数据模型之间的差异 [英] Difference between Data models

问题描述

需要网络模型，关系模型，实体集模型之间的简单区别，为什么和什么时候可以使用每个？

Need a brief difference between network model, relational model,entity set model like why and when can each one be used?

我已经研究了各种资源，但是需要对所述主题有一个简短的概念

I have looked into various resources but there needs to be a brief and concise idea about the said topic

推荐答案

在图形术语中，关系模型是一个无向n其中节点是值并且边是行的图。逻辑上，表表示谓词，行表示关于表示为域的值的实体的命题。边被连接以形成路径并在集合中处理。

In graph terms, the relational model is an undirected n-ary graph in which the nodes are values and the edges are rows. Logically, tables represent predicates and rows represent propositions about entities which are represented as values of a domain. Edges are joined to form paths and processed in sets.

实体关系模型也是无向的n元图，其中一些表表示节点集合和其他表表示边的集合。它是一个建立在关系模型之上的语义框架，虽然它提供了一个看起来比纯关系模型更简单和更丰富的结构，但它实际上更复杂，更不具表达性。它通常使用关系机制来查询和处理。

The entity-relationship model is also an undirected n-ary graph in which some tables represent sets of nodes and other tables represent sets of edges. It's a "semantic framework" built on top of the relational model, and while it offers a seemingly simpler and richer structure than the pure relational model, it's actually more complicated and less expressive. It's usually queried and processed using relational mechanisms.

网络数据模型是一个有向二进制图，其中节点是行，边是指针。与关系模型不同，它通常是按要求处理的，并且边缘被导航到相关节点。它使属性和关系之间的硬性区分（不同于ER，属性是二进制关系）。

The network data model is a directed binary graph in which the nodes are rows and the edges are pointers. Unlike relational models it's usually processed imperatively and edges are navigated to get to related nodes. It makes a hard distinction between attributes and relationships (unlike ER in which attributes are binary relationships).

我不太熟悉实体集模型，看见它在领域。你可能意味着实体关系模型吗？有关实体集模型的详情，请参见数据结构和访问在数据库系统中。

I'm not very familiar with the entity set model and haven't seen it in the field. Did you perhaps mean the entity-relationship model? For more on the entity set model, see Data structures and accessing in data-base systems.

存在不同的数据模型，因为人们对如何最好地组织，操作和访问数据有不同的概念。关系模型是唯一已被证明等价于一阶逻辑的模型。虽然这些模型的知识在理解数据方面是非常有价值的，但我们不能自由地选择模型，而是必须在倾向于实现模型和特征的混杂的软件系统中进行选择。

Different data models exist since people have different concepts of how best to organize, manipulate and access data. The relational model is the only one that has been proven equivalent to first-order logic. While knowledge of these models are invaluable in understanding data, we don't get to choose models freely, but must choose among software systems that tend to implement a mish-mash of models and features.

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