Posted on Categories:Data Modeling, 数据建模代写, 数据科学代写

## 数据科学代写|数据建模代考DATA MODELING代考|ISM4547 Creating a Dimensional Logical Data Model

avatest.org数据建模Data Modeling代写，免费提交作业要求， 满意后付款，成绩80\%以下全额退款，安全省心无顾虑。专业硕 博写手团队，所有订单可靠准时，保证 100% 原创。avatest.org™， 最高质量的数据建模Data Modeling作业代写，服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面，考虑到同学们的经济条件，在保障代写质量的前提下，我们为客户提供最合理的价格。 由于统计Statistics作业种类很多，同时其中的大部分作业在字数上都没有具体要求，因此数据建模Data Modeling作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。

avatest.org™ 为您的留学生涯保驾护航 在数据科学代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的数据科学代服务。我们的专家在数据建模Data Modeling代写方面经验极为丰富，各种数据建模Data Modeling相关的作业也就用不着 说。

## 数据科学代写|数据建模代考DATA MODELING代考|Creating a Dimensional Logical Data Model

Recall the dimensional data model from earlier in this chapter, repeated here in Figure 9.8.
In a dimensional model, each structure is assigned a Table Model type, which is distinguished by an icon displayed in the upper left corner of the entity box. You can further describe the structure in the Table Editor by assigning it a Table Type that displays at the bottom of the entity (e.g., Fixed). The Table Type does not change how the data is handled but provides information to the reader about the data in the structure. It is purely for documentation purposes.

Account Balance is an example of a fact table (on a conceptual and logical data model, this is often called a “meter”). The icon for a meter in ER/Studio is the graph symbol because we are measuring something. A meter is an entity containing a related set of measures. It is not a person, place, event, or thing, as we find on the relational model. Instead, it is a bucket of common measures; in this case, just the measure Account Balance Amount. As a group, common measures address a business process such as Profitability, Employee Satisfaction, or Sales.

A fact table can be further classified into one these four types:

• Aggregate. Also known as a summarization, an aggregate contains information that is stored at a higher level of granularity than translation level details. Aggregates provide quick access to data and can be very user-friendly structures for users and reporting tools. Account Balance is an aggregate.
• Atomic. Contains the lowest level of detail available in the business, often the same level of detail that exists in operational systems such as order entry systems. An
example of an atomic fact in the account balance subject area would be the individual bank account withdrawal and deposit transactions.
• Cumulative. Also known as accumulating, cumulative captures how long it takes to complete a business process. For example, tracking how long it takes from application through completion of a home mortgage application would be represented in a cumulative fact.
• Snapshot. Contains time-related information that details specific steps in the life of the entity. For example, snapshot information for a sale could contain information such as when the order was created, confirmed, shipped, delivered, and paid for.

## 数据科学代写|数据建模代考DATA MODELING代考|Physical Data Model Explanation

The physical data model (PDM) is the logical data model compromised for specific software or hardware. On the CDM, we learn what the terms, business rules, and scope would be for a new order entry system. After understanding the need for an order entry system, we create a LDM representing the business solution. It contains all of the attributes and business rules needed to deliver the system. For example, the conceptual data model will show that a Customer may place one or many Orders. The LDM will capture all of the details behind Customer and Order such as the customer’s name, their address, and the order number. After understanding the business solution, we move on to the technical solution and build the PDM. We may make some modifications to the
Customer and Order structures, for example, for factors such as performance or storage. While building the PDM, we address the issues that have to do with specific hardware or software such as:

• We have a big data scenario, so how can we process a lot of data very quickly and then afterwards analyze it very quickly?
• How can we make this information secure?
• How can we answer this business question in less than two seconds?
Note that in the early days of data modeling, when storage space was expensive and computers were slow, there were major modifications made to the PDM to make it work efficiently. In some cases, the PDM looked like it was for an entirely different application than the LDM. As technology improved, the PDM started looking more like the LDM. Faster and cheaper processors, cheaper and more generous disc space and system memory, and also specialized hardware have all played their part to make the physical look more like its logical counterpart. However, with big data processing and analytical tools becoming more mainstream, there is now (at least temporarily) a large difference again between physical and logical. Physical big data designs can even be file- or documentbased to allow for fast loading and analyzing of data. So be aware of physical data models that are all in one table (or file); it may be the optimal design depending on the database technology.

## 数据科学代写|数据建模代考DATA MODELING代考|Creating a Dimensional Logical Data Model

• 总计的。也称为摘要，聚合包含存储在比翻译级别详细信息更高粒度级别的信息。聚合提供对数据的快速访问，并且对于用户和报告工具来说是非常用户友好的结构。账户余额是一个汇总。
• 原子。包含业务中可用的最低级别的详细信息，通常与订单输入系统等操作系统中存在的相同级别的详细信息。账户余额主题领域中的一个基本事实的一个
例子是个人银行账户的取款和存款交易。
• 累积。也称为累积，累积捕获完成业务流程所需的时间。例如，跟踪从申请到完成房屋抵押贷款申请所需的时间将在累积事实中表示。
• 快照。包含与时间相关的信息，详细说明实体生命周期中的特定步骤。例如，销售的快照信息可能包含诸如订单创建、确认、发货、交付和付款等信息。

## 数据科学代写|数据建模代考DATA MODELING代考|Physical Data Model Explanation

• 我们有一个大数据场景，那么我们如何快速处理大量数据，然后快速分析呢？
• 我们怎样才能保证这些信息的安全？
• 我们如何在不到两秒的时间内回答这个业务问题？
请注意，在数据建模的早期，当存储空间昂贵且计算机运行缓慢时，对 PDM 进行了重大修改以使其高效工作。在某些情况下，PDM 看起来像是用于与 LDM 完全不同的应用程序。随着技术的进步，PDM 开始看起来更像 LDM。更快、更便宜的处理器、更便宜、更宽敞的磁盘空间和系统内存以及专用硬件都发挥了作用，使物理看起来更像它的逻辑对应物。然而，随着大数据处理和分析工具变得越来越主流，现在（至少暂时）物理和逻辑之间再次存在很大差异。物理大数据设计甚至可以是基于文件或文档的，以允许快速加载和分析数据。所以要注意物理数据模型都在一个表（或文件）中；它可能是取决于数据库技术的最佳设计。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

Posted on Categories:Data Modeling, 数据建模代写, 数据科学代写

## 数据科学代写|数据建模代考DATA MODELING代考|Accy628 Creating a Relational Logical Data Model

avatest.org数据建模Data Modeling代写，免费提交作业要求， 满意后付款，成绩80\%以下全额退款，安全省心无顾虑。专业硕 博写手团队，所有订单可靠准时，保证 100% 原创。avatest.org™， 最高质量的数据建模Data Modeling作业代写，服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面，考虑到同学们的经济条件，在保障代写质量的前提下，我们为客户提供最合理的价格。 由于统计Statistics作业种类很多，同时其中的大部分作业在字数上都没有具体要求，因此数据建模Data Modeling作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。

avatest.org™ 为您的留学生涯保驾护航 在数据科学代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的数据科学代服务。我们的专家在数据建模Data Modeling代写方面经验极为丰富，各种数据建模Data Modeling相关的作业也就用不着 说。

## 数据科学代写|数据建模代考DATA MODELING代考|Creating a Relational Logical Data Model

The two techniques used to build the relational logical data model are normalization and abstraction.
Normalization
When I turned 12, I received a trunk full of baseball cards as a birthday present from my parents. I was delighted, not just because there may have been a Hank Aaron or Pete Rose buried somewhere in that trunk, but because I loved to organize the cards. I categorized each card according to year and team. Organizing the cards in this way gave me a deep understanding of the players and their teams. To this day, I can answer many baseball card trivia questions.

Normalization, in general, is the process of applying a set of rules with the goal of organizing something. I was normalizing the baseball cards according to year and team. We can also apply a set of rules and normalize the attributes within our organizations. Just as those baseball cards lay unsorted in that trunk, our companies have huge numbers of attributes spread throughout departments and applications. The rules applied to normalizing the baseball cards entailed first sorting by year and then by team within a year. The rules for normalizing our attributes can be boiled down to a single sentence:
Make sure every attribute is single-valued and provides a fact completely and only about its primary key.
The underlined terms require more of an explanation.

## 数据科学代写|数据建模代考DATA MODELING代考|Abstraction

Notice the extra flexibility we gain with abstraction. By abstracting Employee into the Party Role concept, we can accommodate additional roles without changes to our model and most likely without changes to our application. Roles such as Contractor and Consumer can be added gracefully without updates to our model. However, this extra flexibility does come with a price. Actually, three high prices:

• Loss of communication. The concepts we abstract are no longer represented explicitly on the model. That is, when we abstract, we often convert column names to entity instances. For example, Employee is no longer an explicit entity but is instead an entity instance of Party Role, with a Role Type Code value of 03 for Employee. One of the main reasons we model is to aid communication, but abstracting can definitely hinder communication.
• Loss of business rules. When we abstract, we can also lose business rules. To be more specific, the rules we enforced on the data model before abstraction now need to be enforced through other means such as through programming code. If we wanted to enforce that an Employee must have a Start Date, for example, we can no longer enforce this rule through the abstracted data model in Figure 9.7.
• Additional development complexity. Abstracting requires sophisticated development techniques to turn attributes into values when loading an abstract structure, or to turn values back into attributes when populating a structure from an abstract source. Imagine the work to populate Party Role from the source Employee. It would be much easier for a developer to load data from an entity called Employee into an entity called Employee. The code would be simpler and it would be very fast to load.

## 数据科学代写|数据建模代考DATA MODELING代考|Abstraction

• 失去沟通。我们抽象的概念不再在模型上明确表示。也就是我们在抽象的时候，经常会将列名转换为实体实例。例如，Employee 不再是显式实体，而是 Party Role 的实体实例，Employee 的 Role Type Code 值为 03。我们建模的主要原因之一是帮助沟通，但抽象肯定会阻碍沟通。
• 业务规则丢失。当我们抽象时，我们也可能会丢失业务规则。更具体地说，我们在抽象之前对数据模型实施的规则现在需要通过其他方式实施，例如通过编程代码。例如，如果我们想强制员工必须有一个开始日期，我们就不能再通过图 9.7 中的抽象数据模型强制执行此规则。
• 额外的开发复杂性。抽象化需要复杂的开发技术，以便在加载抽象结构时将属性转换为值，或者在从抽象源填充结构时将值转换回属性。想象一下从源 Employee 填充 Party Role 的工作。开发人员将数据从名为 Employee 的实体加载到名为 Employee 的实体中会容易得多。代码会更简单，加载速度会非常快。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

Posted on Categories:Data Modeling, 数据建模代写, 数据科学代写

## 数据科学代写|数据建模代考DATA MODELING代考|INFO4707 Conceptual Data Model Explanation

avatest.org数据建模Data Modeling代写，免费提交作业要求， 满意后付款，成绩80\%以下全额退款，安全省心无顾虑。专业硕 博写手团队，所有订单可靠准时，保证 100% 原创。avatest.org™， 最高质量的数据建模Data Modeling作业代写，服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面，考虑到同学们的经济条件，在保障代写质量的前提下，我们为客户提供最合理的价格。 由于统计Statistics作业种类很多，同时其中的大部分作业在字数上都没有具体要求，因此数据建模Data Modeling作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。

avatest.org™ 为您的留学生涯保驾护航 在数据科学代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的数据科学代服务。我们的专家在数据建模Data Modeling代写方面经验极为丰富，各种数据建模Data Modeling相关的作业也就用不着 说。

## 数据科学代写|数据建模代考DATA MODELING代考|Conceptual Data Model Explanation

A concept is a key idea that is both basic and critical to your audience. “Basic” means this term is probably mentioned many times a day in conversations with the people who are the audience for the model. “Critical” means the business would be very different or nonexistent without this concept.

The majority of concepts are easy to identify and include ideas that are common across industries, such as Customer, Employee, and Product. An airline may call a Customer a Passenger, and a hospital may call a Customer a Patient, but in general they are all people who receive goods or services. Each concept will be shown in much more detail at the logical and physical phases of design. For example, the Customer concept might encompass the logical entities Customer, Customer Association, Customer Demographics, Customer Type, and so on.

Many concepts, however, can be more challenging to identify, as they may be concepts to your audience but not to others in the same department, company, or industry. For example, Account would most likely be a concept for a bank and for a manufacturing company. However, the audience for the bank conceptual data model might also require Checking Account and Savings Account to be on their model, whereas the audience for the manufacturing conceptual data model might, instead, require General Ledger Account and Accounts Receivable Account to be on the model.

In our publishing data model, for example, an audience that needs to see the entire company on a conceptual may just require the entity Order, yet in communicating with the sales department, the Sales Conceptual Data Model will have more details, showing not only Order but also Order Line and Order Adjustment.

## 数据科学代写|数据建模代考DATA MODELING代考|Relational and Dimensional Conceptual Data Models

Recall from this section’s introduction that relational data modeling is the process of capturing how the business works by precisely representing business rules, while dimensional data modeling is the process of capturing how the business is monitored by precisely representing navigation. There are both relational and dimensional conceptual data models.
Relational CDM Example
The relational conceptual model includes concepts, their definitions, and the relationships that capture the business rules binding these concepts. Unlike the logical and physical data models, as we will see, conceptual models may contain many-to-many relationships. For example, Figure $8.2$ contains part of a financial relational CDM.

Business Rules (listed in the order we would typically walk someone through the model):
Each Customer may own one or many Accounts.
Each Account must be owned by one or many Customers.
Each Account may contain one or many Account Balances.
Each Account Balance must belong to one Account.
Notice that in this example definitions were not displayed directly on the diagram as on the model in Figure 8.1. I find that if the data model is small enough (and the definitions are short enough), it can be a valuable communication tool to display the definitions on the diagram. I also choose to display the definitions when I need to highlight poor or lacking definitions or definitions that I know will spur debate.

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。