Co-spatial Searcher: Efficient Tag-Based Collaborative Spatial Search on Geo-social Network

Abstract

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.

Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

“…In the query processing of spatial-keyword search, indexing techniques for both text and geographic data are used [44].…”

“…More importantly, users with similar interests can collaborate to plan social activities like going out to eat and shop or going on a bike ride. Coordinating such plans requires pinpointing a collection of spatial objects of amenities that may best meet the needs of the users [44].…”

“…Another factor that influences a spatial search is the scalability of the dataset with regards to size. J. Zhong, X. Meng, X. Zhou, and D. Liu [44] carried out a series of tests to assess the scalability of three algorithms by altering the number of spatial objects. Their main goal was to simulate real geosocial networking in which the number of spatial objects and tags continuously increases.…”

Geo-Social Top-k and Skyline Keyword Queries on Road Networks

“…Geo-Social query processing is an emerging field that has recently garnered considerable attention from the research community [29][30][31][32][33]. Huang et al [34] proposed geo-social network services that organize users in social networks based on geographic features, retrieving the set of nearby users that share common interests.…”

Collective spatial keyword queries

“…It is defined to be the diameter of S ∪ {q}. In fact, diameter-related cost functions have been commonly adopted in graph databases [1,13,2,15] and spatial databases [25,26,27]. To the best of our knowledge, we are the first to study this cost function for CoSKQ.…”