We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. B...We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. Based on the model, we design a parallel query processing method and a parallel validation method for multicore processing platforms. The time complexity of the algorithms is O((log|D|+p.k)/p.k)?and O(log p.k), respectively, which are all O(1/k) times the time complexity of the state-of-the-art method. The experiment result confirms the superiority of our algorithms over the state-of-the-art method.展开更多
语义网数据的大量增加使得RDF数据查询成为一个重要研究主题.关键词查询方式不需要掌握数据模式或查询语言,更适合普通用户使用.文中提出一种RDF数据关键词查询方法KREAG(Keyword query over RDF data based on Entity-triple Associati...语义网数据的大量增加使得RDF数据查询成为一个重要研究主题.关键词查询方式不需要掌握数据模式或查询语言,更适合普通用户使用.文中提出一种RDF数据关键词查询方法KREAG(Keyword query over RDF data based on Entity-triple Association Graph).为了支持用户对属性或关系名进行查询,将RDF数据建模为顶点带标签的实体三元组关联图.该模型保证了RDF数据中实体间关联转化为关联图中顶点间的通路,且文本信息全部封装到关联图顶点标签上.在此基础上,将关键词查询问题转化为关联图上查找有向斯坦纳树问题.在保证近似比为m的前提下(m为查询关键词的个数),利用近似算法实现快速查询响应.通过合理的评分方式衡量查询结果的相关性,支持top-k查询.算法的时间复杂度为O(m.|V|),其中|V|为实体三元组关联图中顶点个数.实验表明KREAG较其它方法具有更快的响应时间,同时能够有效地实现RDF数据的关键词查询.展开更多
文摘We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. Based on the model, we design a parallel query processing method and a parallel validation method for multicore processing platforms. The time complexity of the algorithms is O((log|D|+p.k)/p.k)?and O(log p.k), respectively, which are all O(1/k) times the time complexity of the state-of-the-art method. The experiment result confirms the superiority of our algorithms over the state-of-the-art method.
文摘语义网数据的大量增加使得RDF数据查询成为一个重要研究主题.关键词查询方式不需要掌握数据模式或查询语言,更适合普通用户使用.文中提出一种RDF数据关键词查询方法KREAG(Keyword query over RDF data based on Entity-triple Association Graph).为了支持用户对属性或关系名进行查询,将RDF数据建模为顶点带标签的实体三元组关联图.该模型保证了RDF数据中实体间关联转化为关联图中顶点间的通路,且文本信息全部封装到关联图顶点标签上.在此基础上,将关键词查询问题转化为关联图上查找有向斯坦纳树问题.在保证近似比为m的前提下(m为查询关键词的个数),利用近似算法实现快速查询响应.通过合理的评分方式衡量查询结果的相关性,支持top-k查询.算法的时间复杂度为O(m.|V|),其中|V|为实体三元组关联图中顶点个数.实验表明KREAG较其它方法具有更快的响应时间,同时能够有效地实现RDF数据的关键词查询.
