Research Article
Reverse Collective Spatial Keyword Querying (Short Paper)
@INPROCEEDINGS{10.1007/978-3-030-12981-1_15, author={Yang Wu and Jian Xu and Liming Tu and Ming Luo and Zhi Chen and Ning Zheng}, title={Reverse Collective Spatial Keyword Querying (Short Paper)}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Collective Spatial Keyword Querying A set of query objects Reverse}, doi={10.1007/978-3-030-12981-1_15} }- Yang Wu
Jian Xu
Liming Tu
Ming Luo
Zhi Chen
Ning Zheng
Year: 2019
Reverse Collective Spatial Keyword Querying (Short Paper)
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_15
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.