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数据分析与知识发现  2020, Vol. 4 Issue (7): 50-65     https://doi.org/10.11925/infotech.2096-3467.2020.0452
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
以图为中心的新型大数据技术栈研究 *
沈志宏1(),赵子豪1,2,王海波1
1中国科学院计算机网络信息中心 北京 100190
2中国科学院大学 北京 100049
Big Data Technology Stack Shifting: From SQL Centric to Graph Centric
Shen Zhihong1(),Zhao Zihao1,2,Wang Haibo1
1Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 

目的】传统的以SQL为中心的技术栈无法有效地应对大数据场景带来的多元异构数据管理、大规模关系网络管理和复杂网络分析等挑战,本文针对新型大数据技术栈展开研究。【方法】通过分析图数据模型的优势,结合图技术的发展和应用现状,提出以图为中心的新型大数据技术栈,并介绍了智能融合数据管理系统PandaDB。【结果】该技术栈在生物数据网络、科技知识图谱等实际应用中得到较好的验证,PandaDB具备良好的结构化、非结构化数据融合管理能力。【局限】 该技术栈的大面积推广还存在支撑工具不足、应用生态不够成熟等困难。【结论】以图为中心的新型大数据技术栈会在更多的大数据应用场景中发挥更大的价值。

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沈志宏
赵子豪
王海波
关键词 图模型图数据库数据仓库技术栈    
Abstract

[Objective] The traditional SQL centric technology stack cannot handle multivariant and heterogeneous data management, large-scale network management, as well as complex network analysis. Therefore, we proposed a new graphic centric technology stack for big data.[Methods] First, we analyzed the advantages of graph-based data model and established a new graph centric technology stack. Then, we developed PandaDB, an intelligent fusion data management system.[Results] The new technology stack performed well in the applications of biological data network and scholar knowledge graph. PandaDB could manage structured and unstructured data fusion.[Limitations] It is difficult to further promote this technology stack due to the lack of supporting tools and complete application ecology.[Conclusions] Our new technology stack will play a greater role in big data applications.

Key wordsGraph Model    Graph Database    Data Warehouse    Technolgy Stack
收稿日期: 2020-05-20      出版日期: 2020-07-25
ZTFLH:  TP393  
基金资助:*本文系国家重点研发计划云计算和大数据专项“科学大数据管理系统”(项目编号:2016YFB1000605);中国科学院计算机网络信息中心与国家自然科学基金委员会合作项目“国家自然科学基金大数据知识管理服务平台”(项目编号:GC-FG4161781);中国烟草总公司科技重大专项项目“烟草科研数据融合与关联挖掘关键技术研究”的研究成果之一(项目编号:110201801019(SJ-01))
通讯作者: 沈志宏     E-mail: bluejoe@cnic.cn
引用本文:   
沈志宏,赵子豪,王海波. 以图为中心的新型大数据技术栈研究 *[J]. 数据分析与知识发现, 2020, 4(7): 50-65.
Shen Zhihong,Zhao Zihao,Wang Haibo. Big Data Technology Stack Shifting: From SQL Centric to Graph Centric. Data Analysis and Knowledge Discovery, 2020, 4(7): 50-65.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0452      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I7/50
Fig.1  Apache Kylin提供SQL的多维统计接口[15]
Fig.2  以SQL为中心的技术栈
Fig.3  BigDAWG系统架构[27]
名称 顶点规模 边规模 描述
Wiki-Talk 2 394 385篇文章 5 021 410条交流关系 Wikipedia Talk网络
Amazon0601 403 394类商品 3 387 388条“合买”(Co-purchasing) Amazon产品合买记录
Flickr 11 195 144张照片 34 734 221条“喜欢” Flickr照片及“喜欢”记录
USA Patents 3 774 768项专利 16 518 948条引用关系 美国专利(1975~1999年)及引用关系
DBLP Data 4 215 613篇论文 9 086 030条与作者的关系 DBLP论文及作者关系
musae-github 37 700个深度开发者 289 003条“关注” GitHub开发者关系网络
roadNet-CA 1 965 206个路口 2 766 607条道路 California公路网络
Table 1  关系网络数据集的数据规模示例
映射方法 顶点
关系模型 表的一行映射成一个顶点,每一列列映射成顶点的属性 主外键关联映射成边
KV模型 一个KV对映射成一个具有一个属性的顶点
列式模型 表的一行映射成一个顶点,每一列列映射成顶点的属性
文档模型 一个文档映射成一个顶点,文档的字段映射成顶点的属性 文档的嵌套关系映射成边
Table 2  图数据模型对其他模型的表达能力
Fig.4  图数据库发展趋势[43]
Fig.5  图计算框架发展历史[46]
Fig.6  恐怖分子网络[59]
Fig.7  以图为中心的新型大数据技术栈
Fig.8  数据湖管理系统Delta Lake[65]
Fig.9  图数据中台
工具技术 以SQL为中心的技术栈 以图为中心的技术栈
数据库 关系数据库
查询语言为SQL
驱动包括ODBC、JDBC、DAO等
图数据库
查询语言包括Cypher、SPARQL、Gremlin等
数据湖 结构化、半结构化、非结构化数据的集中混搭式管理
其中结构化数据以关系表为主
一张图管理:基于图的结构化、半结构化、非结构化数据的融合管理
数据仓库 多维数据仓库 多维数据仓库+图数据仓库,增强关系挖掘、社区挖掘等能力
ETL ETL多基于SQL进行 gETL:以图数据为主,包括实体抽取、关系抽取、实体消歧、链接预测等任务
大数据中台 数据服务以SQL报表、数据库CRUD为主 图数据:提倡以图为核心实现数据资产的管理,服务以网络分析、图谱可视化为主中台
Table 3  以SQL、图为中心的技术栈之间的比较
Fig.10  采用属性图表示结构化、非结构化数据
操作符 含义 示例
:: 计算x和y之间的相似度 x::y=0.7
~: 计算x和y是否相似? x~:y=true
!: 计算x和y是否不相似? x!:y=false
<: 计算x是否在y里 x<:y=true
>: 计算x是否包含y y>:x=true
Table 4  CypherPlus针对Package定义的语义操作符
Fig.11  AIPM技术架构
Fig.12  Cypher查询的执行过程
Fig.13  PandaDB总体架构
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