图形数据库与三元组存储 [英] Graph databases vs. triple stores

问题描述

当前持久保留类似图的结构的最佳选择是什么?图形数据库(例如 Neo4j )或RDF三元存储(例如 Virtuoso )?

例如，我们有以下用例:

• 具有近1000万个节点的弱连接图(类似于集合中的一篇学术论文)；
• 相当少的更新；
• 关键操作:检索特定子图，更新给定子图中的节点，在更新某些节点后重新计算链接分析度量(例如HITS或PageRank).

还希望提供标准API来查询数据以获取第三方应用程序(例如Facebook或Twitter的数据).

解决方案

使用Virtuoso，您可以进行以下工作:

-SPARQL，SQL，SPASQL(SQL内的SPARQL)和SPARQL内的SQL支持(例如，通过魔术/函数谓词/属性处理N元关系).

-作为紧凑型引擎(例如，通过KDE Desktop利用)或大型DBMS，如通过实时170亿Triples + LOD Cloud Cache或较小的DBpedia实时实例所展示.

-在SPARQL中包括全文本索引和文本模式(通过bif:contains)，还包括XPath/Xquery(通过xcontains)

-处理属性图存储时，酸"或非酸"模式同上纲要

-通过转换中间件，它可以从80多个数据源(包括REST API，SOAP服务，超媒体资源，ODBC或JDBC可访问的关系数据源等)中提取数据，并转换为瞬态或持久链接数据图

-链接数据发布是自动的，即在创建DBMS记录后，您将内置链接数据页面作为DBMS的视图.再也不乱了. URL重写规则，303重定向或类似的方法. InterWeb规模的超级键可以正常工作！

仅此而已:-)

What's currently the best choice to persist graph-like structures? Graph databases (e.g. Neo4j) or RDF triple stores (e.g. Virtuoso)?

For example, we have the following use case:

• the weakly connected graph (similar to the one of scholarly papers in a collection) with nearly 10M nodes;
• quite rare updates;
• critical operations: retrieving particular sub-graphs, updating nodes in a given sub-graph, re-computing link analysis measures (e.g. HITS or PageRank) after updating some nodes.

Providing the standard API to query the data for third party applications (a la Facebook's or Twitter's) is desired as well.

解决方案

With Virtuoso you have the following working for you:

-- SPARQL, SQL, SPASQL (SPARQL inside SQL), and SQL inside SPARQL support (e.g. for dealing with N-ary relations via magic/function predicates/properties.

-- works as a compact engine (e.g., as exploited via KDE Desktop) or massive DBMS as demonstrated via the live 17 Billion Triples+ LOD Cloud Cache or the smaller DBpedia live instance.

-- includes Full Text indexing and text patterns in SPARQL (via bif:contains) it also included XPath/Xquery (via xcontains)

-- Acid or Non Acid mode ditto Schema-Last when dealing with Property Graph Store

-- Via Transformation Middleware it can pull data from 80+ data sources (includes REST APIs, SOAP services, Hypermedia Resource, ODBC or JDBC accessible relational data sources etc..) and transform into Transient or Persistent Linked Data graphs

-- Linked Data publishing is automatic i.e., post DBMS record creation you have in-built Linked Data Pages that as views into the DBMS. No messing around re. URL-Rewrite rules, 303 redirects or anything like that. InterWeb scale Super Keys just work!

That's it for now :-)

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