Co-spatial searcher: efficient tag-based collaborative spatial search on geo-social network

  • Authors:

  • Jinzeng Zhang;Xiaofeng Meng;Xuan Zhou;Dongqi Liu

  • Affiliations:

  • School of Information, Renmin University of China, Beijing, China;School of Information, Renmin University of China, Beijing, China;School of Information, Renmin University of China, Beijing, China and Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China;School of Information, Renmin University of China, Beijing, China

  • Venue:
  • DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
  • Year:
  • 2012

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Abstract

The proliferation of geo-social network, such as Foursquare and Facebook Places, enables users to generate location information and its corresponding descriptive tags. Using geo-social networks, users with similar interests can plan for social activities collaboratively. This paper proposes a novel type of query, called Tag-based top-k Collaborative Spatial (TkCoS) query, for users to make outdoor plans collaboratively. This type of queries aim to retrieve groups of geographic objects that can satisfy a group of users' requirements expressed in tags, while ensuring that the objects be within the minimum spatial distance from the users. To answer TkCoS queries efficiently, we introduce a hybrid index structure called Spatial-Tag R-tree (STR-tree), which is an extension of the R-tree. Based on STR-tree, we propose a query processing algorithm that utilizes both spatial and tag similarity constraints to prune search space and identify desired objects quickly. Moreover, a differential impact factor is adopted to fine-tune the returned results in order to maximize the users' overall satisfaction. Extensive experiments on synthetic and real datatsets validate the efficiency and the scalability of the proposed algorithm.