Abstract
Recently, Collective Spatial Keyword Querying (CoSKQ), which returns a group of objects that cover a set of given keywords collectively and have the smallest cost, has received extensive attention in spatial database community. However, no research so far focuses on a situation when the result of CoSKQ is taken as the input of a query. But this kind of query has many applications in location based services. In this paper, we introduce a new problem Reverse Collective Spatial Keyword Querying (RCoSKQ) that returns a region, in which the query objects are qualified objects with the highest spatial and textual similarity. We propose an efficient method which uses IR-tree to retrieve objects with text descriptions. To accelerate the query process, a pruning method that effectively reduces computing is proposed. The experiments over real and synthesis data sets demonstrate the efficiency of our approaches.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61572165), the Natural Science Foundation of Zhejiang Province (No. LZ15F 020003).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wu, Y., Xu, J., Tu, L., Luo, M., Chen, Z., Zheng, N. (2019). Reverse Collective Spatial Keyword Querying (Short Paper). In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_15
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DOI: https://doi.org/10.1007/978-3-030-12981-1_15
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