Monochromatic and bichromatic ranked reverse boolean spatial keyword nearest neighbors search

Recently, Reverse k Nearest Neighbors (R k NN) queries, returning every answer for which the query is one of its k nearest neighbors, have been extensively studied on the database research community. But the R k NN query cannot retrieve spatio-textual objects which are described by their spatial loc...

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Bibliographic Details
Published in:World wide web (Bussum) 2017, Vol.20 (1), p.39-59
Main Authors: Zhao, Pengpeng, Fang, Hailin, Sheng, Victor S., Li, Zhixu, Xu, Jiajie, Wu, Jian, Cui, Zhiming
Format: Article
Language:English
Subjects:
Boolean algebra
Communities
Computer Science
Database Management
Information Systems Applications (incl.Internet)
Object recognition
Operating Systems
Performance indices
Queries
Similarity
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Summary:Recently, Reverse k Nearest Neighbors (R k NN) queries, returning every answer for which the query is one of its k nearest neighbors, have been extensively studied on the database research community. But the R k NN query cannot retrieve spatio-textual objects which are described by their spatial location and a set of keywords. Therefore, researchers proposed a RST k NN query to find these objects, taking both spatial and textual similarity into consideration. However, the RST k NN query cannot control the size of answer set and to be sorted according to the degree of influence on the query. In this paper, we propose a new problem Ranked Reverse Boolean Spatial Keyword Nearest Neighbors query called Ranked-RBSKNN query, which considers both spatial similarity and textual relevance, and returns t answers with most degree of influence. We propose a separate index and a hybrid index to process such queries efficiently. Experimental results on different real-world and synthetic datasets show that our approaches achieve better performance.
ISSN:1386-145X
1573-1413
DOI:10.1007/s11280-016-0399-8