基于位置的手机博客系统设计与实现

随着移动产业发展和移动技术提高,基于用户位置的业务迅速发展,如:紧急援助、信息查询等,基于位置业务创新已经成为移动产业发展的巨大推动力.文中在ISG平台上设计和实现基于位置的手机博客系统.与传统的手机博客系统相比较,文中引入用户位置信息.用户写博客时,系统自动记录用户的位置信息,并把用户位置与其所写博客动态绑定存储;用户可以根据自己的位置动态搜索博客.     

手机学校电子地图软件的研究与实现

随着移动产业发展和移动技术的提高,基于位置的服务迅速发展,如交通导航、生活信息查询等等。基于位置业务创新已经成为移动产业发展的巨大推动力。但是目前手机地图所含信息量在某些方面缺少针对性,难以满足人们日益增大的信息需求。文中在iOS手机系统下设计和实现了基于位置的校内通系统,针对与在校大学生这个特殊的群体设计的软件,能充分满足广大学生的需求,通过该软件学生能快速方便地获取地理信息,不仅包括食堂、寝室、教学楼等所在的位置,还能获取食堂今天有什么优惠活动、体育馆开放时间等等和学生生活息息相关的信息。     

旅行信息服务系统结构设计

目前，很多手机公司提供与位置相关的信息用于住置服务（LBS），但尚无一个系统能同时提供如下四种信息：用户当前位置、当前时间、用户资料、用户过程。文中的旅行信息提供（TIP）系统把所有这些基于移动设备，各种数据库和事件通知服务的功能整合到一个单一的框架中，为以后的LBS系统的开发提供一个可供参考的模型。     

手机动漫运营系统研究与设计

近年来,中国移动互联网市场正在经历快速发展阶段,随着网络速度和智能手机普及率不断提升,用户对移动互联网服务消费习惯逐渐形成.同时,在移动互联网时代的背景下,中国动漫产业得到了飞速发展,作为移动互联网与动漫产业相结合的手机动漫成为了动漫产业发展的最新方向.文章提出了一种手机动漫运营系统的设计和建设方法,并详细该系统的体系架构、设计和实现方法.系统投入运行后,为广大手机动漫的作品创作者与庞大的移动终端手机动漫用户之间建立了统一的手机动漫运营服务平台,为手机动漫系统的可持续化运营提供了系统保障,进而推进了数字动漫特别是手机动漫在我国的发展.     

基于混合相似度和信任传播的位置推荐系统

当前存在大量基于位置推荐的移动社交应用。提出一种基于CF算法的混合相似度和信任传播位置推荐系统的方案。方案中主要分三个考量要素,首先将用户偏好分为用户静态偏好和用户动态偏好,对于用户静态偏好主要是基于位置的种类信息和历史评价,而用户动态偏好主要是基于地理信息和二位云模型,最后用户的社会关系是基于信任传播的信息。该方法优势是不仅考虑用户偏好的多样性,而且通过信任传播可以有效缓解数据稀疏性问题。并应用Hadoop以提高计算平台的数据处理能力。该方案对比现有方法,基于CF算法的位置推荐预测用户对新位置的喜好更加准确且高效。     

城市公交实时位置手机查询系统设计

针对用户选择公交出行时不能准确获知公交车实时位置等信息的问题,对移动互联网在智能交通中的应用进行了探讨。使用百度地图接口技术设计一种基于移动客户端公交实时位置查询系统,并给出了经纬度数据在电子地图中发生偏移的修正方法。用户使用手机或PDA等网络设备进入此查询系统界面,就能随时查询特定线路上公交车辆的实时位置、数量等信息。经测试,该系统具有一定的可行性。     

基于J2ME手机矢量地图及定位技术的应用研究

基于定位的服务（LBS）是指为移动设备的用户提供一系列个性化的与位置相关的服务。文章着眼于J2ME技术的应用，开发一项向无线用户提供的增值业务——手机地图，对手机地图实现矢量地图缩放、地理信息等功能进行分析和研究。     

云网销售排行榜

现在不少朋友在网上做起了博客，把自己的心事，情感，工作写给自己也分享给别人。然而当你突发灵感或者刚用手机拍到一幅绝好的照片想发布在Blog中怎么办？中国博客网推出的“移动博客”手机日志这时就派上用场了，它可以让你在手机上完成日志或将拍摄的照片随时上传。移动博客。简称为MBlog，用户只要将自己的手机与博客账号绑定，便可随时随地通过手机发表日志，上传手机图片，就像发短信一样简单，开通移动博客的用户现可免费使用以下手机博客功能，写日志，传图片，看日志，精品图片等。     

支持多维标签云的移动餐厅推荐系统仿真研究

实现了一个可提供基于位置的移动餐厅推荐及导航服务的系统。结合最新的Web2.0应用数据,实现了多模式的餐厅信息推送服务,包括基于模糊条件约束的查询机制,基于位置的地图浏览模式和基于三维标签云的协同信息共享机制,为用户提供实时的基于位置的餐厅个性推荐及导航。同时,针对移动手机屏幕较小,显示内容受限的问题,研究并提出了一个“面向移动终端的多维标签云可视化方法”,并实现了一种标签云关联分析和聚类算法,实现了基于用户偏好的快速导航。     

基于Android手机的人员定位与管理系统

提出来一种基于Android手机系统和Windows平台的人员定位与管理系统。该系统使用Android手机采集人员的位置和通信信息,通过手机GPRS网络将信息传送给服务器。管理者通过浏览程序访问服务器,获取人员的位置信息及与特定人员进行通信。系统是使用手机移动设备对人员进行管理的初步尝试,并且能建立高效的人员管理程序。     

Personalized Semantic Based Blog Retrieval

Blog retrieval is a complex task because of the informal language usage.Blogs deviate from the language which is used in traditional corpora largely due to various reasons.Spelling errors,grammatical irregularity,over use of abbreviations and symbolic characters like emotions are a few reasons of irregular corpus blogs.To make the retrieval of blogs easier,the novel idea of personalized semantic based blog retrieval(PSBBR) system is discussed in this paper.The blogs are tagged with a relationship to one another with reference to ontology.The meanings of the blog content and key term are tagged as XML tags.The query term accesses the XML tags to retrieve entire blog content.The system is evaluated with a huge number of blogs extracted from various blog sources.Relevance score is calculated for every blog associated with     

Personalized blog content recommender system for mobile phone users

Compared to newspaper columnists and broadcast media commentators, bloggers do not have organizations actively promoting their content to users; instead, they rely on word-of-mouth or casual visits by web surfers. We believe the WAP Push service feature of mobile phones can help bridge the gap between internet and mobile services, and expand the number of potential blog readers. Since mobile phone screen size is very limited, content providers must be familiar with individual user preferences in order to recommend content that matches narrowly defined personal interests. To help identify popular blog topics, we have created (a) an information retrieval process that clusters blogs into groups based on keyword analyses, and (b) a mobile content recommender system (M-CRS) for calculating user preferences for new blog documents. Here we describe results from a case study involving 20,000 mobile phone users in which we examined the effects of personalized content recommendations. Browsing habits and user histories were recorded and analyzed to determine individual preferences for making content recommendations via the WAP Push feature. The evaluation results of our recommender system indicate significant increases in both blog-related push service click rates and user time spent reading personalized web pages. The process used in this study supports accurate recommendations of personalized mobile content according to user interests. This approach can be applied to other embedded systems with device limitations, since document subject lines are elaborated and more attractive to intended users.     

Mobile service oriented architectures for NN-queries

The ever-increasing number of mobile device users has also raised the users’ expectation of mobile services accordingly. This phenomenon has given pressures to the mobile service providers to improve their services in order to stay competitive in the market. The service oriented approach is seen to be a promising scheme for mobile services. This paper presents mobile service oriented architectures for Nearest-Neighbor (NN) queries that are classified into five categories, namely (i) intermittent query mobile services, (ii) continuous query mobile services, (iii) context-aware mobile services, (iv) continuous moving object query mobile services, and (v) data broadcast mobile services. These services incorporate query, location and context-aware services, ontological context model, and broadcast. The proposed architectures are concerned with mobile services for clients on the move requesting services based on their current location, which is arguably the most important feature in a wireless environment. Furthermore, we also discuss the Quality-of-Service (QoS) requirement for mobile services in which request latency time is one of the most important parameters to consider. Some analytical models for query latency measurement are presented and the results are compared with the simulation experiments.     

Design and implementation of Blog Rendering and Accessing INstantly system (BRAINS)

A blog (shortened from ‘weblog’) is a trendy way to share personal journal with others in the cyber world. Traditionally rendering and accessing blogs are normally conducted at a stationary PC. However, such a scheme hinders blog users from writing and reading blogs timely. A short-lived idea came out and passed away suddenly. To facilitate the instant blog updating and retrieving, we combined cellular messaging (SMS/MMS messaging and MMS/WAP push) and open-source software (Blosxom and Apache) to develop a novel system for the Blog Rendering and Accessing INstantly (BRAINS). BRAINS enables blog users to note down their whims and share interests anytime and anywhere. Blog journalists can utilize their mobile phones in hand to compose and deliver blogs to BRAINS by SMS and MMS messaging at their pleasure. Through pre-registering on BRAINS, readers also can get the up-to-date blogs content or the incoming blog notification immediately by MMS push or WAP push. With pervasive networks like the GPRS, 3G, public WLAN (PWLAN) and fixed DSL networks, BRAINS makes mobile blog rendering and accessing more evident.     

Personalized recommendation of popular blog articles for mobile applications

Weblogs have emerged as a new communication and publication medium on the Internet for diffusing the latest useful information. Providing value-added mobile services, such as blog articles, is increasingly important to attract mobile users to mobile commerce, in order to benefit from the proliferation and convenience of using mobile devices to receive information any time and anywhere. However, there are a tremendous number of blog articles, and mobile users generally have difficulty in browsing weblogs owing to the limitations of mobile devices. Accordingly, providing mobile users with blog articles that suit their particular interests is an important issue. Very little research, however, has focused on this issue.In this work, we propose a novel Customized Content Service on a mobile device (m-CCS) to filter and push blog articles to mobile users. The m-CCS includes a novel forecasting approach to predict the latest popular blog topics based on the trend of time-sensitive popularity of weblogs. Mobile users may, however, have different interests regarding the latest popular blog topics. Thus, the m-CCS further analyzes the mobile users’ browsing logs to determine their interests, which are then combined with the latest popular blog topics to derive their preferred blog topics and articles. A novel hybrid approach is proposed to recommend blog articles by integrating personalized popularity of topic clusters, item-based collaborative filtering (CF) and attention degree (click times) of blog articles. The experiment result demonstrates that the m-CCS system can effectively recommend mobile users’ desired blog articles with respect to both popularity and personal interests.     

基于位置服务中的连续查询隐私保护研究

近年来,伴随着移动计算技术和无限设备的蓬勃发展,位置服务中的隐私保护研究受到了学术界的广泛关注,提出了很多匿名算法以保护移动用户的隐私信息.但是现有方法均针对snapshot查询,不能适用于连续查询.如果将现有的静态匿名算法直接应用于连续查询,将会产生隐私泄露、匿名服务器工作代价大等问题.针对这些问题,提出了δp-隐私模型和δq-质量模型来均衡隐私保护与服务质量的矛盾,并基于此提出了一种贪心匿名算法.该算法不仅适用于snapshot查询,也适用于连续查询.实验结果证明了算法的有效性.     

Exploring the Relationship between Keywords and Feed Elements in Blog Post Search

Blogs are increasingly accepted as a useful means to proliferate a variety of information on the web. As the popularity of blogs grows rapidly, a number of blog search engines have appeared recently to help users access and discover blog posts efficiently. Nevertheless, existing approaches tend to focus on ranking the blog posts according to their recency or popularity only, leaving the problem of retrieving more topic relevant posts to a user’s query largely unexplored. In this paper, we present a novel blog ranking framework, called PTRank, that improves search quality by taking account of relevance feedback from users as well as various information available from RSS feeds. A neural network method is employed to learn ranking functions that provide a relevance score between a keyword and a blog post. Extensive experiments on real blog data have been conducted to validate the proposed ranking framework for blog post search, and the results indicate that PTRank performs significantly better than the existing popular approach.     

An effective coherence measure to determine topical consistency in user-generated content

When searching for blogs on a specific topic, information seekers prefer blogs that place a central focus on that topic over blogs whose mention of the topic is diffuse or incidental. In order to present users with better blog feed search results, we developed a measure of topical consistency that is able to capture whether or not a blog is topically focused. The measure, called the coherence score, is inspired by the genetics literature and captures the tightness of the clustering structure of a data set relative to a background collection. In a set of experiments on synthetic data, the coherence score is shown to provide a faithful reflection of topic clustering structure. The properties that make the coherence score more appropriate than lexical cohesion, a common measure of topical structure, are discussed. Retrieval experiments show that integrating the coherence score as a prior in a language modeling-based approach to blog feed search improves retrieval effectiveness. The coherence score must, however, be used judiciously in order to avoid boosting the ranking of irrelevant but topically focused blogs. To this end, we experiment with a series of weighting schemes that adjust the contribution of the coherence score according to the relevance of a blog to the user query. An appropriate weighting scheme is able to improve retrieval performance. Finally, we show that the coherence score can be reliably estimated with a sample exceeding 20 posts in size. Consistent with this finding, experiments show that the best retrieval performance is achieved if coherence scores are used when a blog contains more than 20 posts.     

A telematics service discovery platform using the distributed peer‐to‐peer networking system

In this paper, a distributed telematics peer‐to‐peer networking system is proposed to provide an efficient and feasible service discovery mechanism for mobile users. When mobile users travel on the road, they may request for some services from service providers to meet their demands. Mobile users in vehicles are assumed to go through a lot of regions, in which each region is associated with a region server. Related information of all service providers in a region is stored in its region server, and each region server is in charge of the service discovery requests from all vehicles located in its region. This paper focuses on the issues of developing a distributed peer‐to‐peer telematics service discovery networking system over the vehicular network environment for mobile users. To provide the communication mechanism for telematics service discovery among region server, mobile users, and service providers, the Telematics Service Markup Language is proposed. According to clients' demands, three types of query scenarios are proposed, which are (1) reference point based query, (2) continual query, and (3) route‐based query. Finally, we present usage examples and system implementation of distributed telematics peer‐to‐peer. Copyright © 2012 John Wiley & Sons, Ltd.     

A Query Routing Approach Based on Users’ Satisfaction for Resource Discovery in Service-Oriented Networks

Service oriented networks are distributed computing infrastructures that provide widely distributed resources. These networks are dynamic and their size and complexity continue to increase and allow to users a ubiquitous access to available resources and services. Therefore, efficient query routing approaches in large and highly distributed service oriented networks are required and need to be adaptive in order to cope with a dynamically changing environment. In this paper, a query routing approach based on mobile agents and random walks with a reinforcement learning technique is presented. By enhancing random walks with a reinforcement learning mechanism centered on users’ satisfaction, this approach allows dynamic and self-adaptive location of required resources. Peers incorporate knowledge from past and present queries which will be used during next searches by mobile agents to select their next hops. This approach is analyzed through two query routing techniques using the network simulator ns2.