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Database Laboratory Regular Seminar 2013-09-22 TaeHoon Kim

Towards Location-based Social Networking Services Chi-Yin Chow, Jie Bao , Mohamed F.Mokbel Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA LBSN 2010, ACM.

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Database Laboratory Regular Seminar 2013-09-22 TaeHoon Kim

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  1. Towards Location-based Social Networking Services Chi-Yin Chow, JieBao, Mohamed F.Mokbel Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA LBSN 2010, ACM Database Laboratory Regular Seminar 2013-09-22 TaeHoon Kim

  2. Contents • Introduction • System Architecture • Location-Based News Feed • Location-Based News Ranking • Location-Based Recommendation • Related Work • Conclusion

  3. Introduction • Social networking application have become one of the most important web services • Facebook[12], Twitter[39] • With the advance in location-aware mobile devices(GPS-enabled portable devices), wireless communication technologies, mapservices, and spatial DBMSs, location-based social networking applications have been taking shape at fast pace • Google Buzz Mobile • Loopt • Microsoft Geo-Life

  4. Introduction • The limitations of location-based services • Rely on nearest-neighbor queries, range queries, skyline queries • Completely ignoring the social aspect in networking services • Location-based social networking systems • Provide services with social relevance for users • Provide with spatial relevance for the users • A user wants to find new restaurants within a certain area based on his or her friends opinions

  5. Introduce • In this paper, we present GeoSocialDB; holistic location-based social networking System • Which is currently under joint development by the City University of Hong Kong and the University of Minnesota • GeoSocialDB provides the following three new location-based social networking services • Location-based news feed: “Q1:send me the messages submitted by my friends with tagged locations within d miles of my location” • Location-based news ranking : “Q2:send me the k most relevant messages submitted by my friends with tagged locations within d miles of my location” • Location-based recommendation : “Q3:recommend me the best k restaurants within d miles of my location based on my friends’ opinions”

  6. System Architecture • User updates • Geo-tagged Messages • Geo-tagged with multimedia data( e.g) jpeg with geo-tagged • Profile Updates • Users’ Personal information( e.g) a list of the user’s friend ) • Object Ratings • e.g)User’s opinions for objects or places( e.g) restaurant and hotel score • Log-on query • When a user logs in GeoSocialDB through it web-based user interface or refreshes the user interface, the system generates a log-on query with the user’s location and user specified range distance d to the location-based news feed module

  7. System Architecture • Log-on query • Is sent by the user’s friend, i.e., the social relevance • Is tagged with a location within the range distance d of the user i.e., the spatial relevance • Finally, the k most relevant messages are returned to the user and displayed on the user interface with their tagged locations indicated by markers on the underlying map • Recommendation query • When a user requests recommendations for a specific object type(e.g., restaurants or hotels, within a range distance from the user’s location through the GeoSocialDB’s web-based user interface), GeoSocialDB generates a recommendation query to the location-based recommendation module • Predicts a score

  8. System Architecture

  9. Location-Based News Feed • Challenge I : Location-aware Query Operators • Straightforward execution of query Q1 is extremely inefficient • Join(Social networking system stores a huge number of messages)

  10. Location-Based News Feed • Challenge II : Answer Materialization • Materialization techniques are known to be used to minimize query response time • The major challenge is to select an appropriate set of users to maintain their materialized answer, in order to minimize the computational overhead of GeoSocialDB • The solution to this challenge is to determine QueryRate and QueryCost for each user, and predict the Update Rate and Update Cost of each user’s materialized answer

  11. Location-Based News Feed • Challenge III : Continuous Query Processing • Incremental query processing(Anew (Anew ∩ Aold) • Safe region • The safe region is a circular area centered the user’s location with a radius of the distance between the user and the selected message m1,m2,m3,m4,m5 Aold m6, m7, m8 Anew S C m4,m5,m6,m7,m8

  12. Location-Based News Feed • Challenge IV : Privacy-Aware Query Processing • Spatial Cloaking • The idea of this technique is to blur a user location into a cloaked area that satisfies the user specified privacy requirements • Require a fully trusted third party placed between the user and datase server • The third party may become a single point attack or a system bottleneck • Data Transformation • Transforms the data and query locations into a encrypted space and processes location based queries in the transformed space to support nearest neighbor queries and range queries

  13. Location-Based News Ranking • Challenge I : Location- and Rank-aware Query Operators • The basic idea of this optimization is to sort the selected messages in a particular order based on the most important user preference(ie., the preference has large weight) • Challenge II : Answer Materialization • The basic idea of its solution to the answer materialization challenge is the same as I the location-based news feed service

  14. Location-Based News Ranking • Challenge III : Continuous Query Processing • To find a result answer for the newly reported location, GeoSocialDB ranks the messages in the previous and new answers based on their ranking scores, and selects the k messages with the highest ranking scores • Modified safe region optimization has to select message that is outside the searched area of a query answer, is the nearest one to user, and has a higher ranking score than the messages included in the query answer • Challenge IV : Privacy-Aware Query Processing • The basic idea of its solution to the answer materialization challenge is the same as I the location-based news feed service

  15. Location-Based Recommendation • Challenge I : Location-aware Query Operators • Use he function RecScore to compute a recommendation score for each selected restaurant • Finally, the top-k restaurants with the highest recommendation scores are recommended to the querying user

  16. Location-Based Recommendation • Challenge II : Answer Materialization • Whenever the user views his or her profile, the personalized recommendation is immediately available to be displayed on the user profile page • Challenge III : Continuous Query Processing • GeoSocialDB ranks the restaurants in the previous and new answers based on their recommendation scores, and selects the k restaurants with the highest recommendation scores • Challenge IV : Privacy-Aware Query Processing • The data provider can use the privacy-preserving data transformation scheme to provide the transformed location • Give the required information for query processing and data decryption

  17. Related Work • Social networking services • Sharing geo-tagged messages[7] • Enables the users to get the geo-tagged messages within their proximity, where the proximity is determined by the system based on the capacity of their mobile devices • Supporting privacy-preserving buddy search[24, 36] • Allow users to find their friends within a certain area without revealing their locations to the system

  18. Related Work • Recommender systems : The most popular method used in recommender systems. • 1) User-based collaborative filtering(e.g.,[18, 25, 34]) • The idea is to predict a recommendation score for each item that has not been rated by a querying user, based on the opinions given by other similar users. • Then the top-k items with the highest recommendation scores are returned to the user • 2) Model-based collaborative filtering(e.g.,[11,22,18,25,28,34,35]) • The idea is to use an offline-built model to predict recommendation scores for those items not rated by the querying user. e.g)hotel

  19. Conclusion • In this paper • We introduced the system architecture of GeoSocialDB, holistic location-based social networking database system • Currently under joint development at City University of Hong Kong and University of Minnesota • Delivers location-based news feed, location-based news ranking and location-based recommendation services

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