Deep web数据集成需要对web查询接口进行模式匹配并获得映射关系.在web查询接口集成中引入语义冲突的概念,通过分析语义冲突的起源和分类,提出了一种基于本体的模式匹配方法.以房产领域的web查询接口集成为实例,详细阐述了这种方法的具...Deep web数据集成需要对web查询接口进行模式匹配并获得映射关系.在web查询接口集成中引入语义冲突的概念,通过分析语义冲突的起源和分类,提出了一种基于本体的模式匹配方法.以房产领域的web查询接口集成为实例,详细阐述了这种方法的具体过程:通过比较语义相似度自动检测不同查询接口之间存在的语义冲突,识别冲突类别并且给冲突解决器发送消息,冲突解决器借助领域专家定义推理规则来消除冲突获得映射表.使用检测和解决语义冲突的方法来进行模式匹配,算法简单易于实现,扩充本体定义就可以使用于不同领域,灵活性和重用性较好.展开更多
This paper introduces a new algorithm of mining association rules. The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions. ...This paper introduces a new algorithm of mining association rules. The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions. The total number of passes over the database is only (k + 2m - 2)/m, where k is the longest size in the itemsets. It is much less than k.展开更多
With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestria...With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestrial wireless sensor networks(WSN),can provide sustainable solutions for data harvesting.The rising demands for efficient data collection in a larger open area have been posed in the literature,which requires efficient UAV trajectory planning with lower energy consumption methods.Currently,there are amounts of inextricable solutions of UAV planning for a larger open area,and one of the most practical techniques in previous studies is deep reinforcement learning(DRL).However,the overestimated problem in limited-experience DRL quickly throws the UAV path planning process into a locally optimized condition.Moreover,using the central nodes of the sub-WSNs as the sink nodes or navigation points for UAVs to visit may lead to extra collection costs.This paper develops a data-driven DRL-based game framework with two partners to fulfill the above demands.A cluster head processor(CHP)is employed to determine the sink nodes,and a navigation order processor(NOP)is established to plan the path.CHP and NOP receive information from each other and provide optimized solutions after the Nash equilibrium.The numerical results show that the proposed game framework could offer UAVs low-cost data collection trajectories,which can save at least 17.58%of energy consumption compared with the baseline methods.展开更多
基金The National Natural Science Foundation of China(No.60673130)the Natural Science Foundation of Shandong Province(No.Y2006G29,Y2007G24,Y2007G38)the Encouragement Fund for Young Scholars of Shandong Province(No.2005BS01002)
文摘This paper introduces a new algorithm of mining association rules. The algorithm RP counts the itemsets with different sizes in the same pass of scanning over the database by dividing the database into m partitions. The total number of passes over the database is only (k + 2m - 2)/m, where k is the longest size in the itemsets. It is much less than k.
基金the National Natural Science Foundation of China under Grant No.61972230the Natural Science Foundation of Shandong Province of China under Grant No.ZR2021LZH006.
文摘With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestrial wireless sensor networks(WSN),can provide sustainable solutions for data harvesting.The rising demands for efficient data collection in a larger open area have been posed in the literature,which requires efficient UAV trajectory planning with lower energy consumption methods.Currently,there are amounts of inextricable solutions of UAV planning for a larger open area,and one of the most practical techniques in previous studies is deep reinforcement learning(DRL).However,the overestimated problem in limited-experience DRL quickly throws the UAV path planning process into a locally optimized condition.Moreover,using the central nodes of the sub-WSNs as the sink nodes or navigation points for UAVs to visit may lead to extra collection costs.This paper develops a data-driven DRL-based game framework with two partners to fulfill the above demands.A cluster head processor(CHP)is employed to determine the sink nodes,and a navigation order processor(NOP)is established to plan the path.CHP and NOP receive information from each other and provide optimized solutions after the Nash equilibrium.The numerical results show that the proposed game framework could offer UAVs low-cost data collection trajectories,which can save at least 17.58%of energy consumption compared with the baseline methods.
