With the development of information technology, the amount of power grid topology data has gradually increased. Therefore, accurate querying of this data has become particularly important. Several researchers have cho...With the development of information technology, the amount of power grid topology data has gradually increased. Therefore, accurate querying of this data has become particularly important. Several researchers have chosen different indexing methods in the filtering stage to obtain more optimized query results because currently there is no uniform and efficient indexing mechanism that achieves good query results. In the traditional algorithm, the hash table for index storage is prone to "collision" problems, which decrease the index construction efficiency. Aiming at the problem of quick index entry, based on the construction of frequent subgraph indexes, a method of serialized storage optimization based on multiple hash tables is proposed. This method mainly uses the exploration sequence to make the keywords evenly distributed; it avoids conflicts of the stored procedure and performs a quick search of the index. The proposed algorithm mainly adopts the "filterverify" mechanism; in the filtering stage, the index is first established offline, and then the frequent subgraphs are found using the "contains logic" rule to obtain the candidate set. Experimental results show that this method can reduce the time and scale of candidate set generation and improve query efficiency.展开更多
图半监督学习(Graph based semi-supervised learning,GSL)方法需要花费大量时间构造一个近邻图,速度比较慢.本文提出了一种哈希图半监督学习(Hash graph based semi-supervised learning,HGSL)方法,该方法通过局部敏感的哈希函数进行...图半监督学习(Graph based semi-supervised learning,GSL)方法需要花费大量时间构造一个近邻图,速度比较慢.本文提出了一种哈希图半监督学习(Hash graph based semi-supervised learning,HGSL)方法,该方法通过局部敏感的哈希函数进行近邻搜索,可以有效降低图半监督学习方法所需的构图时间.图像分割实验表明,该方法一方面可以达到更好的分割效果,使分割准确率提高0.47%左右;另一方面可以大幅度减小分割时间,以一幅大小为300像素×800像素的图像为例,分割时间可减少为图半监督学习所需时间的28.5%左右.展开更多
基金supported by the State Grid Science and Technology Project (Title: Research on High Performance Analysis Technology of Power Grid GIS Topology Based on Graph Database, 5455HJ160005)
文摘With the development of information technology, the amount of power grid topology data has gradually increased. Therefore, accurate querying of this data has become particularly important. Several researchers have chosen different indexing methods in the filtering stage to obtain more optimized query results because currently there is no uniform and efficient indexing mechanism that achieves good query results. In the traditional algorithm, the hash table for index storage is prone to "collision" problems, which decrease the index construction efficiency. Aiming at the problem of quick index entry, based on the construction of frequent subgraph indexes, a method of serialized storage optimization based on multiple hash tables is proposed. This method mainly uses the exploration sequence to make the keywords evenly distributed; it avoids conflicts of the stored procedure and performs a quick search of the index. The proposed algorithm mainly adopts the "filterverify" mechanism; in the filtering stage, the index is first established offline, and then the frequent subgraphs are found using the "contains logic" rule to obtain the candidate set. Experimental results show that this method can reduce the time and scale of candidate set generation and improve query efficiency.
文摘图半监督学习(Graph based semi-supervised learning,GSL)方法需要花费大量时间构造一个近邻图,速度比较慢.本文提出了一种哈希图半监督学习(Hash graph based semi-supervised learning,HGSL)方法,该方法通过局部敏感的哈希函数进行近邻搜索,可以有效降低图半监督学习方法所需的构图时间.图像分割实验表明,该方法一方面可以达到更好的分割效果,使分割准确率提高0.47%左右;另一方面可以大幅度减小分割时间,以一幅大小为300像素×800像素的图像为例,分割时间可减少为图半监督学习所需时间的28.5%左右.
