网上商城系统的设计与实现

网上商城系统的设计与实现

摘要：随着互联网的高速发展，互联网技术已经存在于我们生活的各个角落，成为现在生活不可或缺的一部分。如何能够方便、快捷的买到自己想要的商品，成为了大家关心的问题。目前，常见的解决问题的方案是，通过互联网技术，将传统的商店搬到网络上，使人们能够在家里点击鼠标就可以买到自己满意的商品。我们设计的系统是基于Struts2+Spring+Hibernate三大框架搭建的，三大框架的整合使系统耦合性降低，具有较强的兼容性。系统使用jQuery实现事件处理、制作动态效果。采用MVC的设计模式，分模块实现了用户的订单管理、地址管理，管理员的商品管理、订单管理、用户管理等。

本论文在第一章讲解了网上商城的现状，在第二章介绍本系统开发所用到的Struts2+Spring+Hibernate三大框架以及开发工具。在第三章对本系统进行需求分析，并制定系统的总体设计方案，包括系统的总体设计、功能设计以及数据库的设计。最后在第四章具体描述了本系统主要模块的实现。

关键字：电子商务；网上商城；SSH

Design and Implementation Online Shopping System

Abstract: With the rapid development of the Internet, Internet technologies already exist in every corner of our lives, and become an indispensable part of life now. It has become a concern for everyone that how to buy desired commodities conveniently and fast. The solution of solving the problem is to apply Internet technology to move the traditional store to the network. People can buy their goods at home via computer. The online store system is based on three frameworks-Struts2, Spring, Hibernate, which enhance the system compatibility and allow the system to reduce coupling. The system uses jQuery for event handling and creating dynamic effects, and uses MVC design pattern. The system includes some sub-modules, such as user's order management, address management, administrator commodity management, order management, and user management.

The paper is organized as follows. The first chapter explains the current situation of the online store. The second chapter describes three frameworks- Struts2 + Spring + Hibernate and development tools. In the third chapter, the system requirement is analyzed, and the overall scheme is designed, including the design of the overall system, functional design, and database design. The fourth chapter describes the detailed implementation of main modules of the system.

Key words: E-commerce; Online store; SSH

面向事件的垂直搜索引擎设计与实现

摘要：随着互联网的发展，数据呈现海量增长的趋势。这使得从这些数据中找到人们所要的信息，变得越来越困难。搜索引擎对该问题的解决发挥着巨大作用。

本课题的主要内容是采用C/S模式设计一个基础的面向事件的垂直搜索引擎。本课题使用技术有Java、JSP、Hibernate、Struts、正则表达式和ICTCLAS4J 分词系统等。系统的主要功能是获取各个搜索引擎的新闻版块的结果，并按计算的相关度重新排序。该系统将用户所提供的关键词组进行编码，然后获取相关搜索引擎的返回结果。当获得返回结果后，获取源网页的源代码，从中提取如title、keywords、description和正文等参数，计算获得权重值，并将所有的信息写入数据库中。这里权重是使用余弦相似度算法进行计算的。最后，通过将从数据库中读取的信息返回到浏览器进行分页显示。

通过该系统可以对一些搜索引擎的新闻板块的结果进行整合，从而方便用户的使用。

关键词：垂直搜索引擎；面向事件；新闻

Design and Development of Event-Oriented Vertical Search Engine

Abstract: With the development of the Internet, the data show massive growth trend. This makes it more difficult for people to find the desired information from these data. Search engines play a huge role of solving the problem.

The paper uses the C/S Mode to design a basic event-oriented vertical search engine. The technologies used in this topic include Java, JSP, Hibernate, Struts, regular expressions, and ICTCLAS4J segmentation system. The main function of the system is to obtain results of each search engine's news section, and resort these results by calculating the degree of correlation. This system encodes the key phrases provided by uses. Then access the returned results to news pages of relevant search engines. After getting the returned results, the system obtains the source page's source code, then extracts the parameters such as title, keywords, description, and body, obtains the weight value, and writes all of the information in the database. The weight calculated by using the cosine similarity algorithm. Finally, the system returns the information from the database to the browser and shows them by paging.

The system can integrate the results of news sector of some search engines, so it can make user convenient to use these information.

Key words: Vertical search engines; event-oriented; News

摘要：针对从微博中尽可能全面的获取事件信息的需求，提出了基于迭代策略的微博事件查询扩展方法。介绍了事件查询迭代扩展的模型和算法，讨论了该模型的核心技术；根据事件查询项的特点，提出了事件要素的近义扩展和关联扩展方法；根据微博媒体类型的特点，提出了新颖的扩展事件查询项的组装方法。以8个事件查询项为例，面向腾讯微博，在迭代扩展次数、近义扩展、关联扩展等方面进行了实验评测。结果表明，提出的融合了微博媒体类型特点和事件查询内容特点的迭代扩展方法，改善了微博事件查询获取信息的性能。

关键词：事件查询；迭代扩展；近义扩展；关联扩展

Abstract: To meet the requirements of obtaining comprehensive microblog event information as much as possible, an iterative strategy based microblog event query expansion model is proposed, and the key procedures and techniques are discussed. With in deep analysis of the characteristics of event query to the microblog data, our novel model applies the near-synonymy expansion and associated expansion for event elements abstraction and expanded event query generation. We conducted a set of experiments on eight event queries on Tencent microblog data to evaluate iterative expansion number, near-synonymy expansion and associated expansion. The experimental results show that the proposed method of iterative expansion incorporating the characteristics of microblog media and event query improves the performance of obtaining event information from microblog.

Key words: Event query; Iterative expansion; Near-synonymy expansion; Associated expansion

摘要：主题Web采集是信息检索领域内一个将采集技术与过滤方法相结合的新兴研究热点。本文针对多主题信息采集效率低下的问题，调研了主题规则在内置搜索引擎和通用搜索引擎上搜索结果的差异，提出将主题的规则拆分成原子规则的思想，分析了原子规则间的相同、互换、包含三种关系。在原子规则之间关系的基础上，设计了针对内置搜索和通用搜索不同的原子规则分配策略，这样做一方面提高主题信息采集的准确率，另一方面减少搜索采集的次数。针对原子规则直接搜索结果的准确率不高的问题，提出了基于句群的主题与信息相关性的过滤方法。设置138条主题规则（拆分后的原子规则为8223条），14个内置搜索引擎和4个通用搜索引擎，在单位时间内采集到的信息总条数与采集到的相关信息的条数两个方面进行了实验比较。结果表明，所提方法在信息采集数目及相关信息采集数目方面均具有较好的性能。

关键字：多主题信息采集；原子规则；内置搜索；通用搜索；相关性计算

A Method of Multi-Topic Crawling Based on Search Strategy

Abstract: Focused web crawling is a new research hotspot combining crawling and filtering in the field of information retrieval. Aiming at the low efficiency of

multi-topic crawling, the difference between built-in search engines (BSEs) and general search engines (GSEs) is investigated. The idea and method of dividing topic rules into atomic rules are proposed respectively, and three relations (equating relation, exchanging relation and containing relation) between atomic rules are analyzed. Based on atomic rule relations, the different allocation strategies for BSEs and GSEs are designed, which can not only improve the precision of topic-specific crawling, but also reduce crawling times. Furthermore, a method of sentence cluster-based relevance computing between topics and documents is proposed for solving the low precision problem of directly crawling information by atomic rules. The authors conduct an experiment with 138 topic rules (containing 8223 atomic rules), 14 BSEs and 4 GSEs for evaluating the number of crawling information and related information in unit time. The results show that the proposed method offers more effective performances.

Key words: Multi-topic crawling; Atomic rules; Built-in search engines; General search engines; Relevance computing

Abstract: Previous work analyzing social networks has mainly focused on binary friendship relations. However, in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths (e.g., acquaintances and best friends mixed together). In this case, the binary friendship indicator provides only a coarse representation of relationship information. In this work, we develop an unsupervised model to estimate relationship strength from interaction activity (e.g., communication, tagging) and user similarity. More specifically, we formulate a

link-based latent variable model, along with a coordinate ascent optimization procedure for the inference. We evaluate our approach on real-world data from Facebook, showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy.

In recent years, microblogs have become an important source for reporting real-world events. A real world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.