• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

We consider rule expressions defined in relation algebra as a starting point for determining rule styles. We create an ontology for business rules that contains the necessary components to be displayed in the interface. The focus is on how the mapped business rules can be displayed in the interface. The Ampersand tool [6] displays errors directly when detecting business rule violations. The rule modality determines whether the user may or may not proceed without resolving the violation. If the user may proceed, a violation message appears in a yellow box as a warning. If the violation may not even be temporary, then the message box is red.

Ampersand classifies rules by the method of enforcement, which can be an axiom, invariant rule, process rule or automated rule [6]. The syntax of a rule in Ampersand contains a purpose, meaning, message, violation text and expression of the rule in relation algebra. The purpose and meaning are used to inform the end user why a rule exists and what it means. The message and violation text contain information to be displayed on screen when the rule is violated. The violation text can contain references to the concepts and relations that cause the violation, whereas the message informs the user without explicitly mentioning the affected instance. We use the message of a rule in our research for displaying rule violations. For process and automated rules, a role is defined that should maintain the business rule.

Another Ampersand feature is limiting input values to a composition of relations. This feature prevents users from selecting values that would trigger a violation. While Ampersand displays violations on screen, it does not change the style of a field when a rule states that this field should be mandatory. The limitation of input values for an input field based on a composition of relations must be configured separately, as the composition is not derived from business rules specified by a business engineer. In earlier work, we use Ampersand to demonstrate how relation algebra implementations of business rules apply to IT alignment [4].

Fresnel displays RDF data in a human readable form on semantic browsers [11]. CSS [1] has a significant influence on Fresnel: CSS does for XML and HTML what Fresnel does for RDF. For example, the CSS concepts of selectors and the formatting model are also important to Fresnel. A CSS selector specifies components of XML and HTML documents so it can apply a given style to them. The formatting model for CSS is the HTML document display, with familiar document structure components such as paragraphs, along with a more abstract box model. A CSS rule has a selector and a style declaration. A CSS declaration can say which type of formatting model component the selected document content appears in. A CSS declaration also defines the more familiar aspects of CSS visual style for selected content, such as font size.

While CSS has its own syntax, Fresnel code consists of RDF triples, which conform to the RDFS-defined ontology for Fresnel. Fresnel has two main concepts: lenses and formats, both of which correspond roughly to the CSS rule. Lenses determine which properties of RDF (Resource Description Format) data to show and in what order. Formats determine how to display, typically using CSS.

The remaining sections evaluate our proposed model for rule styles by demonstration. We implement business rules retrieved from $E U$-Rent [10]. Our prototype uses and, where needed, extends an OWL-defined ontology for EU-Rent [12] and an Ampersand implementation for EU-Rent [5]. Each demonstration has this structure:

• Explanation of the rule and desired behavior of the rule style
• Demonstration in Ampersand
• Encoding in the Semantic Web
• Proposed Fresnel implementation
• Example of the rule style output in Semantic MediaWiki.
Our ontology for alethic and deontic business rules is derived from how Ampersand captures business rules with the rule syntax [6]. We made the rule types in our ontology explicit as OWL restrictions. The ontology can be extended with additional subclasses and properties relevant for each rule type. A business rule is created as subclass of a new general violation class. The rules contain class restrictions modelled as equivalent classes that are asserted by as style in Fresnel with Fresnel Violations. A property for the violation message of the business rules captures the message to be displayed for the violation.

The message annotation is also similar in Ampersand. The main difference is that Ampersand can also integrate classes and properties (or concepts and relations) in the message using the keyword VIOLATION, whereas our message is an annotation property that only contains text. A comparison of syntax used in Ampersand and our Violation ontology is listed in Table 2. This paper’s code fragments use bold font to emphasis specific aspects in the syntax of the various languages. In Ampersand, bold font indicates where the logic is defined, while the rest is names and strings. The Fresnel code fragments have our own extensions displayed in bold.

This paper’s Semantic Web definitions of this same type of rule are usually as OWL equivalent classes of OWL restrictions, as the code fragments in the upcoming sections show. Items that violate of the rule get inferred as an instance of the rule’s assigned class. The benefit of using this method is that is guides exchange of rule expressions between relation algebra and OWL, provided that the RDFS classes and properties have equivalents in the concepts and relations in Ampersand. In each demonstration, we show Semantic Web code. We use bold font for URLs that we make, either as extensions to existing ontologies, or as our own example triples. The restriction that determines whether an instance will be inferred is listed under the header equivalent class. Constructs that the Violation ontology introduces have the namespace prefix “fresvio:”.

This section’s implementation shows a list of violations of the EU-Rent rule that prohibits a rental period from exceeding 90 days. While an alethic rule would prevent any initial car rental reservation from lasting more than 90 days, one can extend reservations during rental, and renters can return cars late. Therefore, an open car rental can become overdue and thus trigger a rule violation. Process lists inform the user of this kind of rule violation. Since neither relation algebra nor the Semantic Web perform math, we simply add a data property to the RDFS ontology code, and a relation in relation algebra representation, each of which indicate overextended rentals. For Ampersand, an example for computing the maximum rental days is made [5].

This rule is post-justified, and thus deontic, and similar to the process rule in Ampersand. The Ampersand tool displays existing process rule violations as a list (see Fig. 3). Clicking on the warning message shows the elements causing the violation. Clicking on one presents a form to amend information about the element. This can be viewed as a process list. The rule in Ampersand is expressed as follows:
RULE max90Days : I [Carmovement] I- -longerthang0days
MESSAGE
“There are car rentals open for more than 90 days”
As before, we define this on the Semantic Web as an OWL restriction. A data property states whether the car movement exceeds the 90 -day maximum. We verify the rule with an example with a Car Movement individual whose property isOverdue is true, which then infers it as a CarMovementopenoverdue.

Our goal is to show all instances of class CarMovementopenoverdue in a table overview, similar to how Ampersand displays a process list. Our Fresnel code defines a

lens that selects the URL for the class. This lens displays the message and then all objects linked by the inverse property for rdf : type, which thus infers all movements violating this rule. The Fresnel lens code is:
vioRent:CarMovementopenoverduelns a fresnel: Lens ; fresnel:instanceLensDomain $\quad$ VioRent: CarMovementopenOverdue ; fresnel:purpose fresnel:defaultLens; fresnel: showProperties $\quad$ ( fresvio:message fresvio:hasinstance ) .

## 商业建模代考

＆符号通过执行方法对规则进行分类，可以是公理、不变规则、过程规则或自动规则[6]。Ampersand 中规则的语法包含关系代数中规则的目的、含义、消息、违规文本和表达式。目的和含义用于告知最终用户规则存在的原因及其含义。消息和违规文本包含违反规则时要在屏幕上显示的信息。违规文本可以包含对导致违规的概念和关系的引用，而消息会通知用户而没有明确提及受影响的实例。我们在研究中使用规则信息来显示违反规则的情况。对于流程和自动化规则，定义了一个应该维护业务规则的角色。

Fresnel 在语义浏览器 [11] 上以人类可读的形式显示 RDF 数据。CSS [1] 对 Fresnel 有重大影响：CSS 对 XML 和 HTML 的作用就像 Fresnel 对 RDF 的作用一样。例如，选择器的 CSS 概念和格式化模型对 Fresnel 也很重要。CSS 选择器指定 XML 和 HTML 文档的组件，因此它可以将给定的样式应用于它们。CSS 的格式化模型是 HTML 文档显示，具有熟悉的文档结构组件（例如段落）以及更抽象的框模型。CSS 规则有一个选择器和一个样式声明。CSS 声明可以说明所选文档内容出现在哪种类型的格式化模型组件中。CSS 声明还定义了所选内容的 CSS 视觉样式更熟悉的方面，例如字体大小。

• 规则说明和规则样式的期望行为
• & 符号中的演示
• 语义网中的编码
• 建议的菲涅耳实施
• Semantic MediaWiki 中的规则样式输出示例。
我们的 alethic 和 deontic 业务规则本体源自 Ampersand 如何使用规则语法 [6] 捕获业务规则。我们将本体中的规则类型明确表示为 OWL 限制。可以使用与每个规则类型相关的附加子类和属性来扩展本体。业务规则被创建为新的一般违规类的子类。规则包含建模为等效类的类限制，这些类限制在 Fresnel with Fresnel Violations 中由 as 样式声明。业务规则的违规消息的属性捕获要为违规显示的消息。

Ampersand 中的消息注释也类似。主要区别在于，Ampersand 还可以使用关键字 VIOLATION 在消息中集成类和属性（或概念和关系），而我们的消息是仅包含文本的注释属性。表 2 中列出了 Ampersand 和我们的 Violation 本体中使用的语法比较。本文的代码片段使用粗体字来强调各种语言语法中的特定方面。在与符号中，粗体表示逻辑的定义位置，其余的是名称和字符串。菲涅耳代码片段有我们自己的扩展，以粗体显示。

RULE max90Days : I [Carmovement] I- -longerthang0days
MESSAGE
“有租车营业时间超过 90 天”

vioRent:CarMovementopenoverduelns a fresnel: Lens ；菲涅耳：instanceLensDomainVioRent: CarMovementopenOverdue ; 菲涅尔：目的菲涅尔：defaultLens；菲涅尔：showProperties（fresvio：消息fresvio：hasinstance）。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。