基于图OLAP的学术网络分析  

Analysis of Academic Network Based on Graph OLAP

作　　者：杨恒[1] 朱焱[1] YANG Heng;ZHU Yan(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chendu 611756,China)

机构地区：[1]西南交通大学计算机与人工智能学院,成都611756

出　　处：《计算机科学》2023年第S01期587-591,共5页Computer Science

基　　金：四川省科技计划项目(2019YFSY0032)。

摘　　要：近年来学术领域逐渐积累了海量的数据,网络结构作为一种表示和分析大数据的有效方法,具有较丰富的维度且能够对现实生活中大量数据进行建模。Graph OLAP(图联机处理)技术继承了传统OLAP技术的相关思想,允许用户从不同角度与粒度对多维网络数据进行分析。然而现有的图OLAP技术大多围绕数据立方体的构建展开,相关操作大多都是传统OLAP技术在图数据上的简单扩展,并且构建的模型对网络自身的拓扑结构的挖掘能力较弱。为此,首先设计了学术网络星座模式和相关的图OLAP分析算法,更加明显地突出了学术网络的拓扑结构信息,提高了图OLAP的分析能力,其次提出了对应的物化策略,有效地提升了图OLAP分析的效率。In recent years,academia has gradually accumulated a large amount of data.As an effective method for representing and analyzing big data,network structure has rich dimensions and can model a large amount of data in real life.Graph online analytic processing(Graph OLAP)technology inherits the related ideas of traditional OLAP technology,allowing users to analyze multi-dimensional network data from different angles and granularities.However,most of the existing graph OLAP technologies revolve around the construction of data cubes,and most of the related operations are simple extensions of traditional OLAP technologies on graph data,and the built models have weak ability to mine the topology of the network itself.To this end,the aca-demic network constellation model and related graph OLAP analysis algorithms are firstly designed,which more clearly highlights the topological structure information of academic networks and improves the analysis ability of graph OLAP.Secondly,the corresponding materialization strategy is proposed,which effectively improves the efficiency of graph OLAP analysis.

关 键 词：图联机分析处理 学术网络星座模式 物化策略 数据分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]