Due to the wide-spread use of geo-positioning technologies and geo-social networks,the reverse top-k geo-social keyword query has attracted considerable attention from both industry and research communities.A reverse ...Due to the wide-spread use of geo-positioning technologies and geo-social networks,the reverse top-k geo-social keyword query has attracted considerable attention from both industry and research communities.A reverse top-k geo-social keyword(RkGSK)query finds the users who are spatially near,textually similar,and socially relevant to a specified point of interest.RkGSK queries are useful in many real-life applications.For example,they can help the query issuer identify potential customers in marketing decisions.However,the query constraints could be too strict sometimes,making it hard to find any result for the RkGSK query.The query issuers may wonder how to modify their original queries to get a certain number of query results.In this paper,we study non-answer questions on reverse top-k geo-social keyword queries(NARGSK).Given an RkGSK query and the required number M of query results,NARGSK aim to find the refined RkGSK query having M users in its result set.To efficiently answer NARGSK,we propose two algorithms(ERQ and NRG)based on query relaxation.As this is the first work to address NARGSK to the best of our knowledge,ERQ is the baseline extended from the state-of-the-art method,while NRG further improves the efficiency of ERQ.Extensive experiments using real-life datasets demonstrate the efficiency of our proposed algorithms,and the performance of NRG is improved by a factor of 1–2 on average compared with ERQ.展开更多
随着移动互联网与社会网络的深度融合,基于位置服务(Location Based Service,LBS)的社交媒体应用更加流行,成为地理社会网络(Geo-Social Networks,GSN)的研究重点。基于位置信息的社会网络(Location Based Social Network,LBSN)由于具...随着移动互联网与社会网络的深度融合,基于位置服务(Location Based Service,LBS)的社交媒体应用更加流行,成为地理社会网络(Geo-Social Networks,GSN)的研究重点。基于位置信息的社会网络(Location Based Social Network,LBSN)由于具有时空特性,其海量数据可视化不同于传统信息可视化,必须结合其地理信息特征进行表达。该文以GSN中抽取出的海量时空数据为分析对象,从LBSN时空数据抽取、海量时空数据可视化等方面进行综述,对地理社会网络时空数据交互可视化分析技术开展研究,以期能够实现比较方便、快速、直接地从地理社会网络的海量数据中提取出有用、可靠、可知识化的综合信息,并通过信息可视化方式进行直观表达、展示与分析。展开更多
基金the National Natural Science Foundation of China under Grant Nos.61972338,62025206 and 62102351。
文摘Due to the wide-spread use of geo-positioning technologies and geo-social networks,the reverse top-k geo-social keyword query has attracted considerable attention from both industry and research communities.A reverse top-k geo-social keyword(RkGSK)query finds the users who are spatially near,textually similar,and socially relevant to a specified point of interest.RkGSK queries are useful in many real-life applications.For example,they can help the query issuer identify potential customers in marketing decisions.However,the query constraints could be too strict sometimes,making it hard to find any result for the RkGSK query.The query issuers may wonder how to modify their original queries to get a certain number of query results.In this paper,we study non-answer questions on reverse top-k geo-social keyword queries(NARGSK).Given an RkGSK query and the required number M of query results,NARGSK aim to find the refined RkGSK query having M users in its result set.To efficiently answer NARGSK,we propose two algorithms(ERQ and NRG)based on query relaxation.As this is the first work to address NARGSK to the best of our knowledge,ERQ is the baseline extended from the state-of-the-art method,while NRG further improves the efficiency of ERQ.Extensive experiments using real-life datasets demonstrate the efficiency of our proposed algorithms,and the performance of NRG is improved by a factor of 1–2 on average compared with ERQ.
文摘随着移动互联网与社会网络的深度融合,基于位置服务(Location Based Service,LBS)的社交媒体应用更加流行,成为地理社会网络(Geo-Social Networks,GSN)的研究重点。基于位置信息的社会网络(Location Based Social Network,LBSN)由于具有时空特性,其海量数据可视化不同于传统信息可视化,必须结合其地理信息特征进行表达。该文以GSN中抽取出的海量时空数据为分析对象,从LBSN时空数据抽取、海量时空数据可视化等方面进行综述,对地理社会网络时空数据交互可视化分析技术开展研究,以期能够实现比较方便、快速、直接地从地理社会网络的海量数据中提取出有用、可靠、可知识化的综合信息,并通过信息可视化方式进行直观表达、展示与分析。
