基于RSS信息源的用户兴趣模型研究

互联网迅速发展,个性化信息服务成为研究的热点之一.在个性化信息服务的研究中,用户兴趣建模是核心问题之一.本文针对 RSS 标准的信息源,从用户兴趣知识获取、用户模型表示、用户模型学习、用户模型更新这四个方面论述了基于 RSS 的用户兴趣模型构建过程中的理论、方法和技术.     

基于QoS量化用户体验质量的评价模型

针对网络环境中业务供应商提供的业务不能很好的满足用户需求的问题,引入用户体验质量（QoE）并结合服务质量（QoS）参数,通过仿真网络得到相应数据,使用matlab工具分别建立用户体验质量与比特率、用户体验质量与丢包率的评价模型.运用统计分析方法对评价模型进行数据分析,并与史蒂文斯幂定律、韦伯-费希纳定律等模型进行对比验证.结果表明,构建的评价模型能更精确的体现用户体验质量与服务质量的关系,为供应商提供了重要的参考依据,并指明了服务方向.     

面向用户体验的云计算服务可信模型研究

随着云计算的快速发展,云中提供的服务日益丰富,越来越多的用户开始使用云计算提供的服务,如何为用户提供最有效、最可信的服务成为新的挑战.考虑到信任因素的复杂性与不确定性,本文主要研究如何面向用户体验对信任概念进行量化,将用户体验作为服务可信模型的重要评估标准和参考因素,提出了一种基于云计算的全局信任量化计算模型,对其计算方法进行了重点讲述,并将信任量化计算模型作为一个构件挂接于北京科技大学云计算平台之上,在为用户提供可信服务的同时获得有效的验证数据,提供量化的趋近真实的服务可信度.通过在实际系统中的应用,验证了该模型的有效性.     

用户个性化推荐系统的设计与实现

为实现个性化服务,理解用户兴趣就成了提供服务的关键任务,因此,提出了隐性采集用户浏览内容、用户浏览时间和用户操作时间的信息方法,通过对网络爬虫程序抓取的网页进行内容清洗提取出主要内容之后,利用VSM建立文档模型,并采用SVM分类方法建立推荐库.基于从客户端采集的用户兴趣信息建模,以及根据该模型和推荐库的相似度,给用户推荐信息.此外,给出了基于该模型的推荐原型系统的实现,使用查准率来评价该系统.试验结果表明,系统较好地实现了基于用户兴趣来推荐阅读的信息.     

一种Web服务QoS可信性评价模型

随着Web服务数量的增加,根据Web服务的非功能属性(QoS)度量Web服务质量成为研究热点之一.目前对Web服务QoS的研究主要集中在QoS信息管理、QoS度量方法及基于QoS驱动的服务选取上,但对于QoS评价的可信性研究较少.本文在目前QoS研究的基础上提出一个Web服务QoS可信性评价模型,并给出了两种Web服务QoS可信性评价方法.最后,描述了可信性评价的实验过程及效率分析.     

基于混合协同过滤的个性化Web服务推荐

随着网络上Web服务的不断增加,Web服务的个性化选择和推荐成为服务计算领域最重要的挑战之一。对个性化Web服务推荐方法进行了研究,提出了基于模型和基于内存混合的Web服务推荐方法。该方法基于客观连续的服务质量（quality of service,QoS）数据和主观离散的评价数据,采用聚类、映射、聚合等算法预测服务的质量,并对用户的期望、评分和服务的QoS信息进行了量化描述。此外,设计了Web服务推荐框架,实现了信息的采集与处理、Web服务的个性化推荐。实验结果表明,与主流的推荐算法相比,所提方法在多种评分误差的评价指标上都取得了更好的结果。     

Web信息检索结果个性化排序模型

本文讨论了如何从网页点击次数的统计数据中获得用户对网页中包含信息的评价.在考虑了网页内容,时间等因素对信息价值的影响后,给出了一种基于用户评价的对信息检索结果个性化排序的模型.模型根据用户浏览网页时的行为和用户的特征信息,预测用户对信息的需求,智能地对信息检索结果进行个性化的排序.模型实现简单,可以应用于多数信息检索系统,为用户提供个性化的信息服务.     

IT产品用户体验质量的模糊综合评价研究

提出了一种基于可用性测试的旨在对产品使用过程中的用户体验质量进行综合评价的模型。首先结合ISO9241-11有关可用性的定义,建立了包括人-机交互角度、用户角度和观察者角度的用户体验质量的360°指标体系模型;然后针对可用性概念的模糊性及其综合评估中的问题,应用层次分析法(AHP)确定了指标体系中相关因素的权重,并根据模糊隶属度函数对用户体验质量评价标准进行了模糊化处理;最后采用模糊评价方法对用户体验质量进行评价。实例表明用户体验质量的综合性评估模型可以有效地对用户体验质量进行综合性评价。     

基于情景感知的N-gram改进预测模型研究

情景感知服务(Context-Awareness Services)借助信息技术为用户提供自适应服务,卓著的个性化特性使其将成为下一代杀手级应用.本文综合分析移动用户的位置、时间和业务信息,通过改进N-gram模型有效地预测用户行为趋势,以期通过最优化的方式使用户需求与其所处的环境资源相匹配,既期望能从用户视角获得更加丰富的信息体验,又期望能对运营商有所帮助,对理论研究及商业实践均具有积极意义.     

基于质量的数据挖掘服务选择

在面向服务的数据挖掘系统中各种数据挖掘的算法封装成 Web服务.用户选择合适的数据挖掘服务执行自己的数据挖掘任务,而大多数最终用户并不具备这样的专业知识.从方便用户的角度出发,系统需提供一套服务选择机制,来帮助用户选择高质量的数据挖掘服务.综合通用Web服务的评价标准、数据挖掘领域的专用评价因子及用户评价反馈等多种因素及服务的动态性,给出了一个较全面的数据挖掘服务评价本体,讨论了服务质量的评价方法,给出了基于服务质量评价的动态数据挖掘服务选择方法,用户可根据数据挖掘服务评价本体的语义模型,输入质量约束条件,也可以调整评价因子权值,系统在满足用户约束条件的服务集中,通过计算出服务的综合质量值,挑选最适合的算法执行.     

Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing

Cloud computing promises to provide high quality, on-demand services with service-oriented architecture. However, cloud service typically come with various levels of services and performance characteristics, which makes Quality of Cloud Service (QoCS) high variance. Hence, it is difficult for the users to evaluate these cloud services and select them to fit their QoCS requirements. In this paper, we propose an accurate evaluation approach of QoCS in service-oriented cloud computing. We first employ fuzzy synthetic decision to evaluate cloud service providers according to cloud users’ preferences and then adopt cloud model to computing the uncertainty of cloud services based on monitored QoCS data. Finally, we obtain the evaluation results of QoCS using fuzzy logic control. The simulation results demonstrate that our proposed approach can perform an accurate evaluation of QoCS in service-oriented cloud computing.     

基于QoWS的Web服务评估算法

本文提出了一个完整的Web服务质量的评估算法。此算法在分析了Web服务评量和相对服务质量的基础之上，计算Web服务质量的综合评估值，并且建立一个Web服务质量评估模型，将此算法应用于评估模型中，可以在多个Web服务中选择最优的服务提供给用户。最后，将此模型应用于一个实际的系统中。     

基于用户QoE的流媒体系统性能测评模型设计

影响流媒体用户服务质量体验QoE(Quality of Experience)的因素有很多,如何对用户QoE进行量化判别是一个复杂的问题。为此,以媒体传输指标MDI为基础,研究了用于测量流媒体用户QoE的基本指标,并提出了相关测量数据的获取方法。以流媒体用户的实际体验质量为依据,提出了一种有效的流媒体系统性能评价模型,该模型为流媒体系统的性能分析和优化调整提供了理论基础,具有广泛的应用价值。     

A knowledge and collaboration-based CBR process to improve network performance-related support activities

In a context characterized by a growing demand for networked services, users of advanced applications sometimes face network performance troubles that may actually prevent them from completing their tasks. Therefore, providing assistance for user communities that have difficulties using the network has been identified as one of the major issues of performance-related support activities. Despite the advances network management has made over the last years, there is a lack of guidance services to provide users with information that goes beyond merely presenting network properties. In this light, the research community has been highlighting the importance of User-Perceived Quality (UPQ) scores during the evaluation of network services for network applications, such as Quality of Experience (QoE) and Mean Opinion Score (MOS). However, despite their potential to assist end-users to deal with network performance troubles, only few types of network applications have well established UPQ scores. Besides that, they are defined through experiments essentially conducted in laboratory, rather than actual usage. This paper thus presents a knowledge and Collaboration-based Network Users’ Support (CNUS) Case-Based Reasoning (CBR) Process that predicts UPQ scores to assist users by focusing on the collaboration among them through the sharing of their experiences in using network applications. It builds (i) a knowledge base that includes not only information about network performance problems, but also applications’ characteristics, (ii) a case base that contains users’ opinions, and (iii) a user database that stores users’ profiles. By processing them, CNUS benefits users through the indication of the degree of satisfaction they may achieve based on the general opinion from members of their communities in similar contexts. In order to evaluate the suitability of CNUS, a CBR system was built and validated through an experimental study conducted in laboratory with a multi-agent system that simulated scenarios where users request for assistance. The simulation was supported by an ontology of network services and applications and reputation scheme implemented through the PageRank algorithm. The results of the study pointed to the effectiveness of CNUS, and its resilience to users’ collusive and incoherent behaviors. Besides that, they showed the influence of the knowledge about network characteristics, users’ profiles and application features on computer-based support activities.     

QoE assessment model for multimedia streaming services using QoS parameters

This paper proposes a video QoE (Quality of Experience) assessment model which can assess video quality of experience with only QoS (Quality of Service) parameters and their relative importance at network layer. Since network or service providers can forecast whether to provide multimedia services above a certain level of service quality using the proposed model, they can offer and maintain optimum network environment for multimedia service such as IPTV. Through an experiment of video quality comparison we show that our QoS/QoE correlation model is closely related with video quality degradation patterns to network environmental change.     

基于用户的Web服务FRC评估选择模型

Web服务非功能属性评佑是服务选择的重要前提,而目前的服务评佑方法没有根据各非功能属性内在特征进行其属性值的评估,也没有考虑用户对多个属性的复杂需求,无法有效地选择出真正满足用户需求的最佳服务。因此从用户角度出发,提出了基于用户的Web服务FRC评佑选择模型,模型考虑了用户对各个属性的需求,并根据非功能属性隶属的不同数学性质,将隶属度的概念、模糊评判理论、概率统计方法和TOPSIS多属性决策法应用于模型,建立了模糊性子模型F和随机性子模型R,最终通过确定性子模型C综合评估来实现服务选择。仿真实验结果表明,该模型能较好地根据用户需求有效实现服务选择。     

Quality of experience in distributed databases

This paper proposes an approach to improve the level of Quality of Experience (QoE) that distributed database systems provide. Quality of Experience is a measure of users?? satisfaction when using a certain service or application. Therefore, the main objective of this paper is to provide mechanisms to increase users?? satisfaction when accessing distributed database systems. In traditional database systems, users cannot specify execution-related constraints. Then, the database system cannot evaluate if user expectations are satisfied and neither the system can take corrective actions when necessary. In this work, we present the QoE-oriented distributed database system (QoE-DDB). It allow users to specify Data Access Requirements (DARs) and aims to please users by satisfying the DARs they define. We define a set of types of Data Access Requirements and propose some SQL extensions that enable users to specify execution-related requirements. Proposed types of DARs include execution deadline and priority, execution start and finish times, data availability and freshness degrees, and disconnected execution mode. In our QoE-DDB, each user??s command is transformed into one or more tasks that are executed by data services. Community modules and local data services negotiate Service Level Objectives (SLOs) for each task, which improves the system??s dependability. We propose both QoE-oriented scheduling and dynamic data placement strategies. Proposed architecture and scheduling strategies enable the system to be used in a wide range of distributed environments, from tightly-coupled homogeneous environments (e.g. composed by off-the-shelf computers connected by a LAN) to highly heterogeneous and geographically distributed systems, where data services have some degree of autonomy. Traditional performance indicators (e.g. throughput and response time) are not adequate to measure the QoE a system provides. We also propose some specialized Key Performance Indicators (KPIs) to estimate the QoE level a database system provides. Finally, we present experimental results obtained through the use of benchmark data and queries together with a prototype that implements proposed strategies. In our experiments, we consider realistic scenarios and compare proposed scheduling strategies with their best-effort oriented counterparts. Obtained results prove the importance of our QoE-oriented approach.     

Applying a Stochastic Model to a Dynamic, QoS Enabled Web Services Hosting Environment

Data centres which host Web services for other organisations and users in a Grid environment must provide for Quality of Service (QoS) requirements to be specified to ensure deployed services perform as desired. As services hosted by a data centre receive unpredictable rates of demand, servers must be allocated dynamically to service pools that are over utilised to avoid breaking QoS requirements. This work describes how a cost based stochastic model for resource allocation is used in data centre middleware to balance server utilisation, and how the model was used to enable a data centre to meet QoS requirements. The stochastic QoS model is compared to two other QoS models and is shown to be the most effective in a number of experiments.