Efficient Group Top-k Spatial Keyword Query Processing

摘要：

　　With the proliferation of geo-positioning and geo-tagging,spatial web objects that possess both a geographical location and textual description are gaining in prevalence.Given a spatial location and a set of keywords,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.To our knowledge,existing study on spatial keyword query processing only focuses on single query point scenario.In this paper,we take the first step to study the problem of multiple query points (or group queries) top-k spatial keyword query processing.We first propose a threshold-based algorithm,which first performs incremental top-k spatial keyword query for each query point and then combines their results.Next,we propose another more efficient algorithm by treating the whole query set as a query unit,which can significantly reduce the objects to be examined,and thus achieve higher performance.Extensive experiments using real datasets demonstrate that our approaches are efficient in terms of runtime and I/O cost,as compared to the baseline algorithm.

关键词： Spatial keyword query Top-k query Spatial databases

作者: Kai Yao Jianjun Li Guohui Li Changyin Luo

作者单位: School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan,China School of Computer,Central China Normal University,Wuhan,China

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 153-165

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）