The Community for Technology Leaders
Green Image
Issue No. 11 - Nov. (2015 vol. 27)
ISSN: 1041-4347
pp: 3056-3069
Xin Lin , , East China Normal University, Shanghai, China
Jianliang Xu , , Hong Kong Baptist University, Kowloon Tong, Hong Kong
Haibo Hu , , Hong Kong Baptist University, Kowloon Tong, Hong Kong
Spatio-textual queries retrieve the most similar objects with respect to a given location and a keyword set. Existing studies mainly focus on how to efficiently find the top-$_$k$_$ result set given a spatio-textual query. Nevertheless, in many application scenarios, users cannot precisely formulate their keywords and instead prefer to choose them from some candidate keyword sets. Moreover, in information browsing applications, it is useful to highlight the objects with the tags (keywords) under which the objects have high rankings. Driven by these applications, we propose a novel query paradigm, namely reverse keyword search for s patio-textual top-$_$k$_$ queries ( $_$\mathcal {RSTQ}$_$ ). It returns the keywords under which a target object will be a spatio-textual top-$_$k$_$ result. To efficiently process the new query, we devise a novel hybrid index KcR-tree to store and summarize the spatial and textual information of objects. By accessing the high-level nodes of KcR-tree, we can estimate the rankings of the target object without accessing the actual objects. To further improve the performance, we propose three query optimization techniques, i.e., KcR*-tree, lazy upper-bound updating, and keyword set filtering. We also extend $_$\mathcal {RSTQ}$_$ to allow the input location to be a spatial region instead of a point. Extensive experimental evaluation demonstrates the efficiency of our proposed query techniques in terms of both the computational cost and I/O cost.
Indexes, Query processing, Keyword search, Upper bound, Estimation, Tagging, Mobile radio mobility management

X. Lin, J. Xu and H. Hu, "Reverse Keyword Search for Spatio-Textual Top-$k$ Queries in Location-Based Services," in IEEE Transactions on Knowledge & Data Engineering, vol. 27, no. 11, pp. 3056-3069, 2015.
689 ms
(Ver 3.3 (11022016))