Abstract
With the proliferation of geo-positioning and geo-tagging, spatial keyword query (SKQ) processing is gaining great concern recently. Typically, a top-k spatial keyword query returns the k best spatio-textual objects ranked according to their proximity to the query location and relevance to the query keywords. While there has been much work devoted to top-k spatial keyword processing, most of them are either focused on single query or only suitable for Euclidean space. In this paper, we take the first step to study the problem of multiple query points (or group) top-k spatial keyword query processing in road networks. We first propose a basic group query-processing algorithm by using three distinct index structures. Next, we propose another more efficient algorithm based on the concept of Minimum Bounding Rectangle (MBR), which can significantly reduce the objects to be examined and thus achieve higher performance. Finally, extensive experiments using real data sets are conducted to validate the effectiveness of the proposed algorithms.
The work was partially supported by the State Key Program of National Natural Science of China under Grant No. 61332001, the National Natural Science Foundation of China under Grant Nos. 61300045, 61572215, 61672252, 61602197, and the Fundamental Research Funds for the Central Universities, HUST-2016YXMS076, HUST-2016YXMS085.
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Ekomie, H.B., Yao, K., Li, J., Li, G., Li, Y. (2017). Group Top-k Spatial Keyword Query Processing in Road Networks. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_30
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