将新闻放在口袋中——利用PocketRSS进行随身浏览

在当今,互联网无疑有着最丰富,最及时的新闻与资源,对我这种IT资讯人来说是恨不得一天从头到晚都将电脑屏幕放在眼前,搜索、浏览各种新闻与资料;即使是对于普通用户,如果能随时看到网上新闻,无疑也比报纸及时和全面。但很明显,我们不可能一天到晚背一个笔记本,更不可能在路上、在车上……随时都打开笔记本来看新闻。好在随着技术的发展,无数不可能都变为了现实,今天我们要向大家介绍的就是:如何将新闻放在口袋中,做到随时随地的“掌中看天下”。在具体介绍之前,我们需要先来了解一下目前互联网上最为热门的RSS新闻推送技术。RSS是一种用于共享新闻和其他Web内容的数据交换规范,网络用户可以在客户端借助于支持RSS的新闻聚合工具软件,如《看天下》、《博阅》,在不打开网站内容页面的情况下阅读支持RSS的网站内容。RSS起源于网景通讯公司的推"Push"技术,在网景通讯还是浏览器老大的时代,它定义了一套描述新闻频道的语言,这就是RSS,只不过后来随着Netscape浏览器的每况愈下,最终也没能发布一个正式的RSS规范(只发布了一个0.9版本)。而微软也在当时推出了支持自己IE4.0的CDF(ChannelDefinitionFormat)数据规格,与RSS非常接近。微软试图用IE4.0的新闻频道功能把“推”(Push)技术变成一个应用主流,并与Netscape抗衡。不过出人意料的是,“推”技术自始至终没有找到合适的商业模型,而且伴随着其它各类网络特性的出现,自身的优势无法显现,新闻频道最终在IE的后续版本中消失了。但是RSS并没有被业界人士所抛弃。过去两年,Blog兴起了,它从一个专业群体开始,逐步成为了网络上最热门的新话题。而RSS则是描述Blog主题和更新信息的最基本方法,随着Blog的兴起,RSS再次复兴,成为了今日互联网上最为流行的新闻阅读技术,它在国内也有着众多的支持者,主流的资讯网站包括各大论坛,都已经把它作为必备的技术,这也为我们今天利用RSS浏览新闻和资讯打下了坚实的基础。我们今天的实施方案是在PPC掌上电脑上安装支持RSS新闻推送的PocketRSS软件,然后将PPC与手机蓝牙连接,再利用手机GPRS带PPC上网,我们就可以随时阅尽天下新闻。而且这里我们是利用手机的WAPGPRS包月服务,每月不限时、不限流量,依地方不同,收费仅10-20元。当然如果读者使用的是PPC智能手机的话,那就更可一步到位,仅使用PPC智能手机就可以阅读RSS新闻。     

RSS有前途吗？

在这个信息爆炸的年代,上互联网看新闻已成为人们的一种生活习惯。随着互联网的大规模普及,以及互联网应用的空前丰富,看新闻的方式也不再单一,与到新闻门户网站尽知天下事相比,RSS便可以称为一种更具针对性的新闻获取方式。     

Internet下的个性化RSS新闻订阅系统

在网络信息传播高速发展的今天,一种全新的新闻传播方式已经走到了我们的身边,它就是RSS新闻订阅.使用RSS新闻订阅系统的人们不再需要花费大量的时间冲浪和从新闻网站下载,就能在更短的时间里阅览更多的新闻.提出了新闻订阅系统的设计方案,并利用OPML技术为用户提供方便的个性化服务.     

基于RSS技术的新闻发布系统研究

该文提出的基于RSS技术的新闻发布系统不仅包括一般的新闻载入,浏览,检索和栏目管理功能,还可以根据新闻模板自动生产新闻文件,发表新闻评论,使用关键字搜索并显示相关新闻,使用RSS阅读新闻等。本系统能够节省新闻发布的成本,提高新闻发布的效率,体现新闻的时效性,因此具有很高的实用价值。     

Foxmail也玩RSS新闻

一般情况下，要想订阅RSS新闻就得安装一个RSS阅读器。而现在，Foxmail 6已经集成了这项非常实用的功能，让我们看新闻，收邮件两不误。[编者按]     

RSS“魔法按钮”全攻略

随着互联网巨量信息的更新与堆积，RSS的精准订阅功能日渐成为大众网民汲取信息的重要手段。简单讲，RSS就是一种用来分发和汇集网页内容的XML格式。它能让我们不再需要逐个网站，逐个网页的寻找资讯。只要将所需要的内容订阅在一个RSS阅读器中，这些内容即会自动出现阅读器皇。一旦网络上出现资讯的更新，RSS便会自动下载这些资讯，确保收录订户所关注的所有信息而不被遗漏。目前RSS的主要功用集中在两大领域，第一，各大新闻网站的订阅应用；第二，网络博客的更新订阅。     

基于ASP．NET的RSS新闻聚合阅读器的设计与实现

RSS是一种用于共享新闻标题和其他Web内容的XML格式标准,在互联网上应用非常广泛。本文结合ASP．NET和XML技术,开发一个在线的RSS新闻聚合阅读器,并对阅读器的结构设计和实现的关键技术进行阐述。     

开放的管道Syndication聚合

有人讲，RSS就是Blog内容的订阅服务；也有人说，RSS是源于新闻聚合，自然与发布关联：还有人说RSS是互联网的管道，将互联网上开放的应用聚合在一起。事实上，RSS英文缩写有两种说法，一种是“Rich Site Summary”或“RDF Site Summary”，另一种是“Really Simple Syndication”，其实从功能看，RSS就是一个发布和聚合，她本质特性是开放、共享、聚合的工具，RSS天然地与发布、订阅有着密切关联。     

基于Silverlight开发的RSS聚合系统的设计与实现

RSS是一种简易的XML格式,用以发布经常更新内容的信息,在互联网上得到了广泛的应用。通过对RSS应用特点的分析以及RSS技术的研究,该文主要讨论了RSS个性化聚合系统的设计思路和实现方法。该RSS聚合系统采用目前较为流行的Sil-verlight富互联网应用技术,并实现了RSS在线阅读器;站内外RSS资源的订阅、收藏、分享;站内RSS资源搜索以及为不提供RSS输出的站点创建RSS Feed等一系列功能。     

RSS订阅 手机PC齐步走

很多人都用RSS订阅网站和论坛的最新消息,以获取关注资讯的最新动态。而对更多人来说,资讯看一次就好,可能上班时希望在电脑前获取资讯信息,而在上下班途中打开手机就能收看之前在电脑上未看完的最新资讯。不过往往只能在电脑上或手机上利用软件分别订阅查看,而无法实现电脑和手机轮流随时看。其实,利用电脑和智能手机端软件,再加上给力的RSS网络订阅服务,即可实现RSS订阅手机PC齐步走,上班下班可以随时查看订阅的信息,下班前未看完的新闻,也可以在下班途中打开手机继续看了,非常方便。要实现RSS订阅,手机PC齐步走,在电脑平台上要用到傲游浏览器和FeedDeamon两个软件,同时注册Goolge账号并开通拥有Google Reader阅读器的RSS服务,在手机上安装大牛新闻客户端,既可方便地实现RSS订阅PC和智能手机互通,也可最大限度解决手机阅读RSS访问移动互联网造成的巨额话费问题。     

Using maximal spanning trees and word similarity to generate hierarchical clusters of non-redundant RSS news articles

RSS news articles that are either partially or completely duplicated in content are easily found on the Internet these days, which require Web users to sort through the articles to identify non-redundant information. This manual-filtering process is time-consuming and tedious. In this paper, we present a new filtering and clustering approach, called FICUS, which starts with identifying and eliminating redundant RSS news articles using a fuzzy set information retrieval approach and then clusters the remaining non-redundant RSS news articles according to their degrees of resemblance. FICUS uses a tree hierarchy to organize clusters of RSS news articles. The contents of the respective clusters are captured by the representative keywords from RSS news articles in the clusters so that searching and retrieval of similar RSS news articles is fast and efficient. FICUS is simple, since it uses the pre-defined word-correlation factors to determine related (words in) RSS news articles and filter redundant ones, and is supported by well-known and yet simple mathematical models, such as the standard deviation, vector space model, and probability theory, to generate clusters of non-redundant RSS news articles. Experiments performed on (test sets of) RSS news articles on various topics, which were downloaded from different online sources, verify the accuracy of FICUS on eliminating redundant RSS news articles, clustering similar RSS news articles together, and segregating different RSS news articles in terms of their?contents. In addition, further empirical studies show that FICUS outperforms well-known approaches adopted for clustering RSS news articles.     

基于新闻类RSS文档的信息隐藏系统

分析新闻类聚合内容(RSS)文档的结构,提出一种基于新闻类RSS文档的信息隐藏方法。根据各新闻条目的先后顺序不会影响RSS文档使用的特点,利用标签模块的排列组合隐藏机密信息。同时选取另外2种基于XML的隐藏方法进行合理组合,构建一个基于多种隐藏方法的新闻类RSS文档的信息隐藏系统。实验结果表明,排序后的3种隐藏方法不会发生冲突,在提高隐藏容量的同时,具有较好的隐蔽性和鲁棒性。     

Efficient Monitoring Algorithm for Fast News Alerts

Recently, there has been a dramatic increase in the use of XML data to deliver information over the Web. Personal Weblogs, news Web sites, and discussion forums are now publishing RSS feeds for their subscribers to retrieve new postings. As the popularity of personal Weblogs and RSS feeds grows rapidly, RSS aggregation services and blog search engines have appeared, which try to provide a central access point for simpler access and discovery of new content from a large number of diverse RSS sources. In this paper, we study how the RSS aggregation services should monitor the data sources to retrieve new content quickly using minimal resources and to provide its subscribers with fast news alerts. We believe that the change characteristics of RSS sources and the general user access behavior pose distinct requirements that make this task significantly different from the traditional index refresh problem for Web search engines. Our studies on a collection of 10,000 RSS feeds reveal some general characteristics of the RSS feeds and show that, with proper resource allocation and scheduling, the RSS aggregator provides news alerts significantly faster than the best existing approach.     

使用C#语言在ASP.NET网站中集成RSS新闻

分析了RSS新闻的传输原理,通过实例,讲述了使用Visual Studio2010+C#语言在ASP.NET网站的页面中获取远程RSS新闻数据,以及使用GridView控件分页显示新闻列表、查看新闻的方法。     

基于3G无线新闻阅读器开发

随着3G网络的建成运行和智能手机的普及,基于智能移动设备的无线互联网应用的需求将越来越大。无线新闻阅读器是基于SymbianS60平台下开发的。传输过程中完全采用XML格式,并使用RSS订阅方式进行新闻推送功能的实现。用户可以订阅新闻频道,并对其进行的增,删,改,更新和新闻内容的存储。     

RSS – MORE THAN JUST NEWS FEEDS

RSS (Really Simple Syndication/RDF Site Summary) is a simple lightweight format that was originally used for news alerts and content syndication. It is now being used in a wide range of applications. This paper describes the background to RSS and describes the various ways in which RSS can be used. Examples are provided of the ways in which RSS can be viewed and the variety of approaches for creating RSS. The paper argues that, although there is still confusion over the different types of RSS standards, the format is an essential for use in digital library services.     

RSS-based e-learning recommendations exploiting fuzzy FCA for Knowledge Modeling

Nowadays, Web 2.0 focuses on user generated content, data sharing and collaboration activities. Formats like Really Simple Syndication (RSS) provide structured Web information, display changes in summary form and stay updated about news headlines of interest. This trend has also affected the e-learning domain, where RSS feeds demand for dynamic learning activities, enabling learners and teachers to access to new blog posts, to keep track of new shared media, to consult Learning Objects which meet their needs.This paper presents an approach to enrich personalized e-learning experiences with user-generated content, through a contextualized RSS-feeds fruition. The synergic exploitation of Knowledge Modeling and Formal Concept Analysis techniques enables the design and development of a system that supports learners in their learning activities by collecting, conceptualizing, classifying and providing updated information on specific topics coming from relevant information sources. An agent-based layer supervises the extraction and filtering of RSS feeds whose topics cover a specific educational domain.     

Adaptation of RSS feeds based on the user profile and on the end device

In the last decade, the advances in technology along with the ease of access to information have dramatically changed the World Wide Web status during the last few years. The Internet acts as a means of finding useful information and more specifically news articles. Additionally, more and more people want to utilize their mobile devices towards the scope of reading news articles. The aforementioned situation generates a significant, yet almost untouched problem: easily locating interesting news articles on a daily basis within the space that is available on the small screen device. In our work, we propose a framework that, by utilizing RSS feeds, is able to personalize on the needs of the users and on the capabilities of their device, in order to present to them only a fraction of the news articles and merely the useful information that derives from them. Deploying a generalized, multi-functional mechanism that produces efficient results for the situation described, seems to be a panacea for most of the text-based, information retrieval needs. Within this framework we created PeRSSonal, a mechanism that is able to create personalized, pre-categorized, dynamically generated RSS feeds focalized on the end user's small screen device. The system is based on algorithms that incorporate the user into the categorization and summarization procedures, while the articles are presented back to him/her according to her interests and the client device capacity.     

Multilingual news extraction via stopword language model scoring

Web news provides a quick and convenient means to create collections of large documents. The creation of a web news corpus has typically required the construction of a set of HTML parsing rules to identify content text. In general, these parsing rules are written manually and treat different web pages differently. We address this issue and propose a news content recognition algorithm that is language and layout independent. Our method first scans a given HTML document and roughly localizes a set of candidate news areas. Next, we apply a designed scoring function to rank the best content. To validate this approach, we evaluate the systems performance using 1092 items of multilingual web news data covering 17 global regions and 11 distinct languages. We compare these data with nine published content extraction systems using standard settings. The results of this empirical study show that our method outperforms the second-best approach (Boilerpipe) by 6.04 and 10.79 % with regard to the relative micro and macro F-measures, respectively. We also apply our system to monitor online RSS news distribution. It collected 0.4 million news articles from 200 RSS channels in 20 days. This sample quality test shows that our method achieved 93 % extraction accuracy for large news streams.     

A distributed full-text top-k document dissemination system in distributed hash tables

Recent years witnessed the explosive growth of ??live?? web content in the World Wide Web like Weblogs, RSS feeds, and real-time news, etc. The popular usage of RSS feeds/readers enables end users to subscribe for favorite contents via input RSS URLs. However, the RSS feeds/readers architecture suffers from (i) the high bandwidth consumption issue, and (ii) limited filtering semantics. In this paper, we proposed a stateful full text dissemination scheme over structured P2Ps to address both issues. Specifically, for the semantic side, end users are allowed to subscribe for favorite contents via input keywords; for the network bandwidth side, the cooperative content polling, filtering and disseminating via DHT-based P2P overlay networks save the network bandwidth consumption. Our contributions include the novel techniques to (i) reduce the unit-publishing cost by pruning irreverent documents during the forwarding path towards destinations, and (ii) reduce the publication amount by selecting a very small number of meaningful terms. Based on real data sets, our experimental results show that the proposed scheme can significantly reduce the publishing cost with low maintenance overhead and a high document quality.