云计算环境下隐私需求的描述与检测方法

云计算已经成为一种计算范型为用户提供服务,但其开放性、虚拟化和服务外包化的特点使得用户的隐私信息难以控制和保护。以描述逻辑为基础,提出了一种云计算环境下面向语义的隐私需求描述与检测方法。首先,对用户隐私需求与服务提供者的隐私策略进行描述;其次,对两者之间是否存在冲突进行检测,发现满足用户隐私需求的服务;最后,利用Protégé本体建模工具对用户的隐私需求和服务提供者的隐私策略进行建模,并利用Pellet推理机进行了实验,分别对本体模型中的概念进行一致性检测和对概念与逻辑公理之间的可满足性进行检验,从而证明了此检测方法的正确性与可行性。     

物联网需求驱动的服务组合方法

为实现依据用户需求动态地组合原子服务, 形成服务集合, 提出一种基于本体的服务组合方法。首先对物联网服务进行多视图建模, 使用服务交互模型描述服务与物理实体的交互过程, 服务功能模型刻画物理世界中服务的属性和行为, 服务状态模型表示服务引起的期望状态变化, 并采用扩展的OWL描述面向物理对象的服务。同时, 采用多维QoS模型对用户的需求进行建模, 使用语义的方法进行服务的匹配与组合, 并基于QoS选择满足约束的服务。最后, 通过案例研究和实验验证了所提方法的正确性和有效性。     

用户驾驶行为建模的研究和应用

随着汽车工业的快速发展,车载环境成为人们日常生活中最重要的私人空间之一。同时,随着车载设备的增多,车载服务也日益丰富,然而大多数车载服务都是针对大众而设计的,缺乏个性化支持。通过车载环境下的用户建模,可以使车载环境成为以用户为中心的个性化空间,从而为用户提供个性化车载服务。车载服务的一个关键目标是提高用户的行车安全,它与用户的驾驶行为息息相关。因此,对用户驾驶行为的建模是车载环境下用户建模的一个重要组成部分。对车载环境下的用户建模进行了研究,提出并实现了用户驾驶行为的建模方案,并在其基础上构建了个性化车载服务。     

RGPS制导的按需服务组织与推荐方法

随着互联网上各种Web服务数量的快速增长,如何发现满足用户个性化、多样化需求的服务成为亟待解决的关键问题之一.文中提出了一种面向领域问题的、基于角色-目标-流程-服务模型的按需服务组织与推荐方法.该方法在对与领域问题相关的用户角色(R)、目标(G)、流程(P)和服务(S)进行建模的基础上,利用RGPS元素间的关联关系对领域中服务实现了按需组织,同时根据不同的用户需求表达方式相应地设计了3种服务推荐算法.最后,通过实验验证了所提方法的有效性,并结合实际案例介绍了所开发的系统平台.     

分布式系统可伸缩性研究综述

可伸缩性(Scalabifity)反映了系统可随系统需求和资源变化,持续满足性能需求的能力。在不同的场景下,可伸缩性的基本定义和度量方法能够通过不同的角度进行理解和表达。根据系统需求和运行状态,改变可用资源数量以及任务调度方式,动态调整系统性能,是系统可伸缩性实现的主要途径。分布式资源管理系统可伸缩性设计的关键技术可以从并行任务调度和分布式系统框架两个方面进行分析。可伸缩性测试是检测和评价系统性能的主要依据,并行代码测试以及可伸缩性测试系统设计的主要方法是测试技术的两个重要组成部分。随着软件范型的发展变化,软件的部署和提供逐步向基于开放、共享虚拟化资源管理平台的在线服务方式的转变,可伸缩性已成为云计算背景下软件服务的重要性能指标,进一步探讨可伸缩性在新的软件范型下所面临的挑战性问题是可伸缩性研究的新方向。     

一种基于操作序列的Web服务建模与自动构建方法

近年来,基于SOA(service-oriented architecture)的软件开发方法已成为学术界与工业界的热点,其良好的可扩展性和松散耦合性在工业领域得到了很好的发挥,在学术领域对于Web服务的互操作等核心问题的研究也获得了大量成果.然而,对于Web服务本身构建方法的研究与实现却仍然不能很好地满足用户的需求,创建Web服务的手段比较单一,自动化构建技术尚不成熟.针对这些问题,提出了一种基于操作序列的Web服务建模与自动构建方法.用户通过3个步骤完成Web服务中操作序列组件的建模:①元操作建模;②操作元序列建模;③操作序列建模.在模型的基础上,提出了由模型转换为操作序列组件代码的算法.提出的方法在实际系统中得到了应用,并通过相关对比实验说明了算法的高效性.     

服务构件模型的研究与设计

针对如何更高效和准确地将已存在的Web服务集成到业务流程中,提出了服务构件模型。该模型以Web服务为业务功能实现的基础,用BPMN建模用户需求,输出为可执行的BPEL模型。该服务构件模型内部对原子服务进行了重新定义,便于服务的查找;而且加入了服务的QoS特征,便于服务的选择。     

个性化信息服务中基于Tag的用户兴趣模型

随着web信息爆炸增长,个性化信息服务成为人们研究的热点,用户兴趣建模是个性化服务的关键,针对当前用户建模的缺点和tag的广泛应用,对基于tag的用户兴趣建模进行研究,首先通过实验证明tag中蕴含用户稳定的兴趣及tag分布的其他特征,然后提出加权树形结构由粗到细的粒度表示用户模型,为提高服务时效性,对用户频繁一起使用的...     

教育网站个性化推荐系统中的一种用户兴趣模型的建立

用户兴趣模型用于描述用户的个人信息、专业背景、偏好倾向和历史行为等,通过这些信息,系统可以发现和预测用户的信息需求,从而对用户进行个性化的信息推荐服务.用户兴趣模型是影响推荐系统服务效率的重要因素,因此针对用户兴趣进行建模是个性化推荐系统实现中要重点考虑的问题之一.本文从教育网站用户对象特点出发,提出了将用户兴趣分为固定兴趣与临时兴趣相结合的动态模型.     

Web服务组合的互模拟验证

为检验Web服务组合的实现与用户需求的一致性,在开互模拟形式化理论和检验工具的基础上,提出了一个自动化检验方法。首先,用π-演算分别对用户需求和商业流程可执行语言（BPEL4WS）程序实现建模,然后对它们进行弱开互模拟检验,当它们不互模拟时,检验工具能自动标识关键的不互模拟的BPEL4WS程序片段。最后通过实例说明这一方法的可行性。     

支持服务组合的需求模型及其可满足性判定

服务组合一般是根据用户需求来查找匹配的服务并对其进行组合,但用户需求往往是基于自然语.言的,很难用于服务的自动组合.提出了一种基于环境本体的组合服务需求模型,该模型以环境实体上的意图为基础,将关联意图集定义为任务.引入Petri网作为任务间的控制逻辑关系,并给出了一种判定需求可满足性的方法.最后选择旅行安排作为具体案例...     

智能计算服务的需求获取方法

智能计算服务由服务提供者通过互联网为服务消费者提供数据的分析和处理，并建立学习模型完成智能计算功能。由于服务提供者与服务消费者之间缺乏有效的沟通渠道，以及服务消费者反馈的需求描述模糊、混乱，目前缺乏一种统一的服务需求获取方法对用户持续变化的需求进行有效的分析、组织和规约，导致智能计算服务无法根据用户的需求进行快速改进。针对服务开发中需求变更的持续性和不确定性等问题，提出了一种智能计算服务的需求获取方法。该方法首先从Stack Overflow问答论坛获取智能计算服务的应用反馈和问题，然后根据服务消费者所关注的需求类型采用不同的学习模型（包括支持向量机（SVM）、朴素贝叶斯和TextCNN）对其进行知识分类和优先级排序，最后采用自定义的服务需求模板统一描述智能计算服务的需求。     

Personal mobile services

Ubiquitous information access through mobile devices has become a typical practice in everyday life. The mobile service paradigm shifts the role of mobile devices from consumers to providers, opening up new opportunities for a multitude of collaborative services and applications ranging from sharing personal information to collaborative participatory sensing. Although many basic principles of the standard Web service approach continue to apply, the inherent constraints of mobile devices and broadband wireless access render the deployment of the standard architecture in mobile environments inefficient. This paper introduced personal services, a user-centric paradigm that enables service-oriented interactions among mobile devices that are controlled via user-specified authorization policies. Personal services exploit the user’s contact list (ranging from phonebook to social lists) in order to publish and discover Web services while placing users in full control of their own personal data and privacy. Experimental validation demonstrates the ability of personal services to foster a new generation of collaborative mobile services. Performance evaluation results show that the publication and discovery through contact lists are efficient and that service announcements and discovery requests can reach a huge number of users in a few seconds. Results also support a conclusion that resources-constrained devices can collaborate to carry out functionalities beyond the ability of their resources limitations.     

Structural analysis of value creation in software service platforms

As software service platforms grow in number of users and variety of service offerings, it raises the question of how this phenomenon impacts the value obtained by users. This paper identifies system usability, service variety, and personal connectivity to be the major determinants that contribute to the value offered to users on mobile software service platforms. A structural equation model, which is based on utility theory, technology acceptance theory, and the theory of network externalities, has been constructed from seven observed constructs, reflecting the three determinants and the user value. The lower bound of user value is estimated through the user’s willingness-to-pay for services and the user’s willingness to spend time on using services. For the validation, a co-variance-based structural equation analysis has been conducted on online survey data of 210 users of mobile service platforms (e.g., Android, iOS). The results show that the number of services used and the number of active user connections were found to be the strongest constructs explaining user value. Perceived usefulness did not explain user value as much. In total, they can explain 49 % of the value that the user receives from the platform. The implication of this result is that users’ value from a software service platform cannot be explained by the technology acceptance model itself. Instead, an approach that, as used in this research, of integrating network externality theory, utility theory, and technology acceptance theory is necessary.     

A fast and scalable approach for IoT service selection based on a physical service model

Information Systems (ISs) have become one of the crucial tools for various organizations in managing and coordinating business processes. Now we are entering the era of the Internet of Things (IoT). IoT is a paradigm in which real-world physical things can be connected to the Internet and provide services through the computing devices attached. The IoT infrastructure is starting to be integrated with ISs thereby diminishing the boundaries between the physical world and the business IT systems. With the development of IoT technologies, the number of connected things and their available physical services are increasing rapidly. Thus, selecting an appropriate service that satisfies a user’s requirements from such services becomes a time-consuming challenge. To address this issue, we propose a Physical Service Model (PSM) as a common conceptual model to describe heterogeneous IoT physical services. PSM contains three core concepts (device, resource, and service) and specifies their relationships. Based on the proposed PSM, we define three types of Quality of Service (QoS) attributes and rate candidate services according to user requirements. To dynamically rate QoS values and select an appropriate physical service, we propose a Physical Service Selection (PSS) method that takes a user preference and an absolute dominance relationship among physical services into account. Finally, experiments are conducted to evaluate the performance of the proposed method.     

按需智能问答系统Uniponse

现有的智能问答系统(IQAS,Intclligent Question-Answer System)局限于针对简单问题寻找最适合的答案,无法满足用户日益复杂的个性化问题要求。基于需求元描述框架RGPS,经过需求识别、需求分析和需求验证等手段,将用户的个性化问题转换为一种问答服务需求,然后通过动态挖掘Internet上软件服务所构成的服务管理库,并结合基于服务关系网络的流程服务组合思想,最终形成能够回答此类问题的智能问答服务。构建了面向交通领域的按需智能回答原型系统Uniponse,验证了上述方法的有效性和可行性。     

A flexible service selection for executing virtual services

With the adoption of a service-oriented paradigm on the Web, many software services are likely to fulfil similar functional needs for end-users. We propose to aggregate functionally equivalent software services within one single virtual service, that is, to associate a functionality, a graphical user interface (GUI), and a set of selection rules. When an end user invokes such a virtual service through its GUI to answer his/her functional need, the software service that best responds to the end-user’s selection policy is selected and executed and the result is then rendered to the end-user through the GUI of the virtual service. A key innovation in this paper is the flexibility of our proposed service selection policy. First, each selection policy can refer to heterogeneous parameters (e.g., service price, end-user location, and QoS). Second, additional parameters can be added to an existing or new policy with little investment. Third, the end users themselves define a selection policy to apply during the selection process, thanks to the GUI element added as part of the virtual service design. This approach was validated though the design, implementation, and testing of an end-to-end architecture, including the implementation of several virtual services and utilizing several software services available today on the Web.     

An intelligent web service group-based recommendation system for long-term composition

A modern model long-term composed service (LCS) with a group recommendation system has an indefinite lifespan. An LCS is used as a long-term business goal, and for a business committed to its customers, support will be provided to customers enabling them to book, e.g. an automotive service through online web services by providing information that the LCS then uses to offer more support. However, identifying the exact service to meet the user requirement is essential. Service composition has been identified as the key task in achieving various QoS performances. There exist various approaches that involve service composition according to the throughput and popularity. However, they fail to achieve the expected performance. Towards improving the performance of the LCS, a novel LCS that is based on the user queries of a group of persons is developed to give the best business services based on previous travel details and services. The method carries out service selection and composition according to the ratings provided by users towards any service. Additionally, the method considers the user-to-service rating and service-to-service rating, which are measured according to the coupling quality. Therefore, the proposed novel LCS provides better services based on the user ratings for particular business queries. The method ranks the services according to the rating values to perform service composition, with consideration of the detection of similar user groups and utilization of the rating values in service selection. We aim to propose a novel LCS work based on group ratings and a group of services. This work is intended to reduce the time complexity of changes in the LCS network using the group recommendation system.