Abstract
With the popularity of mobile devices and the development of geo-positioning technology, location-based services (LBS) attract much attention and top-k spatial keyword queries become increasingly complex.It is common to see that clients issue a query to find a restaurant serving pizza and steak, low in price and noise level particularly.However, most of prior works focused only on the spatial keyword while ignoring these independent numerical attributes.
In this paper we demonstrate, for the first time, the Attributes-Aware Spatial Keyword Query (ASKQ), and devise a two-layer hybrid index structure called Quad-cluster Dual-filtering R-Tree (QDR-Tree). In the keyword cluster layer, a Quad-Cluster Tree (QC-Tree) is built based on the hierarchical clustering algorithm using kernel k-means to classify keywords.In the spatial layer, for each leaf node of the QC-Tree, we attach a Dual-Filtering R-Tree (DR-Tree) with two filtering algorithms, namely, keyword bitmap-based and attributes skyline-based filtering. Accordingly, efficient query processing algorithms are proposed.
Through theoretical analysis, we have verified the optimization both in processing time and space consumption. Finally, massive experiments with real-data demonstrate the efficiency and effectiveness of QDR-Tree.
This work was partly supported by the Program of International S&T Cooperation (2016YFE0100300), the China 973 project (2014CB340303), the National Natural Science Foundation of China (Grant number 61472252, 61672353, 61729202 and U1636210), the Shanghai Science and Technology Fund (Grant number 17510740200), CCF-Tencent Open Research Fund (RAGR20170114), and Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University (SZU-GDPHPCL2017).
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Notes
- 1.
Hereafter, Top-k is denoted as Top-\(\kappa \) to avoid confusion with the k-means algorithm.
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Zang, X., Hao, P., Gao, X., Yao, B., Chen, G. (2018). QDR-Tree: An Efficient Index Scheme for Complex Spatial Keyword Query. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham. https://doi.org/10.1007/978-3-319-98809-2_24
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