1. GeoFeed: A Location-Aware News Feed System
Jie Bao Mohamed F. Mokbel Chi-Yin Chow
Department of Computer Science and Engineering
University of Minnesota – Twin Cities
Department of Computer Science City University of Hong Kong \

2. Background Social Networking Services (e.g., Facebook & Twitter)
People on Facebook
More than 500 million active users
50% of our active users log on to Facebook in any given day
Average user has 130 friends
People spend over 700 billion minutes per month on Facebook
Facebook No.1
Twitter No.17th
Recently 5 billion IPO
Become one of the most popular Web services!!!

3. What is News Feeds? News Feed function Examples:
Display a set of messages/news from friends / subscribed news agents
Examples:
Social networking system, i.e., Facebook, Twitter
News Aggregators, i.e., My Yahoo!, iGoogle

4. Motivation Traditional News Feed Motivating Scenarios
Organized by either message issuing time, e.g., Twitter, or some user requirements, e.g., Facebook
Spatial relevance is overlooked, user gets the same news feed from different log on locations
Motivating Scenarios
Travelling user is more interested in the news/messages that are close to her current location to explore the new place
Stationary users may NOT be interested in the news/messages that are issued very far from their locations
If the news feed functionality is aware of the inherent locations of users and messages, more relevant news feed will be delivered

5. “Locations” in Existing Social Networking Systems
Google Latitude
Facebook Place
Twitter Nearby
“Real” Location-Aware News Feed
Social Relevance
Messages from friends/ subscribed news agents
2. Spatial Relevance
Message relevant to the user’s location
Unfortunately not “real” location awareness currently
Share only user’s current location, e.g., Google Latitude
Use location information as a tag , e.g., Facebook Place
View all the messages in a spatial range, e.g., Twitter Nearby

6. Location-Aware News Feeds
Location-Based Messages
Issuer: user/ news agent
Spatial extent: point/range
Location-Aware News Feeds
Recent k spatial relevant messages from each of my friends
Example:
Carol wants her news feed from friends (Alice and Bob)
Alice’s Messages
Message
Content
Spatial
Timestamp M5
Raining S5
14:30 M3
A nice bar S3
14:10 M2
Eating at bar S2
14:04
A location-based query is issued to retrieve the most recent k=2 relevant messages from Alice M2 M6 M5
Bob’s Messages M3 M4
Message
Content
Spatial
Timestamp M6
Local Sale S6
15:30 M4
An accident S4
14:21 M1
Work finished S1
11:40
Carol M1
A location-based query is issued to retrieve the most recent k=2 relevant messages from Bob

7. An Overview of GeoFeed
For a user U with N friends, GeoFeed abstracts location-aware news feed to a set of N location-based queries, such that:
The N location-based queries are fired upon U logging on to the system
Each location- based query is directed to one friend to retrieve the set of k relevant messages
GeoFeed employs three approaches for each location-based query
Spatial Pull approach
Spatial Push approach
Shared Push approach
GeoFeed employs a decision model that decides upon the best approach to evaluate each query such that:
The system computational overhead is minimized
Each user U will get the required news feed in TU time units

8. GeoFeed Preliminary : Problem Formulation
Given:
User location
User friend list
User response time requirement
User activity patterns, i.e., offline time and update frequency
Find:
Best approach among spatial pull, spatial push, and shared push approaches, to evaluate q once u logs on to the system next time
Objective:
Provide location-aware news feed for the user
Guarantee a the response time that u will encounter to get all the requested location-aware news feeds
Minimize the computational overhead for all queries in the system

9. The Spatial Pull Approach in GeoFeed
Do nothing when the user offline
Once the user logs on, compute al the queries for the user
Advantage: No extra overhead during offline period
Disadvantages: High user response time and not efficient for the user with short offline time
Bob
location-based query
Alice
Messages
3. Get cell
2. Alice’s
location
Spatial
Filter
Grid Index
5. Relevant
messages
4. Messages in the cell

10. The Spatial Push Approach in GeoFeed
Maintain a materialized view for the pre-computed messages
Once the user logs on, the answer is ready
Advantage: Users are very happy with very low response time
Disadvantages: System is overwhelmed with maintaining large number of views that may not be necessary
Bob
1. location-based
query
Alice
Materialized
view
3. Range
query
New
message
Other
Friends
Other
Materialized
views
2. Relevant
messages
4.Update
Grid Index

11. The Shard Push Approach in GeoFeed
Shared Push approach
Share one view among queries for the nearby friends
Once the user logs on, the answer is ready
Advantages: Users are still very happy with very low response time, and system overhead could be significantly lower
Disadvantages: Users need to be close enough, continuously check if views can be shared
Bob
1. location-based
query
3. Range
query
Alice
Shared
materialized
view
Filter
New
message
Nearby
Friends
2. Relevant messages
4.Update
Grid Index

12. GeoFeed Cost Model
Spatial pull approach (based on per user-friend evaluation)
Response time
Evaluating the location query
Spatial push approach (based on per user-friend evaluation)
Response time/Query processing cost
Return messages from materialized view
System overhead
Cost to update the materialized view with the user’s the offline time and the friend’s update frequency
Shared push approach (based on per cell evaluation)
Return messages from the shared view with filtering
Cost to update the shared view with the user’s update frequency and friends’ minimum offline time

13. Challenges in Decision Model
Main Challenges:
Guarantee a response time requirement for the user
Do not overwhelm the system
Consider the wide diversity of the user activity patterns in social networking systems, e.g., offline times, update frequencies
To favor user response time
More spatial push approaches will be adapted
System is overkilled to maintain a large number of materialized views and continuous queries
To favor system overhead
More spatial pull approaches may be adapted
Users suffer significant delays to get their news feeds

14. Which is the Best Approach for a Query
System-wide decision
Per-User decision
Per-Query decision
(GeoFeed) A B C D E F A B C D E F A B C D E F A B C D E F OR
Users
Friends
Users
Friends
Consider the wide diversity in user activities in social networking systems e.g., offline times and update frequencies
Users
Friends

15. GeoFeed Decision Algorithm
Step 1. Response Time Guarantee
For each user, this step uses our cost model to decide the MAX number queries (N) to be evaluated by the spatial pull approach
Step 2. Spatial Pull & Push Selection
For each user, this step selects N queries to be evaluated by the spatial pull approach based on our cost model
Step 3. Shared Push Refinement
For each user, this step attempts to share the execution of his/her friends’ queries that are selected to be evaluated by the spatial push approach.

16. Experiments (1/4) Data Sets Experimental Settings
Get final 646,697 tweets issued in State of Minnesota
Use location information in tweets
Coordinate locations
Semantic location, e.g., a city name (use Google Geocoder)
Experimental Settings
Based on a Postgresql database
Based on the statistics from Facebook
A set of evaluation experiments to get the parameters to build the cost model and decision algorithm

17. Experiments (2/4) Inside GeoFeed Decision model Insights:
With the increase of Tu, more spatial pull approaches are selected.
When Tu=0 no spatial pull approaches are applied
When Tu=∞, GeoFeed aims to only minimize the system overhead through employing much of the spatial pull approach.
Comparing two figures shows that with a smaller offline time, more spatial push approaches are applied.
(a) Offline time = 1 hour
(b) Offline time = 8 hours

18. Experiments (3/4) Compare with traditional approaches Insights:
Pure spatial pull has bad response time
Pure spatial push had bad system overhead

19. Experiments (4/4) System overall overhead Insight:
GeoFeed with shared push refinement has the similar response time but saves significant in system overhead

20. System Prototype
Sindbad: A Location-Aware Social Networking System (SIGMOD 2012 demo)

21. Conclusion Location-Aware News Feeds
Social relevance, i.e., a user’s friends/subscribed news agents
Spatial relevance, i.e., messages overlap user’s location
GeoFeed is an efficient system equipped with a smart decision algorithm, which chooses the best approach among spatial pull, spatial push and shared push to evaluate location-aware news feed:
Guarantee the user’s required response time
Minimize the system overhead

22. Thanks