blocks|key|410589|text|Virtuoso+-是Bio2RDF和DBPedia使用的数据存储区|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|410590|entityMap|0|LINK|mutability|MUTABLE|url|http://virtuoso.openlinksw.com/|1|http://quebec.bio2rdf.org/sparql|2|http://dbpedia.org/sparql^0|0|8|0|B|7|1|J|7|2|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]|$A|W|B|X|1|Y]]|C|$]]|$1|D|3|-4|5|6|7|Z|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]|N|$5|G|H|I|C|$J|O]]]]

<a href="http://virtuoso.openlinksw.com" rel="nofollow noreferrer">Virtuoso</a> - is the datastore used by <a href="http://quebec.bio2rdf.org/sparql" rel="nofollow noreferrer">Bio2RDF</a> and <a href="http://dbpedia.org/sparql" rel="nofollow noreferrer">DBPedia</a>

blocks|key|472029|text|根据卡雷尔的建议，今年在ISWC上展示的其中一个参赛作品使用了4store，它确实有那么大的规模，尽管竞争对手设置了一些奇怪的配置，Gralik的首席技术官(开发4store)向我和同事描述说这很疯狂，但4store可以达到这个规模-+http://4store.org|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|472030|此外，Virtuoso还支持这种规模的商店，它们有一个实时应用程序，您可以使用该应用程序对大多数主要LOD+(链接开放数据)数据源进行SPARQL查询，这些数据源总共约为90亿个Triple|472031|Virtuoso+-+http://virtuoso.openlinksw.com|472032|LOD应用程序-+http://lod.openlinksw.com/sparql|472033|entityMap|0|LINK|mutability|MUTABLE|url|http://4store.org/|1|http://virtuoso.openlinksw.com/|2|http://lod.openlinksw.com/sparql^0|3A|H|0|0|0|B|U|1|0|9|W|2|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@$A|W|B|X|1|Y]]|C|$]]|$1|D|3|E|5|6|7|Z|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|10|8|@]|9|@$A|11|B|12|1|13]]|C|$]]|$1|H|3|I|5|6|7|14|8|@]|9|@$A|15|B|16|1|17]]|C|$]]|$1|J|3|-4|5|6|7|18|8|@]|9|@]|C|$]]]|K|$L|$5|M|N|O|C|$P|Q]]|R|$5|M|N|O|C|$P|S]]|T|$5|M|N|O|C|$P|U]]]]

Following from Kaarel's suggestion one of the entries this year presented at ISWC used 4store which does scale that far though the competitor set it up in some weird configuration which the CTO of Gralik (who develop 4store) described to me and colleagues as 'crazy' but 4store would be capable of that scale - <a href="http://4store.org" rel="nofollow noreferrer">http://4store.org</a>

Also Virtuoso supports stores at this scale, they have a live application that you can use to SPARQL query over the majority of the major LOD (Linked Open Data) data sources which total around 9 billion Triples

Virtuoso - <a href="http://virtuoso.openlinksw.com" rel="nofollow noreferrer">http://virtuoso.openlinksw.com</a> 
LOD Application - <a href="http://lod.openlinksw.com/sparql" rel="nofollow noreferrer">http://lod.openlinksw.com/sparql</a>

blocks|key|1817988|text|我在W3C维基上维护了这个大型三元组的列表：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1817989|http://esw.w3.org/topic/LargeTripleStores|offset|length|1817990|已知有7个三元组能够容纳超过10亿个三元组。其中四个是开源的。如果您有更多的信息，请更新上面提到的维基页面。|1817991|显然，性能取决于您使用它的目的。我在一个大型工业项目中使用了Virtuoso，它相当快。|1817992|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|15|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|R|8|@]|9|@$D|S|E|T|1|U]]|A|$]]|$1|F|3|G|5|6|7|V|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|W|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|X|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|C]]]]

I maintain this list of large triplestores on the W3C wiki: 
<a href="http://esw.w3.org/topic/LargeTripleStores" rel="nofollow noreferrer">http://esw.w3.org/topic/LargeTripleStores</a>

There are 7 seven triplestores that are known to be able to hold over a billion triples. Four of them are open source. Please update the above-mentioned wiki page if you have more information.

Obviously, performance depends on what you use it for. I used Virtuoso in a large-scale industrial project, and it is quite fast.

blocks|key|410683|text|Neo4j处理了大约十亿1%2B的开箱即用的三元组，SAIL+API+here，同时仍然有完整的图形来做像Gremlin或SPARQL这样的高级东西。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|410684|免责声明:我是Neo4j团队的一员。|410685|entityMap|0|LINK|mutability|MUTABLE|url|http://www.neo4j.org/|1|http://components.neo4j.org/neo-rdf/|2|http://wiki.github.com/tinkerpop/gremlin/modeling-rdf-as-a-property-graph^0|0|5|0|X|4|1|1F|7|2|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]|$A|V|B|W|1|X]|$A|Y|B|Z|1|10]]|C|$]]|$1|D|3|E|5|6|7|11|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|12|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]|P|$5|I|J|K|C|$L|Q]]]]

<a href="http://www.neo4j.org" rel="nofollow noreferrer">Neo4j</a> handles around 1+ Billion triples out of the box, SAIL API <a href="http://components.neo4j.org/neo-rdf/" rel="nofollow noreferrer">here</a>, while still have the whole graph to do advanced stuff with things like <a href="http://wiki.github.com/tinkerpop/gremlin/modeling-rdf-as-a-property-graph" rel="nofollow noreferrer">Gremlin</a>, or SPARQL.

Disclaimer: I am part of the Neo4j team.

blocks|key|1773095|text|Intellidimension提供了一个名为Semantic+Server的解决方案，它是在微软的SQL+Server2005或2008之上开发的。它很容易扩展到数亿三倍，我知道他们至少有一个客户乐于运行企业部署，拥有超过10亿条语句。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1773096|我是他们的客户之一，处理的数据集超过1亿个。我们的计划是迈向数十亿条语句的100多条。|1773097|entityMap|0|LINK|mutability|MUTABLE|url|http://www.intellidimension.com/|1|http://www.intellidimension.com/products/semantics-server/^0|0|G|0|N|F|1|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

<a href="http://www.intellidimension.com/" rel="nofollow noreferrer">Intellidimension</a> provides a solution called <a href="http://www.intellidimension.com/products/semantics-server/" rel="nofollow noreferrer">Semantic Server</a> that is developed on top of Microsoft's SQL Server 2005 or 2008. It easily scales to the hundreds of millions of triples and I know they have at least one customer happily running an enterprise deployment with over a billion statements.

I am one of their customers working with datasets > 100 million. Our plans are to move towards the 10s of billions of statements.

blocks|key|1773129|text|4store看起来是一个很好的解决方案，但是此时的文档非常稀疏，当我最后一次查看它时，没有从图中删除单个三元组的能力。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1773130|我也会看看BigData|offset|length|1773131|下面是他们主页上的一句话，总结了他们的产品。|1773132|1773133|+Bigdata(R)是一种开源横向扩展存储和计算结构，支持可选事务、非常高的并发性和非常高的聚合IO速率。Bigdata从一开始就被设计为一种分布式数据库体系结构，针对运行在100到1000台机器的集群上的非常高的聚合IO速率进行了优化，但也可以在单服务器模式下运行。Bigdata提供了一个分布式文件系统，类似于Google文件系统，但也适用于工作流队列，数据可扩展的稀疏行存储，类似于Googles广泛认可的bigtable项目，以及用于在集群上并行处理数据密集型工作流的map/reduce处理。|blockquote|1773134|1773135|Bigdata(R)附带一个非常高性能的RDF存储，支持RDF(S)和OWL+Lite推理。Bigdata+RDF+Store是目前唯一能够在索引的动态键范围分区的集群上进行分布式操作的RDF数据库。Bigdata+RDF+Store专为满足超大规模语义对齐和联合的需求而设计。RDF是一种语义Web技术，特别适合于建模图形形状的数据和元数据，例如关联实体-链接模型，由此参与者在与特定问题域相关的实体类型和链接类型的概念本体不断发展的上下文中以自组织方式相互链接。Bigdata+RDF+Store在数据收集系统中可操作地使用，以模式灵活的方式从大量来源创建结构化、半结构化和非结构化数据的混搭。|1773136|entityMap|0|LINK|mutability|MUTABLE|url|http://www.systap.com/bigdata.htm^0|0|5|7|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@]|9|@$D|Y|E|Z|1|10]]|A|$]]|$1|F|3|G|5|6|7|11|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|12|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|13|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|14|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|15|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|16|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V]]]]

4store looks to be a good solution however the documentation is pretty sparse at this time and when I last looked at it there was no ability to delete an individual triple from the graph.
I would also take a look at <a href="http://www.systap.com/bigdata.htm" rel="nofollow noreferrer">BigData</a>
Here is a quote from their main page summarizing their offering.
<blockquote>
Bigdata(R) is an open-source scale-out storage and computing fabric supporting optional transactions, very high concurrency, and very high aggregate IO rates. Bigdata was designed from the ground up as a distributed database architecture optimized for very high aggregate IO rates running over clusters of 100s to 1000s of machines, but can also run in a single-server mode. Bigdata offers a distributed file system, similar to the Google File System but also useful for workflow queues, a data extensible sparse row store, similar to Googles widely recognized bigtable project, and map/reduce processing for parallelizing data intensive workflows over a cluster.
Bigdata(R) comes packaged with a very high-performance RDF store supporting RDF(S) and OWL Lite inference. The Bigdata RDF Store is currently the only RDF database capable of operating distributed on a cluster with dynamic key-range partitioning of indices. The Bigdata RDF Store was designed specifically to meet requirements for very large scale semantic alignment and federation. RDF is a Semantic Web technology particularly well-suited to modeling graph-shaped data and metadata, such as an associative entity-link model, whereby actors are linked to one another in an ad-hoc fashion within the context of an evolving ontology of concepts for entity types and link types related to a particular problem domain. The Bigdata RDF Store is used operationally in data harvesting systems to create mash-ups of structured, semi-structured, and unstructured data from myriad sources in a schema-flexible manner.
</blockquote>

Are there any enterprise-grade database engines (Oracle, MS SQL...etc) that can handle large RDF datasets (320 million) and SPARQL queries? I guess my question is also: is SPARQL/RDF/OWL ready for serving large real-world data warehouses for an enterprise? If not, are there efficient mechanisms for adapting SPARQL/RDF against a typical data warehouse star schema.

Thanks!

Enterprise grade databases that can handle large RDF datasets?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

文章

问答

视频

教程

学习中心

腾讯云实验室

直播

竞赛

腾讯云代码分析专区

腾讯iOA零信任安全管理系统专区

腾讯云架构师技术同盟交流圈

腾讯云数据库专区

腾讯云智能顾问专区

腾讯云原生专区

腾讯混元专区

腾讯云TCE专区

腾讯云Lighthouse专区

腾讯云HAI专区

腾讯云Edgeone专区

腾讯云存储专区

腾讯云智能专区

腾讯轻联专区 

腾讯云开发专区

TAPD专区

腾讯轻量云游戏服专区

EdgeOne AI 安全实战专区

腾讯云最具价值专家

腾讯云架构师技术同盟

腾讯云创作之星

腾讯云开发者先锋

腾讯云AI代码助手

云原生构建

TAPD 敏捷项目管理

Cloud Studio

SDK中心

API中心

命令行工具

功能1上新10个字符

功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符。

功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符。

功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符

功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符

功能4上新

文章&问答评论现已支持表情

全新交互，全新视觉，新增快捷键、悬浮工具栏、高亮块等功能并同时优化现有功能，全面提升创作效率和体验

社区富文本编辑器全新改版！诚邀体验～ 

精选全网热门MCP server，让你的AI更好用 🚀

💥开发者 MCP广场重磅上线！

涵盖代码开发、场景应用、自动测试全流程，助你从零构建专属AI助手

一站式MCP教程库，解锁AI应用新玩法

聚焦“写作效率、视觉美观与运行性能”三方面进行全面升级，为您提供更高效、稳定的创作环境

社区富文本&Markdown编辑器全新改版上线，欢迎大家体验!

诚挚邀请您参与本次调研，分享您的真实使用感受与建议。您的反馈至关重要，感谢您的支持与参与！

社区新版编辑器体验调研

是否有任何企业级数据库引擎(Oracle、MS SQL...etc)可以处理大型SQL...etc数据集(3.2亿)和SPARQL查询？我想我的问题也是: SPARQL/RDF/OWL准备好为企业的大型现实世界数据仓库服务了吗？如果没有，是否有针对典型数据仓库星型模式调整SPARQL/RDF的有效机制。谢谢!

问可以处理大型RDF数据集的企业级数据库？
EN

回答 6

Stack Overflow用户

Stack Overflow用户

Stack Overflow用户

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问可以处理大型RDF数据集的企业级数据库？EN

回答 6

Stack Overflow用户

Stack Overflow用户

Stack Overflow用户

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问可以处理大型RDF数据集的企业级数据库？
EN