一种基于有限记忆多LRU的Web缓存替换算法

Web缓存的核心是缓存内容的替换算法.在动态不确定的网络环境下,本文提出一种基于有限记忆的多LRU (LH-MLRU)Web缓存替换算法,它是一种低开销、高性能和适应性的算法.LH-MLRU综合考虑各项因素对Web对象使用多个LRU队列进行分类管理,引入Web对象最近被访问的历史作为缓存内容替换的一个关键因素,来预测对象可能再次被访问的概率.通过周期性的训练参数可以适应动态不确定的网络环境.轨迹驱动的仿真实验表明LH-MLRU在各项性能指标上均优于其他算法,可以显著的提高Web缓存的性能.     

Web-log mining for predictive Web caching

Caching is a well-known strategy for improving the performance of Web-based systems. The heart of a caching system is its page replacement policy, which selects the pages to be replaced in a cache when a request arrives. In this paper, we present a Web-log mining method for caching Web objects and use this algorithm to enhance the performance of Web caching systems. In our approach, we develop an n-gram-based prediction algorithm that can predict future Web requests. The prediction model is then used to extend the well-known GDSF caching policy. We empirically show that the system performance is improved using the predictive-caching approach.     

基于深度优先序列模式挖掘的预取模型

序列模式挖掘能够发现隐含在Web日志中的用户的访问规律,可以被用来在Web预取模型中预测即将访问的Web对象。目前大多数序列模式挖掘是基于Apriori的宽度优先算法。提出了基于位图深度优先挖掘算法,采用基于字典树数据结构的深度优先策略,同时采用位图保存和计算各序列的支持度,能够较迅速地挖掘出频繁序列。将该序列模式挖掘算法应用于Web预取模型中,在预取缓存一体化的条件下实验表明具有较好的性能。     

Development and evaluation of a Web 2.0 annotation system as a learning tool in an e-learning environment

The emergence of Web 2.0 technology provides more opportunities to foster online communication and sharing in an e-learning environment. The purpose of this study was to develop a Web 2.0 annotation system, MyNote, based on the Web 2.0 core concepts which emphasize ease of access and active sharing and then to gain an understanding about people’s perceptions of MyNote from a usability perspective. MyNote was employed on multimedia learning objects in a Learning Management System (LMS), and out of the LMS as well in this study. The evaluation results showed that, with factor analysis, interactivity, usefulness, helpfulness, and willingness for future use were categorized to represent the perceptions of MyNote. It was also found that the factors of interactivity and helpfulness were statistically significant to predict the future use of MyNote. Lastly, the habit of taking notes also affected learners’ perceptions of using MyNote.     

2D Correlative-Chain Conditional Random Fields for Semantic Annotation of Web Objects

Semantic annotation of Web objects is a key problem for Web information extraction. The Web contains an abundance of useful semi-structured information about real world objects, and the empirical study shows that strong two-dimensional sequence characteristics and correlative characteristics exist for Web information about objects of the same type across different Web sites. Conditional Random Fields (CRFs) are the state-of-the-art approaches taking the sequence characteristics to do better labeling. However, as the appearance of correlative characteristics between Web object elements, previous CRFs have their limitations for semantic annotation of Web objects and cannot deal with the long distance dependencies between Web object elements efficiently. To better incorporate the long distance dependencies, on one hand, this paper describes long distance dependencies by correlative edges, which are built by making good use of structured information and the characteristics of records from external databases; and on the other hand, this paper presents a two-dimensional Correlative-Chain Conditional Random Fields (2DCC-CRFs) to do semantic annotation of Web objects. This approach extends a classic model, two-dimensional Conditional Random Fields (2DCRFs), by adding correlative edges. Experimental results using a large number of real-world data collected from diverse domains show that the proposed approach can significantly improve the semantic annotation accuracy of Web objects.     

基于Web对象流行度的PPM预测模型

Web预取技术是减少网络延迟，提高服务质量的主要解决方案之一．利用Zipf第一法则和第二法则分别对Web高频区对象和低频区对象建立访问流行度模型，进而提出一种基于Web对象流行度的PPM预测模型,实验表明，该模型除继承了传统PPM模型简单易实现的特点外，在缩减模型规模的同时预测精度也有一定程度的提高，并且控制了由预取引起的网络流量．     

Using current web page structure to improve prefetching performance

Web prefetching is a technique aimed at reducing user-perceived latencies in the World Wide Web. The spatial locality shown by user accesses makes it possible to predict future accesses from the previous ones. A prefetching engine uses these predictions to prefetch web objects before the user demands them. The existing prediction algorithms achieved an acceptable performance when they were proposed but the high increase in the number of embedded objects per page has reduced their effectiveness in the current web. In this paper, we show that most of the predictions made by the existing algorithms are not useful to reduce the user-perceived latency because these algorithms do not take into account the structure of the current web pages, i.e., an HTML object with several embedded objects. Thus, they predict the accesses to the embedded objects in an HTML after reading the HTML itself. For this reason, the prediction is not made early enough to prefetch the objects and, therefore, there is no latency reduction. In this paper we present the double dependency graph (DDG) algorithm that distinguishes between container objects (HTML) and embedded objects to create a new prediction model according to the structure of the current web. Results show that, for the same number of extra requests to the server, DDG reduces the perceived latency, on average, 40% more than the existing algorithms. Moreover, DDG distributes latency reductions more homogeneously among users.     

基于概念格和关联规则Web个人化系统

最近的一些研究提出将Web使用日志的挖掘技术应用于Web个人化系统中,用于克服传统个人化技术（如CF技术、基于内容的过滤技术）中存在的问题,如处理大数据量的能力较差,依赖于用户主观的登记信息,产生的用户描述是静态的,不能获取对象之间丰富的语义联系等。但是基于Web使用日志挖掘的个人化技术不能适用于用户的使用信息获取困难或者站点内容经常变化的情况。更有效的办法是将站点的内容特征和使用特征结合到一个Web挖掘结构中去,以备推荐引擎统一使用。提出了一个基于关联规则挖掘的个人化系统,它使用概念格作为存储频繁页面集的数据结构,并介绍了如何利用概念格实时地为当前活动用户产生推荐集。     

Meme media architectures for re-editing and redistributing intellectual assets over the Web

While current Web technologies have allowed us to publish intellectual assets in world-wide repositories, and to browse the resulting massive accumulation, we have no effective tools yet to flexibly re-edit and redistribute such intellectual assets for their reuse in different contexts. Open Hypermedia Systems addressed the problem of augmenting third-party applications in 90 s, and more recently Web augmentation. We need extended OHS technologies for the advanced reuse of Web-published intellectual assets through re-editing and redistributing them. Meme media and meme pool technologies will work as such extended Open Hypermedia Systems technologies to annotate, re-edit, and redistribute Web-published assets. This paper reviews the IntelligentPad and IntelligentBox meme media architectures together with their potential applications, and proposes both the use of XML/XSL or XHTML to define two-dimensional meme media objects. When applied to Web contents, meme media technologies make the World Wide Web operate as a meme pool, where people can publish their intellectual assets as Web pages, access some Web pages to extract some of their parts as meme media objects through drag-and-drop operations, visually combine these meme media objects together with other meme media objects to compose new intellectual assets, and publish these assets again as Web pages. Our framework creates a new vista in the circulation and reuse of our knowledge represented as multimedia documents and/or application programs, especially in the field of science.     

A layer-wise deep stacking model for social image popularity prediction

World Wide Web - In this paper, we present a Layer-wise Deep Stacking (LDS) model to predict the popularity of Flickr-like social posts. LDS stacks multiple regression models in multiple layers,...     

A logistic regression model for Semantic Web service matchmaking

Semantic Web service matchmaking,as one of the most challenging problems in Semantic Web services (SWS),aims to filter and rank a set of services with respect to a service query by using a certain matching strategy.In this paper,we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration (e.g.,the weighted sum) for SWS matchmaking.The logistic regression model is trained on training data derived from binary relevance assessments of existing test collections,and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching strategies.Services are then ranked according to the probabilities of relevance with respect to each query.Our method is evaluated on two main test collections,SAWSDL-TC2 and Jena Geography Dataset(JGD).Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service,and hence can improve the effectiveness of service matchmaking.     

基于.NET的Word报告的设计与实现

在Web应用系统中经常会遇到文本处理的问题,如何在企业的Web管理系统中应用Word文档是系统用户关心的问题。通过在对Word对象的内容与格式及图表对象进行控制,提出了一种动态实现Word报告生成与下载的方法。     

基于．NET的Word报告的设计与实现

在Web应用系统中经常会遇到文本处理的问题,如何在企业的Web管理系统中应用Word文档是系统用户关心的问题。通过在对Word对象的内容与格式及图表对象进行控制,提出了一种动态实现Word报告生成与下栽的方法。     

An MAP-based Poisson cluster model for Web traffic

In our Web traffic model, http session requests occur according to a Poisson process and each request induces multiple transmissions of sub-documents, called objects. We use a transient MAP process to model the epochs at which the transmissions of objects associated to a given document are initiated. The lengths of transmission of the objects are modeled as i.i.d. random variables. We analyze the distribution of the total number of objects being in transmission at any given time and determine its auto-correlation function.     

Using bundles for Web content delivery

The protocols used by the majority of Web transactions are HTTP/1.0 and HTTP/1.1. HTTP/1.0 is typically used with multiple concurrent connections between client and server during the process of Web page retrieval. This approach is inefficient because of the overhead of setting up and tearing down many TCP connections and because of the load imposed on servers and routers. HTTP/1.1 attempts to solve these problems through the use of persistent connections and pipelined requests, but there is inconsistent support for persistent connections, particularly with pipelining, from Web servers, user agents, and intermediaries. In addition, the use of persistent connections in HTTP/1.1 creates the problem of non-deterministic connection duration. Web browsers continue to open multiple concurrent TCP connections to the same server. This paper examines the idea of packaging the set of objects embedded on a Web page into a single bundle object for retrieval by clients. Based on measurements from popular Web sites and an implementation of the bundle mechanism, we show that if embedded objects on a Web page are delivered to clients as a single bundle, the response time experienced by clients is better than that provided by currently deployed mechanisms. Our results indicate that the use of bundles provides shorter overall download times and reduced average object delays as compared to HTTP/1.0 and HTTP/1.1. This approach also reduces the load on the network and servers. Implementation of the mechanism requires no changes to the HTTP protocol.     

Web对象流行度的数学建模方法

Web对象的流行度满足Zipf分布。利用Zipf第一定律和Zipf第二定律建立Web对象访问流行度模型。对于访问频率较高的Web对象，利用Zipf第一定律建模；而对于访问频率较低的Web对象，则使用Zipf第二定律进行描述。该模型为分析和模拟Web用户访问行为提供了依据。     

Reinforcing Web-object Categorization Through Interrelationships

Existing categorization algorithms deal with homogeneous Web objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspect of the inter-object relationship is not sufficient to fully reveal the true categories of Web objects. In this paper, we propose a novel categorization algorithm, called the Iterative Reinforcement Categorization Algorithm (IRC), to exploit the full interrelationship between different types of Web objects on the Web, including Web pages and queries. IRC classifies the interrelated Web objects by iteratively reinforcing the individual classification results of different types of objects via their interrelationship. Experiments on a clickthrough-log dataset from the MSN search engine show that, in terms of the F₁ measure, IRC achieves a 26.4% improvement over a pure content-based classification method. It also achieves a 21% improvement over a query-metadata-based method, as well as a 16.4% improvement on F₁ measure over the well-known virtual document-based method. Our experiments show that IRC converges fast enough to be applicable to real world applications.     

Enabling technologies for e-business

While distributed object concepts can be difficult to grasp at first, applying them to build reusable components eventually leads to faster time to market. And now a host of enabling technologies is available, making it easier to use distributed objects for Web based e-business projects. These enabling technologies provide the technical underpinnings for Web-enabled object architectures. Web-enabled object architectures integrate the power of objects with the Web's distribution capabilities. But we need special tools to help minimize the complexities of using distributed object technologies. The advent of application servers and the benefits of distributed object technologies remove the bounds that previously restricted the Web. Companies can use these new technologies to embrace an expanded scope of business that will in turn continue to generate further requirements for new technology     

用ASP实现对Web数据库的操作

利用ASP的内置对象及内建控件实现对Web数据库的操作，真正体现了ASP服务器端动态网页的实质。文中论述了用ASP访问Web数据库的步骤，详细阐述了对数据库的各种操纵方法，并以实例说明了ASP在浏览器与服务器端数据库之间的桥梁功能。