SDCS-CON-4 3ADT313900R1501 驱动器技术演示 控制板

SDCS-CON-4 3ADT313900R1501 驱动器技术演示 控制板 

为通用查询而修改，并且没有某些不希望的特征
在可能导致数据完整性丢失的插入、更新和删除操作期间。
规范化的好处是发现可能存在的其他业务规则
忽略了标准化的分析严谨性，并确保捕获
业务逻辑。逻辑模型虽然具有比概念模型更多的部分，
企业专家仍然可以理解。在逻辑层面上
建模风格通常用于实体关系或UML类建模。
在物理层面上，信息交换、存储和传输的确切方式，
并确定被处理。在这个层面上，我们谈论的是数据。效率，
考虑了数据使用的可靠性和有保证的重复性。数据类型，确切的
确定存储数据的格式。数据类型需要容纳所有
允许存储或交换的数据必须有效，并且不允许以下格式：
是不允许的。为了提高效率，可以对实体进行非标准化，以便连接操作
不必执行。逻辑关联可以替换为标识符（例如
实体关系图[ERD]中的关联实体或外键或迁移键，或
类模型中的显式标识符属性或关联类）。密钥、标识符和
设置其他查找方式。索引、哈希和其他机制可以设置为
根据要求允许数据访问。物理目标可以是
以下是：
a、 数据库–关系、对象或平面文件。
b、 消息交换格式——文档（如XML）、二进制（如接口定义
语言（IDL））。
c、 控制论（人机），例如打印或屏幕格式，如表单。
核心流程中的使用
信息和数据模型的使用方式如下：
a、 核心流程之间的通用性和互操作性
1.信息模型为企业参与者具体化
对企业以及它们之间的关系非常重要。
2.信息模型可以作为术语标准化的基础
人、机器和人机的概念相互关系
通信。
3.信息模型可以为企业的
人员通过使用分类法和其他关系结构。这个可以
提高内部行动的清晰性、效率、准确性和互操作性
企业
4.信息模型记录了企业关注的范围
以允许与其他感兴趣的社区进行比较的形式显示共同点
利益。
5.COI协调和统一。
6.权威来源识别和管理。
b、 JCIDS和PPBE
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1） 数据和信息模型可用于确定拟定能力是否：
与其他功能互操作、冗余或填补空白。
c、 SE和DAS
1） 数据模型可用于生成信息的持久存储，例如
数据库。
2） 数据模型可用于生成用于在之间交换数据的格式
机器、人和机器对人。例如，XSD是物理数据
通常为交换格式的模型。Web服务可以与关系
DBMS将生成XML，以便以中实现的数据模型的格式进行交换
DBMS。基础数据模型（物理数据模型和交换
数据格式）不必相同；可以调用翻译人员或调解人：
在交流过程中进行翻译。
3） 数据模型可用于比较执行者是否与数据兼容
交换
4） 相互依赖的数据或信息需求。
5） 里程碑评审期间可使用数据和信息模型来验证
解决方案的互操作性、非冗余性和充分性。
6） 信息模型在服务的初始发现中非常有用，可以知道服务的种类
其可以提供访问或其访问的能力所需的信息。一
信息模型是服务描述的一部分。
7） 数据模型有助于了解如何与服务和
它提供的能力和建立服务合同的能力。数据模型是一部分
服务描述和服务合同。
8） 数据库/源整合和迁移。
9） 标准定义和制定。
10） 调解和跨COI共享。
d、 老年退休金计划
e、 CPM
1） 数据和信息模型可用于确定投资组合的组成部分
拥有：
2） 重叠数据或信息生产（潜在不需要的指示
冗余）。
3） 数据资产管理。

SDCS-CON-4 3ADT313900R1501 驱动器技术演示 控制板 

SDCS-CON-4 3ADT313900R1501 驱动器技术演示 控制板 

modified for general-purpose querying and is free of certain undesirable characteristics
during insertion, update, and deletion operations that could lead to a loss of data integrity.
The benefits of normalization are to uncover additional business rules that might have been
overlooked without the analytical rigor of normalization and ensure the precise capture of
business logic. The logical model, though having more parts than the conceptual model,
should still be understandable by enterprise experts. At the logical-level, some sort of
modeling style is normally used such as Entity-Relationship or UML Class modeling.
At the physical-level, the exact means by which the information is to be exchanged, stored,
and processed is determined. At this level, we are talking about data. The efficiency,
reliability, and assured repeatability of the data use are considered. The datatypes, the exact
format in which the data is stored are determined. The datatype needs to accommodate all
the data that is permissible to store or exchange yet be efficient and disallow formats that
are not permissible. The entities may be de-normalized for efficiency so that join operations
don't have to be performed. Logical associations may be replaced with identifiers (e.g., as
associative entities or foreign or migrated keys in Entity Relationship Diagrams [ERDs] or
explicit identifier attributes or association classes in class models). Keys, identifiers, and
other means of lookup are setup. Indexes, hashes, and other mechanisms may be setup to
allow data access in accordance with requirements. The physical target may be any of the
following:
a. Database – relational, object, or flat file.
b. Message exchange format – document (e.g., XML), binary (e.g., Interface Definition
Language (IDL)).
c. Cybernetic (human – machine), e.g., print or screen formats, such as forms.
Usage in Core Processes
Information and Data models are used in the following ways:
a. Commonality and Interoperability between Core processes
1. Information models materialize for enterprise participants what things are
important to the enterprise and how they are related.
2. Information models can serve as a basis for standardization of terminology and
concept inter-relationships for human, machine, and human-machine
communications.
3. Information models can provide cognitive compactness for an enterprise's
personnel through the use of taxonomies and other relationship structures. This can
improve clarity, efficiency, accuracy, and interoperability of action within the
enterprise.
4. Information models document the scope of things the enterprise is concerned with
in a form that allows comparison with other communities of interest to reveal common
interests.
5. COI coordination and harmonization.
6. Authoritative sources identification and management.
b. JCIDS and PPBE
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1) Data and information models can be used to determine if a proposed capability will
interoperate, be redundant with, or fill gaps in conjunction with other capabilities.
c. SE and DAS
1) Data models can be used to generate persistent storage of information such as in
databases.
2) Data models can be used to generate formats for exchanging data between
machines, humans, and machine-to-human. For example, an XSD is a physical data
model that is generally an exchange format. Web services can be used with relational
DBMS' to generate XML for exchange in the format of the data model implemented in
the DBMS. The underlying data models (the physical data model and the exchange
data format) do not have to be the same; a translator or mediator may be invoked to
translate during the exchange.
3) Data models can be used to compare whether Performers are compatible for data
exchange.
4) Interdependent data or information needs.
5) Data and information models can be used during milestone reviews to verify
interoperability, non-redundancy, and sufficiency of the solution.
6) Information models are useful in initial discovery of a service, to know what sorts
of information it may provide access to or its accessed capabilities need. An
information model is part of a service description.
7) Data models are useful in knowing how to interact with a service and the
capabilities it provides and for establishing the service contract. A data model is part
of a service description and service contract.
8) Database/sources consolidation and migration.
9) Standards definition and establishment.
10) Mediation and cross-COI sharing.
d. OPS Planning
e. CPM
1) Data and information models can be used to determine if components of a portfolio
have:
2) Overlapping data or information production (an indication of potential unwanted
redundancy).
3) Data assets management.