TY - JOUR
T1 - Monochromatic and bichromatic ranked reverse boolean spatial keyword nearest neighbors search
AU - Zhao, Pengpeng
AU - Fang, Hailin
AU - Sheng, Victor S.
AU - Li, Zhixu
AU - Xu, Jiajie
AU - Wu, Jian
AU - Cui, Zhiming
N1 - Publisher Copyright:
© 2016, Springer Science+Business Media New York.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Recently, Reverse k Nearest Neighbors (RkNN) 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 RkNN query cannot retrieve spatio-textual objects which are described by their spatial location and a set of keywords. Therefore, researchers proposed a RSTkNN query to find these objects, taking both spatial and textual similarity into consideration. However, the RSTkNN 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.
AB - Recently, Reverse k Nearest Neighbors (RkNN) 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 RkNN query cannot retrieve spatio-textual objects which are described by their spatial location and a set of keywords. Therefore, researchers proposed a RSTkNN query to find these objects, taking both spatial and textual similarity into consideration. However, the RSTkNN 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.
KW - Ranking
KW - Reverse k nearest neighbor
KW - Spatial keyword search
UR - http://www.scopus.com/inward/record.url?scp=84975258335&partnerID=8YFLogxK
U2 - 10.1007/s11280-016-0399-8
DO - 10.1007/s11280-016-0399-8
M3 - Article
AN - SCOPUS:84975258335
SN - 1386-145X
VL - 20
SP - 39
EP - 59
JO - World Wide Web
JF - World Wide Web
IS - 1
ER -