基于Yii框架的模型特征分析管理平台的设计与实现
发布时间：2018-09-09 10:00
【摘要】：随着互联网的快速发展和广泛普及,伴随着市场经济的逐渐成长和电子商务、物流行业等的迅猛增长,互联网广告得到了迅速的发展。机器学习的应用使得互联网广告发生了革命性的变化,在使用机器学习进行广告CTR预估等工作中,特征调研是大家的主要工作,需要进行大量的实验,这些实验通常基于海量的数据(数十或数百TB),大量而繁杂的大数据处理和结果分析工作存在着冗余,分散了调研人员的精力,使得调研效率难以提升。据此,基于Yii框架的模型特征分析管理平台旨在减少冗余工作、提供快捷的实验方式以及对实验结果和分析数据的统一管理,提升调研效率。本文主要是从软件工程的角度,描述分析、设计和开发模型特征分析管理平台的完整过程,论文的主要工作体现在以下方面：(1)介绍了项目的启动背景。平台应对和解决的问题,主要是实验任务配置和操作管理的界面化、数据的管理和可视化查看、对比等。(2)介绍了项目相关的技术。本项目基于Yii框架开发,Yii使用MVC模式。论文中介绍了MVC设计模式和Yii框架,并描述了Yii框架的工作流程。机器学习是整个项目的应用背景,本文简单的介绍了机器学习、模型训练和特征评估等工作。FusionCharts是项目中用到的可视化开发包。(3) 详细分析了项目的需求。详细分析了用户的工作流和数据流,给出了系统的主要用例。(4)描述了项目的总体设计和详细设计。详细描述了数据库中重点数据表的设计,重点功能模块的设计,比如用户权限的管理、数据管理、全流程实验的创建、特征分析、数据对比、平台监控管理等。(5)对项目的关键部分的实现做了详细说明,给出了部分实现细节的代码,以及实现结果的截图等。
[Abstract]:With the rapid development and popularization of the Internet, with the gradual growth of the market economy and the rapid growth of e-commerce, logistics industry, Internet advertising has been rapidly developed. The application of machine learning has revolutionized the Internet advertising. In the work of using machine learning to predict advertising CTR, feature investigation is the main work of everyone, and a large number of experiments are needed. These experiments are usually based on a large amount of data (dozens or hundreds of TB), large and complicated big data processing and analysis of the results of the work there is redundancy, the distraction of the researchers, so that the efficiency of research is difficult to improve. Therefore, the model feature analysis management platform based on Yii framework aims to reduce redundant work, provide rapid experimental methods and unified management of experimental results and analysis data, and improve the efficiency of research. From the point of view of software engineering, this paper describes the complete process of analyzing, designing and developing the model feature analysis management platform. The main work of this paper is as follows: (1) the background of the project is introduced. The main problems to be solved by the platform are the interface of experiment task configuration and operation management, data management and visual view, contrast etc. (2) the project related technology is introduced. This project is based on the Yii framework to develop the Yii using MVC mode. This paper introduces MVC design pattern and Yii framework, and describes the workflow of Yii framework. Machine learning is the application background of the whole project. This paper briefly introduces the machine learning, model training and feature evaluation, and so on, which is the visual development kit used in the project. (3) the requirements of the project are analyzed in detail. The workflow and data flow of users are analyzed in detail, and the main use cases of the system are given. (4) the overall design and detailed design of the project are described. The design of key data tables in database, the design of key function modules, such as the management of user rights, the management of data, the creation of whole flow experiments, the analysis of features, the comparison of data, are described in detail. Platform monitoring and management. (5) the implementation of the key parts of the project is described in detail, and some of the implementation details of the code, as well as the implementation of the results of the screenshot.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP311.52
本文编号：2232058
[Abstract]:With the rapid development and popularization of the Internet, with the gradual growth of the market economy and the rapid growth of e-commerce, logistics industry, Internet advertising has been rapidly developed. The application of machine learning has revolutionized the Internet advertising. In the work of using machine learning to predict advertising CTR, feature investigation is the main work of everyone, and a large number of experiments are needed. These experiments are usually based on a large amount of data (dozens or hundreds of TB), large and complicated big data processing and analysis of the results of the work there is redundancy, the distraction of the researchers, so that the efficiency of research is difficult to improve. Therefore, the model feature analysis management platform based on Yii framework aims to reduce redundant work, provide rapid experimental methods and unified management of experimental results and analysis data, and improve the efficiency of research. From the point of view of software engineering, this paper describes the complete process of analyzing, designing and developing the model feature analysis management platform. The main work of this paper is as follows: (1) the background of the project is introduced. The main problems to be solved by the platform are the interface of experiment task configuration and operation management, data management and visual view, contrast etc. (2) the project related technology is introduced. This project is based on the Yii framework to develop the Yii using MVC mode. This paper introduces MVC design pattern and Yii framework, and describes the workflow of Yii framework. Machine learning is the application background of the whole project. This paper briefly introduces the machine learning, model training and feature evaluation, and so on, which is the visual development kit used in the project. (3) the requirements of the project are analyzed in detail. The workflow and data flow of users are analyzed in detail, and the main use cases of the system are given. (4) the overall design and detailed design of the project are described. The design of key data tables in database, the design of key function modules, such as the management of user rights, the management of data, the creation of whole flow experiments, the analysis of features, the comparison of data, are described in detail. Platform monitoring and management. (5) the implementation of the key parts of the project is described in detail, and some of the implementation details of the code, as well as the implementation of the results of the screenshot.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP311.52
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相关期刊论文 前1条
1 刘庆和;梁正友;;一种基于信息增益的特征优化选择方法[J];计算机工程与应用;2011年12期
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