Bob은 주중에 매일 집에서 직장에 간다. 주말에는 집 근처 공원에 간다.
직장에 가기 위해 기차나 자가용을 탄다.
가끔 직장에 가는 길에 아이를 유치원에 내려준다.
가끔 직장에 가는 길에 주유소에 거쳐 간다.
가끔 새로운 곳에 가기도 하지만 주로 집, 직장, 유치원, 공원, 주유소에 방문한다.

7. GPS data storage Collecting the signal Energy saving
Bob installs the application on his mobile device(iPhone)
the application collects his GPS points(traces and signals)
the application transmits the to lab's server(done in the background)
Energy saving
save battery by switching off the GPS when the user is stationary for ten minutes
switched back on once the user moves, which is detected using wi-fi and/or cell towers

8. GPS data storage Compressing the signal
a lot of redundant information can be removed
when people move from place to place, their trajectories can usually be described using linear route
approximate the trajectory by a small sequence of k segments(coreset)
Trajectory clustering on coresets
n GPS points
k routes
m motion patterns

9. Coresets
n=11, {p1,p2, .. ,p11}, k=5

10. Textual Description of Activity
Two Database tables
2nd table
1st table
begin time
end time
route code
direction
average speed
route code
begin point
end point
(latitude, longitude)
(latitude, longitude)
->for each linear route (k개)
->for each motion pattern(m개)

11. Textual Description of Activity
From numeric tables to text
turns each route code into human-readable and searchable text
reverse geo-coding
ex) Starbucks, Anam street..
use Google Maps with latitude and longitude coordinates
From text to activities
ex) query "sandwich" -> return "Subway"
Use LSA(Latent Semantic Analysis)
each activity is a linear combination of words

12. User Interface

13. User Interface

14. User Interface

15. User Interface
Query search
Ex) restaurants?

16. Experimental results System setup 6 users
using smartphone applications for a period of a year(9GB data)

17. Experimental results
datasets

18. Experimental result
Relevancy