blocks|key|1127740|text|db4o|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1127741|引用自“关于”页面：|1127742|db4o是一个开放源码对象数据库，它允许.NET和.NET开发人员只使用一行代码存储和检索任何应用程序对象，而无需预先定义或维护单独的、僵化的数据模型。|blockquote|1127743|entityMap|0|LINK|mutability|MUTABLE|url|http://www.db4o.com/^0|0|4|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|6|7|U|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|V|8|@]|9|@]|C|$]]|$1|I|3|-4|5|6|7|W|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

<a href="http://www.db4o.com" rel="nofollow noreferrer">db4o</a>

Quote from the "about" page:

<blockquote>
 db4o is the open source object database that enables Java and .NET developers to store and retrieve any application object with only one line of code, eliminating the need to predefine or maintain a separate, rigid data model. 
</blockquote>

blocks|key|831008|text|旧的非关系数据库：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|831009|网络数据库|offset|length|831010|层次数据库|831011|当关系变得可行时，这两种关系基本上都过时了。|831012|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Network_database|1|http://en.wikipedia.org/wiki/Hierarchical_database^0|0|0|5|0|0|0|5|1|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@$D|V|E|W|1|X]]|A|$]]|$1|F|3|G|5|6|7|Y|8|@]|9|@$D|Z|E|10|1|11]]|A|$]]|$1|H|3|I|5|6|7|12|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|13|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]|R|$5|M|N|O|A|$P|S]]]]

Older non-relational databases:

<a href="http://en.wikipedia.org/wiki/Network_database" rel="nofollow noreferrer">Network Database</a>

<a href="http://en.wikipedia.org/wiki/Hierarchical_database" rel="nofollow noreferrer">Hierarchical Database</a>

Both mostly went out of style when relational became feasible.

blocks|key|128772|text|面向列的数据库也是一种不同的动物。不过，他们中的许多人确实支持标准关系数据库SQL。它们通常用于数据仓库类型的应用程序。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|128773|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Column-oriented_DBMS^0|0|7|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

<a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS" rel="nofollow noreferrer">Column-oriented databases</a> are also a bit of a different animal. Many of them do support standard relational database SQL though. These are generally used for data warehouse type applications.

blocks|key|1128015|text|语义网也是一种非关系数据存储模式。没有关系，所有元数据都以与数据相同的方式存储，而且每个实体都有其独特的属性集。实现语义网络标准RDF的开源项目包括耶娜和芝麻.|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1128016|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Semantic_web|1|http://jena.sourceforge.net/|2|http://www.openrdf.org/about.jsp^0|0|3|0|22|2|1|25|2|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://en.wikipedia.org/wiki/Semantic_web" rel="nofollow noreferrer">Semantic Web</a> is also a non-relational data storage paradigm. There are no relations, all metadata is stored in the same way as data, and every entity has potentially its own unique set of attributes. Open-source projects that implement RDF, a Semantic Web standard, include <a href="http://jena.sourceforge.net/" rel="nofollow noreferrer">Jena</a> and <a href="http://www.openrdf.org/about.jsp" rel="nofollow noreferrer">Sesame</a>.

blocks|key|128625|text|亚马逊的SimpleDB不是非关系型的吗？|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|128626|entityMap|0|LINK|mutability|MUTABLE|url|http://www.amazon.com/gp/browse.html?node=342335011^0|4|8|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Isn't Amazon's <a href="http://www.amazon.com/gp/browse.html?node=342335011" rel="nofollow noreferrer">SimpleDB</a> non-relational?

blocks|key|128667|text|正如埃里克所提到的，db4o是一个面向对象数据库管理系统+(OODBMS)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|128668|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Object_database^0|H|B|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

db4o, as mentioned by Eric, is an <a href="http://en.wikipedia.org/wiki/Object_database" rel="nofollow noreferrer">Object-Oriented database management system</a> (OODBMS).

blocks|key|830978|text|有基于对象的数据库(例如，Gemstore)。谷歌的大表和Amason的简单存储我不知道你会如何分类，但两者都是基于地图减少。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|830979|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

There's object-based databases(Gemstore, for example). Google's Big-Table and Amason's Simple Storage I am not sure how you would categorize, but both are map-reduce based.

blocks|key|128786|text|我们一直在研究的非关系文档数据库是Apache+CouchDB。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|128787|Apache+CouchDB是一个分布式的、容错的、无模式的面向文档的数据库，可以通过RESTful+HTTP/JSON访问.在其他特性中，它提供了健壮的增量复制和双向冲突检测和解决，并且使用面向表的视图引擎(使用JavaScript作为默认视图定义语言)可以查询和索引。|blockquote|128788|我们感兴趣的是提供一个分布式访问用户首选项存储，该存储可以避免更改，我们可以序列化来自XULRunner的首选项对象，并使用基于XULRunner的客户端应用程序的Javascript轻松地访问这些对象。|128789|entityMap|0|LINK|mutability|MUTABLE|url|http://incubator.apache.org/couchdb/^0|H|E|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$]]|$1|G|3|H|5|6|7|V|8|@]|9|@]|C|$]]|$1|I|3|-4|5|6|7|W|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

A non-relational document oriented database we have been looking at is <a href="http://incubator.apache.org/couchdb/" rel="nofollow noreferrer">Apache CouchDB</a>.

<blockquote>
 Apache CouchDB is a distributed, fault-tolerant and schema-free document-oriented database accessible via a RESTful HTTP/JSON API. Among other features, it provides robust, incremental replication with bi-directional conflict detection and resolution, and is queryable and indexable using a table-oriented view engine with JavaScript acting as the default view definition language.
</blockquote>

Our interest was in providing a distributed access user preferences store that would be immune to shape changes to which we could serialize preference objects from Java and access those just as easily with Javascript from a XULRunner based client application.

blocks|key|1128132|text|我想更详细地介绍Bill关于语义网和triplestore的答案，因为这是我目前正在做的事情，我有话要说。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1128133|triplestore背后的想法是存储一个基于图形的数据库，其数据模型根位于RDF中。使用RDF，您可以描述节点之间的节点和关联(换句话说，边缘)。数据分为三组：|1128134|start+node+----relation---->+end+node+|code-block|syntax|javascript|1128135|(在RDF语音中:主语-谓词->宾语)。有了这个非常简单的数据模型，任何数据网络都可以通过添加越来越多的三元组来表示，前提是给节点和关系赋予意义。|1128136|RDF非常通用，它是一种基于图形的数据模型，非常适合于搜索条件，在任何组合中都可以查找具有特定组合的主题、谓词或对象的所有三元组。最后，通过一种名为SPARQL的查询语言，您还可以执行更复杂的查询，这种操作可以归结为图同构搜索到图上，在拓扑和节点边缘意义上都是如此(稍后我们将看到这一点)。SPARQL只允许选择(和类似)查询。没有删除，没有插入，没有更新。查询的信息(例如，您感兴趣的特定节点)映射到表中，这就是查询的结果。|1128137|现在，拓扑本身并不意味着什么。为此，已经发明了一种模式语言。实际上，不止一种，并且称之为模式语言，在某些情况下，是非常有限的。今天最著名和使用的是RDF、OWL+(Lite和Full)，它们早于过时的DAML%2BOIL。这些语言的要点是，通过赋予节点一种类型(也被描述为三元组)和关系(边)赋予节点一个意义。此外，您还可以定义这些关系的“范围”和“域”，或者以不同的方式说明什么类型是开始节点，什么类型是结束节点:例如，可以说属性"numberOfWheels“只能应用于将车辆类型的节点连接到一个非零整数值。|1128138|+ns:MyFiat+--rdf:type-->+ns:Vehicle
+ns:MyFiat+--ns:numberOfWheels->+4|1128139|现在，您可以在两个方面使用这些本体论:验证和推断。今天，验证并不那么花哨，但我已经看到了使用的实例。推理在今天是很酷的，因为它允许推理。推理基本上采用包含一组三元组的RDF图，获取本体，将它们混合到一个包含“推理引擎”的三层数据库中，并像魔术一样，推理引擎根据您的本体描述发明三元组。示例:假设您只是将这些信息存储在数据库中。|1128140|ns:MyFiat+--ns:numberOfWheels-->+4|1128141|没别的事了。没有指定有关此节点的类型，但推理引擎将自动添加三个类型，即：|1128142|ns:MyFiat+--rdf:type-->+ns:Vehicle|1128143|因为您在本体中说过，只有类型为Vehicle的对象才能被属性numberOfWheels描述。|1128144|相反，您可以使用推理机根据本体验证数据，从而拒绝不兼容的数据(类似于XML的XML)。在这种情况下，您需要这两个三元组才能使您的数据被triplestore成功接受。|1128145|triplestores的其他特性是公式和上下文感知存储。公式是描述假设事物的语句(和通常一样，三元组主谓宾语)。我从来没有用过公式，所以我不想再详细讲一些我不知道的东西。上下文感知基本上是子图:存储三元组的问题是，您没有什么可说这些三元组来自何处。假设您有两个经销商描述了一个组件的相同价格。一个说价格是5.99，另一个是4.99。如果您只是将这两个三元组存储到数据库中，那么现在您就不知道是谁声明了每个信息。解决这个问题有两种方法。|1128146|一个是物化。具体化意味着存储更多的三元组来描述另一个三元组。这是浪费，并使生活地狱，因为你必须把每一个三倍，你的储存。另一种选择是背景意识。拥有一个上下文感知的存储，就像能够将一组三元组放入一个带有标签的容器中(上下文标识符)。您现在可以将此标识符用作附加语句的主题，从而在单个操作中描述一组三元组。|1128147|entityMap^0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|19|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|1A|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|1B|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|1C|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|1D|8|@]|9|@]|A|$]]|$1|O|3|P|5|F|7|1E|8|@]|9|@]|A|$G|H]]|$1|Q|3|R|5|6|7|1F|8|@]|9|@]|A|$]]|$1|S|3|T|5|F|7|1G|8|@]|9|@]|A|$G|H]]|$1|U|3|V|5|6|7|1H|8|@]|9|@]|A|$]]|$1|W|3|X|5|F|7|1I|8|@]|9|@]|A|$G|H]]|$1|Y|3|Z|5|6|7|1J|8|@]|9|@]|A|$]]|$1|10|3|11|5|6|7|1K|8|@]|9|@]|A|$]]|$1|12|3|13|5|6|7|1L|8|@]|9|@]|A|$]]|$1|14|3|15|5|6|7|1M|8|@]|9|@]|A|$]]|$1|16|3|-4|5|6|7|1N|8|@]|9|@]|A|$]]]|17|$]]

I'd like to detail more on Bill Karwin's answer about semantic web and triplestores, since it's what I am working on at the moment, and I have something to say on it.

The idea behind a triplestore is to store a graph-based database, whose datamodel roots in RDF. With RDF, you describe nodes and associations among nodes (in other words, edges). Data is organized in triples :

<pre><code>start node ----relation----&gt; end node 
</code></pre>

(in RDF speech: subject --predicate--> object). With this very simple data model, any data network can be represented by adding more and more triples, provided you give a meaning to nodes and relations.

RDF is very general, and it's a graph-based data model well suited for search criteria looking for all triples with a particular combination of subject, predicate, or object, in any combination. Eventually, through a query language called SPARQL, you can also perform more complex queries, an operation that boils down to a graph isomorphism search onto the graph, both in terms of topology and in terms of node-edge meaning (we'll see this in a moment). SPARQL allows you only SELECT (and similar) queries. No DELETE, no INSERT, no UPDATE. The information you query (e.g. specific nodes you are interested in) are mapped into a table, which is what you get as a result of your query.

Now, topology in itself does not mean a lot. For this, a Schema language has been invented. Actually, more than one, and calling them schema languages is, in some cases, very limitative. The most famous and used today are RDF-Schema, OWL (Lite and Full), and they predate from the obsolete DAML+OIL. The point of these languages is, boiling down stuff, to give a meaning to nodes (by granting them a type, also described as a triple) and to relationships (edges). Also, you can define the "range" and "domain" of these relationships, or said differently what type is the start node and what type is the end node: you can say for example, that the property "numberOfWheels" can be applied only to connect a node of type Vehicle to a non-zero integer value. 

<pre><code> ns:MyFiat --rdf:type--&gt; ns:Vehicle
 ns:MyFiat --ns:numberOfWheels-&gt; 4
</code></pre>

Now, you can use these ontologies in two directions: validation and inference. Validation is not that fancy today, but I've seen instances of use. Inference is what is cool today, because it allows reasoning. Inference basically takes a RDF graph containing a set of triples, takes an ontology, mixes them into a triplestore database which contains an "inference engine" and like magic the inference engine invents triples according to your ontological description. Example: suppose you just store this information in the database 

<pre><code>ns:MyFiat --ns:numberOfWheels--&gt; 4
</code></pre>

and nothing else. No type is specified about this node, but the inference engine will add automatically a triple saying that 

<pre><code>ns:MyFiat --rdf:type--&gt; ns:Vehicle
</code></pre>

because you said in your ontology that only objects of type Vehicle can be described by a property numberOfWheels. 

Conversely, you can use the inference engine to validate your data against the ontology so to refuse not compliant data (sort of like XML-Schema for XML). In this case, you will need both triples to have your data successfully accepted by the triplestore.

Additional characteristics of triplestores are Formulas and Context-aware storage. Formulas are statements (as usual, triples subject predicate object) that describe something hypothetical. I never used Formulas, so I won't go into more details of something I don't know. Context awareness are basically subgraphs: the problem with storing triples is that you don't have anything to say where these triples come from. Suppose you have two dealers that describe the same price of a component. One says that the price is 5.99 and the other 4.99. If you just store both triples into a database, now you don't know anything about who stated each information. There are two ways to solve this problem.

One is reification. Reification means that you store additional triples to describe another triple. It's wasteful, and makes life hell because you have to reify every and each triple you store. The alternative is context-awareness. Having a context-aware storage It's like being able to box a bunch of triples into a container with a label on it (the context identifier). You now can use this identifier as subject for additional statements, hence describing a bunch of triples in a single action.

blocks|key|1128042|text|4.航行。包括树/层次结构和图形/网络。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1128043|文件系统、语义web、XML、对象数据库、CODASYL和许多其他都属于这个类别。|1128044|那四个差不多就是了。|1128045|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

<h1>4. Navigational. Includes Tree/Hierarchy and Graph/Network.</h1>
File systems, the semantic web, XML, Object databases, CODASYL, and many others all fit into this category.
Those 4 are pretty much it.

blocks|key|1128071|text|还有所谓的“倒排索引”或“倒排清单”数据库。软件公司的Adabas产品就是一个例子。与层次结构一样，由于遗留考虑或某些情况下的性能优势(通常是高端事务应用程序)，这些数据库继续在大型企业或大学环境中使用。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|1128072|entityMap^0|R|6|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

There is also what is referred to as an "inverted index" or "inverted list" database. Software AG's Adabas product would be an example. As with hierachical, these databases continue to be used in large corporate or university environments because of legacy considerations or due to a performance advantage in certain situations (typically high-end transactional applications).

blocks|key|1128077|text|有一些基本系统(基本上是可用的，软状态，最终是一致的)，它们可以很好地处理包含大量数据的简单数据模型。谷歌的BigTable，Dojo的Persevere，亚马逊的Dynamo，Facebook的Cassandra就是一些例子。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1128078|请参阅链接|offset|length|1128079|entityMap|0|LINK|mutability|MUTABLE|url|http://queue.acm.org/detail.cfm?id=1394128^0|0|3|2|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@$D|P|E|Q|1|R]]|A|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]]]

There are BASE systems (Basically Available, Soft State, Eventually consistent) and they work well with simple data models holding vast volumes of data. Google's BigTable, Dojo's Persevere, Amazon's Dynamo, Facebook's Cassandra are some examples.

See <a href="http://queue.acm.org/detail.cfm?id=1394128" rel="nofollow noreferrer">LINK</a>

blocks|key|128953|text|照明相关数据库是一种新的革命性的非关系数据库.关联数据库管理系统(CDBMS)是一种独立的数据模型，其设计目的是在分析系统环境中有效地处理非计划的、特殊的查询。与关系数据库管理系统或面向列的数据库不同，关联数据库使用基于值的存储(VBS)架构，其中每个唯一的数据值只存储一次，自动生成的索引系统维护所有值的上下文(数据为100%25索引)。查询是使用自然语言而不是SQL+(NoSQL)执行的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|128954|了解更多信息，请访问:+www.datainnovationsgroup.com|128955|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

The illuminate Correlation Database is a new revolutionary non-relational database. The Correlation Database Management Dystem (CDBMS) is data model independent and designed to efficiently handle unplanned, ad hoc queries in an analytical system environment. Unlike relational database management systems or column-oriented databases, a correlation database uses a value-based storage (VBS) architecture in which each unique data value is stored only once and an auto-generated indexing system maintains the context for all values (data is 100% indexed). Queries are performed using natural language instead of SQL (NoSQL). 

Learn more at: www.datainnovationsgroup.com

What are the other types of database systems out there. I've recently came across couchDB that handles data in a non relational way. It got me thinking about what other models are other people is using.

So, I want to know what other types of data model is out there. (I'm not looking for any specifics, just want to look at how other people are handling data storage, my interest are purely academic)

The ones I already know are:

<ol>
<li>RDBMS (mysql,postgres etc..)</li>
<li>Document based approach (couchDB, lotus notes)</li>
<li>Key/value pair (BerkeleyDB) </li>
</ol>

Database system that is not relational

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。

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其他类型的数据库系统是什么。我最近遇到了以非关系方式处理数据的couchDB。这让我想到了其他人正在使用的其他型号。所以，我想知道其他类型的数据模型是什么。(我不是在寻找任何细节，只是想看看其他人是如何处理数据存储的，我的兴趣纯粹是学术性的)我已经知道的是：RDBMS (mysql、postgres等)基于文档的方法(couchDB，lotus )密钥/值对(BerkeleyDB)

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