$^2$-tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR$^2$-tree in query response time significantly, often by a factor of orders of magnitude." /> $^2$-tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR$^2$-tree in query response time significantly, often by a factor of orders of magnitude." /> $^2$-tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR$^2$-tree in query response time significantly, often by a factor of orders of magnitude." /> Fast Nearest Neighbor Search with Keywords
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Issue No.04 - April (2014 vol.26)
pp: 878-888
Yufei Tao , Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong, China
Cheng Sheng , Google Switzerland, Switzerland
ABSTRACT
Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects' geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbor query would instead ask for the restaurant that is the closest among those whose menus contain “steak, spaghetti, brandy” all at the same time. Currently, the best solution to such queries is based on the IR 2-tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR 2-tree in query response time significantly, often by a factor of orders of magnitude.
INDEX TERMS
spatial index, Nearest neighbor search, keyword search,
CITATION
Yufei Tao, Cheng Sheng, "Fast Nearest Neighbor Search with Keywords", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 4, pp. 878-888, April 2014, doi:10.1109/TKDE.2013.66
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