首页 | 本学科首页   官方微博 | 高级检索  
     


Finding smallest k-Compact tree set for keyword queries on graphs using mapreduce
Authors:Chengfei Liu  Liang Yao  Jianxin Li  Rui Zhou  Zhenying He
Affiliation:1.Department of Computer Science and Software Engineering,Swinburne University of Technology,Melbourne,Australia;2.Centre for Applied Informatics, College of Engineering and Science,Victoria University,Melbourne,Australia;3.Shanghai Key Laboratory of Data Science, School of Computer Science,Fudan University,Shanghai,China
Abstract:Keyword search is integrated in many applications on account of the convenience to convey users’ query intention. Most existing works in keyword search on graphs modeled the query results as individual minimal connected trees or connected graphs that contain the keywords. We observe that significant overlap may exist among those query results, which would affect the result diversification. Besides, most solutions required accessing graph data and pre-built indexes in memory, which is not suitable to process big dataset. In this paper, we define the smallest k-compact tree set as the keyword query result, where no shared graph node exists between any two compact trees. We then develop a progressive A* based scalable solution using MapReduce to compute the smallest k-compact tree set, where the computation process could be stopped once the generated compact tree set is sufficient to compute the keyword query result. We conduct experiments to show the efficiency of our proposed algorithm.
Keywords:
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号