Abstract
We propose the flexible group spatial keyword query and algorithms to process three variants of the query in the spatial textual domain: (i) the group nearest neighbor with keywords query, which finds the data object that optimizes the aggregate cost function for the whole group Q of size n query objects, (ii) the subgroup nearest neighbor with keywords query, which finds the optimal subgroup of query objects and the data object that optimizes the aggregate cost function for a given subgroup size m (\(m \le n\)), and (iii) the multiple subgroup nearest neighbor with keywords query, which finds optimal subgroups and corresponding data objects for each of the subgroup sizes in the range [m, n]. We design query processing algorithms based on branch-and-bound and best-first paradigms. Finally, we conduct extensive experiments with two real datasets to show the efficiency of the proposed algorithms.
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Acknowledgment
This research is partially supported by the ICT Division, Government of the People’s Republic of Bangladesh. Jianzhong Qi is supported by The University of Melbourne Early Career Researcher Grant (project number 603049).
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Ahmad, S., Kamal, R., Ali, M.E., Qi, J., Scheuermann, P., Tanin, E. (2017). The Flexible Group Spatial Keyword Query. In: Huang, Z., Xiao, X., Cao, X. (eds) Databases Theory and Applications. ADC 2017. Lecture Notes in Computer Science(), vol 10538. Springer, Cham. https://doi.org/10.1007/978-3-319-68155-9_1
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DOI: https://doi.org/10.1007/978-3-319-68155-9_1
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