摘要
数据在社交网络中通常呈现为流式的特征。针对流式RDF数据,提出一种增量的模式匹配方法。设计一种面向RDF数据的索引结构,被定义为顶点聚簇的数据子图。提出一种基于顶点-边标签映射的有效验证的匹配算法,减少遍历过程中候选数据规模。实验结果表明,该方法在环状和星状查询图的模式匹配算法效率更具时间优势。
Data is emerged as a streamlined feature in social networks.Regarding the streamlined feature of RDF data,an incremental method of pattern matching for streaming RDF graph was proposed.A specified data model for RDF data was given and it was defined as a vertex-clustered data subgraph(SGD).A matching algorithm based on valid verification of vertex-edges label mapping(ORCTM-PR)was proposed.The quantity of candidate data in traversal processing was effectively reduced.Experimental results show that the method provides better benefits than relational methods for cycle and star queries.
作者
王翔
WANG Xiang(Office of Information Technology,University of Science and Technology Beijing,Beijing 100083,China)
出处
《计算机工程与设计》
北大核心
2024年第5期1458-1464,共7页
Computer Engineering and Design
关键词
数据流
模式匹配
数据子图
数据索引
顶点聚簇
候选验证
增量匹配算法
data stream
pattern matching
data graph
data index
vertex clustering
candidate verification
incremental matching algorithm
作者简介
王翔(1979),男,河南周口人,博士,高级工程师,CCF专业会员,研究方向为人工智能、大数据和教育信息化。E-mail:wangxiang@ustb.edu.cn。
