GeoFeed: A Location-Aware News Feed System

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

2. Background Social Networking Services (e.g., Facebook & Twitter) Become one of the most popular Web services!!!

3. What is News Feeds? • News Feed function • 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 • 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 Twitter Nearby Google Latitude Facebook Place • 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 “Real” Location-Aware News Feed Social Relevance Messages from friends/ subscribed news agents 2. Spatial Relevance Message relevant to the user’s location

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 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 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 pushapproaches, 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 • Spatial Pull approach • 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 • Spatial Push approach • 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 Materialized view 3. Range query Alice New message Other Friends Other Materialized views 2. Relevant messages Grid Index 4.Update

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. GeoFeedCost 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) • Response time • Return messages from the shared view with filtering • System overhead • 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 D A D A D B E B E B E Users Friends C F C F C F • OR Users Friends A D • Consider the wide diversity in user activities in social networking systems e.g., offline times and update frequencies B E C F 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 • 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. (b) Offline time = 8 hours (a) Offline time = 1 hour

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. SystemPrototype • 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 andshared push to evaluate location-aware news feed: • Guarantee the user’s required response time • Minimize the system overhead

22. Thanks