Computing performance requirements for web service compositions

In order to produce service compositions, modern web applications now combine both in-house and third-party web services. Therefore, their performance depends on the performance of the services that they integrate. At early stages, it may be hard to quantify the performance demanded from the services to meet the requirements of the application, as some services may not be available or may not provide performance guarantees. The authors present several algorithms that compute the required performance for each service from a model of a service composition at an early stage of development. This is also helpful when testing service compositions and selecting candidate web services, enabling performance-driven recommendation systems for web services that could be integrated into service discovery. Domain experts can annotate the model to include partial knowledge on the expected performance of the services. We develop a throughput computation algorithm and two time limit computation algorithms operating on such a model: a baseline algorithm, based on linear programming, and an optimised graph-based algorithm. We conduct theoretical and empirical evaluations of their performance and capabilities on a large sample of models of several classes. Results show that the algorithms can provide an estimation of the performance required by each service, and that the throughput computation algorithm and the graph-based time limit computation algorithm show good performance even in models with many paths.     

基于协同过滤和Slope One算法的Web服务可靠性预测

针对Web服务的可靠性预测已成为服务计算领域的研究热点。为提高已有的针对Web服务可靠性预测方法的性能,提出两种方法。首先,针对基于协同过滤的Web服务可靠性预测方法,对用户的相似性、服务相似性以及预测值的计算方法都进行了适当的改进;其次,将k-means聚类算法与Slope One算法进行集成,进而用于开展Web服务可靠性预测。实验结果表明,相较已有方法,本文所提出的方法具有更高的预测精度。     

基于邻接矩阵的Web服务组合

针对当前Web服务组合方法在动态性和算法时间复杂度方面存在的不足,提出一种基于邻接矩阵的服务组合方法,使用邻接矩阵表示服务间的顺序及并发关系,在构建抽象服务基础上由领域专家初步建立抽象服务的组合关系,利用Warshall算法计算传递闭包来判定服务请求是否可满足,同时构建动态服务组合流程。方法操作简单,Warshall算法时间复杂度为O(n3),在服务组合中有较好的实用性。     

基于时间序列和任务调度的Web数据聚类算法

为了实现Web服务请求数据的快速聚类,并提高聚类的准确率,提出一种基于增量式时间序列和任务调度的Web数据聚类算法,该算法进行了Web数据在时间序列上的聚类定义,并采用增量式时间序列聚类方法,通过数据压缩的形式降低Web数据的复杂性,进行基于服务时间相似性的时间序列数据聚类。针对Web集群服务的最佳服务任务调度问题,通过以服务器执行能力为标准来分配服务任务。实验仿真结果表明,相比基于网格的高维数据层次聚类算法和基于增量学习的多目标模糊聚类算法,提出的算法在聚类时间、聚类精度、服务执行成功率上均获得了更好的效果。     

煤炭企业信息服务能力评价模型

针对煤炭企业信息服务的特点,采用功能团的方法构建了基于信息资源、信息服务基础设施、信息服务满意度、信息服务方式、信息服务结果5个方面的信息服务能力评价指标体系,提出了基于模糊神经网络的信息服务能力评价模型。该模型可对煤炭企业信息服务平台的信息服务能力进行客观的综合评价,具有操作简单、计算速度快、自我学习等优点。     

基于JXTA和OWL-S上的Web Services发现的研究

本文提出基于JXTA协议上的Web服务的新计算模型,利用OWL-S对Web服务功能和特性进行描述,在P2P网络中的Web服务发现时,通过服务功能描述寻找服务,根据服务功能匹配而寻找定位最佳服务。     

Personalization for unobtrusive service interaction

Increasingly, mobile devices play a key role in the communication between users and the services embedded in their environment. With ever greater number of services added to our surroundings, there is a need to personalize services according to the user needs and environmental context avoiding service behavior from becoming overwhelming. In order to prevent this information overload, we present a method for the development of mobile services that can be personalized in terms of obtrusiveness (the degree in which each service intrudes the user’s mind) according to the user needs and preferences. That is, services can be developed to provide their functionality at different obtrusiveness levels depending on the user by minimizing the duplication of efforts. On the one hand, we provide mechanisms for describing the obtrusiveness degree required for a service. On the other hand, we make use of Feature Modeling techniques in order to define the obtrusiveness level adaptation in a declarative manner. An experiment was conducted in order to put in practice the proposal and evaluate the user acceptance for the personalization capabilities provided by our approach.     

Services recommended trust algorithm based on cloud model attributes weighted clustering

There are many different cloud services available, each with different offerings and standards of quality. Choosing a credible and reliable service has become a key issue. To address the shortcomings of existing evaluation methods, we propose a service clustering method based on weighted cloud model attributes. We calculate user-rating similarity with the weighted Pearson correlation coefficient method based on service clustering, and then compute user similarity combined with the user service selection index weight. This method allows us to determine the nearest neighbors. Finally, we obtain the recommended trust of the service for the target user through the recommendation trust algorithm. Simulation results show that the proposed algorithm can more accurately calculate service recommended trust. This method meets the demand of users in terms of service trust, and it improves the success rate of user service selection.     

基于向量空间的Web服务发现模糊方法

Web服务已逐渐发展成为重要的分布式计算范式。在综合分析了现有的Web服务描述文档的基础上,提出了一种基于模糊集的服务特征项集选取算法以及Web服务向量空间的生成方法。利用生成的向量空间,对Web服务进行模糊聚类。基于此,文中给出了向量空间中进行Web服务发现的模糊方法。所提出的方法只需利用现有的Web服务描述信息,保证了服务发现的有效性。     

基于BTM主题模型的Web服务聚类方法研究

针对网络中海量的Web服务聚类时,因其表征数据稀疏而导致使用传统建模方法所获效果不理想的问题,提出了一种基于BTM主题模型的Web服务聚类方法。该方法首先利用BTM学习整个Web服务描述文档集的隐含主题,通过推理得出每篇文档的主题分布,然后应用K Means算法对Web服务进行聚类。通过与LDA、TF IDF等方法进行对比发现,该方法在聚类纯度、熵和F Measure指标上均具有更好的效果。实验表明,该方法能够有效解决因Web服务描述所具有的短文本性质而导致的数据稀疏性问题,可显著提高服务聚类效果。     

银行业务集中式分组交易核心平台研究与应用

银行核心系统是以处理银行最基本的存款、贷款业务为主的IT系统,其中的交易平台体现了银行核心乃至整体IT服务能力.为了提高集中式架构的核心系统在交易高峰期的服务能力,本文设计了一种具备交易分组能力的核心平台.该平台涵盖了交易服务器、交易分发器、交易处理器三层架构,通过交易分组服务算法实现处理作业按交易分组以及动态分配系统资源.实验结果表明,相较于传统的监听-分发两层结构,本文设计的具备交易分组能力的三层结构的核心平台,具备较高的并发处理能力,系统资源使用情况也比较平稳.     

工业物联网中基于多维特征业务的网络切片匹配算法

工业物联网中的业务具有数量大、种类多、特征维度高等特点,为了实现每一个业务都能根据自身的多维特征有效的匹配到具有特定服务质量的网络切片以获取相应的服务,提出了一种多维特征聚类的业务与网络切片匹配算法。该算法可以对工业物联网设备中提取的业务多维特征进行聚类,根据聚类结果将所定义切片的优先级因子添加到相应的业务中,进而实现业务与网络切片的匹配。实验结果表明,该算法在实现业务与网络切片匹配的过程中,与对比算法相比,既实现了高准确率,又加快了匹配完成的速度。     

基于服务上下文的Web服务发现机制

服务发现成为Web服务组合研究的重点,为解决服务组合过程中服务发现方法存在动态适应能力不足,提出了一种基于服务上下文的Web服务发现机制.该机制将一种服务本体描述语言OWL-S和一种基于服务上下文的服务发现算法相结合,利用语义描述服务的信息,同时定义了服务上下文信息并进行服务上下文匹配来发现所需服务.实验结果表明,基于服务上下文的Web服务发现机制能够提高服务发现的准确性,具有较好地动态适应性.     

植入引导式层次聚类Mashup服务推荐算法*

针对传统Mashup服务推荐算法在关键字聚合搜索和网络构建等方式中计算复杂度过高的问题,提出一种基于语义标签的植入引导式层次聚类Mashup服务推荐算法。首先,为提高聚类算法的收敛精度,提高算法运行效率来满足大型数据搜索对算法简化的需求,采用数据预处理和植入易于获取具有代表性的样本数据对聚类进行引导,防止层次聚类算法顶层集分类失败导致的算法聚类失败。其次,利用改进的聚类算法结合实际的Mashup服务数据库,设计了植入引导式层次聚类Mashup服务推荐算法。最后,通过通过仿真对比表明,基于语义的植入式半监督层次聚类Mashup服务推荐算法的精度要好于对比算法,验证所提算法的有效性。     

QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm

One of the requirements of QoS-aware service composition in cloud computing environment is that it should be executed on-the-fly. It requires a trade-off between optimality and the execution speed of service composition. In line with this purpose, many researchers used combinatorial methods in previous works to achieve optimality within the shortest possible time. However, due to the ever-increasing number of services which leads to the enlargement of the search space of the problem, previous methods do not have adequate efficiency in composing the required services within reasonable time. In this paper, genetic algorithm was used to achieve global optimization with regard to service level agreement. Moreover, service clustering was used for reducing the search space of the problem, and association rules were used for a composite service based on their histories to enhance service composition efficiency. The conducted experiments acknowledged the higher efficiency of the proposed method in comparison with similar related works.     

Achieving benefits with enterprise architecture

There is strong anecdotal evidence that Enterprise Architecture (EA) brings benefits to organisations and that organisations are investing significantly in EA initiatives. However, demonstrating the business value of EA has proven elusive. Many of the benefits of EA are intangible and value is achieved indirectly within business change projects. Furthermore, it is not the EA itself that provides benefits, it is the ability to provide advisory services enabled by the EA that is important. In this paper we focus on EA service capability and develop and test a new research model that explains how EA service brings benefits to organisations. Our findings highlight the importance of EA service capability and dynamic capabilities in creating benefits from EA.     

保证服务质量的Web服务优化选取

快速、准确地发现满足用户需求的Web服务是目前Web服务发现的一个研究重点。文中提出了一个基于服务质量的语义Web服务发现框架。首先采用遗传算法来实现服务的聚类预处理,以此来降低服务的查找空间,提高服务的查询效率。进一步,针对服务请求者对服务质量的需求,实现了基于WSMO-QoS的服务匹配策略。最后,实验结果表明了所提方法的有效性。     

On the performance of B2B e-markets: An analysis of organizational capabilities and market opportunities

This research is a study of factors leading to the success of business-to-business (B2B) electronic marketplaces (EMs). A model based on both organizational capability and market opportunity theories was developed to explain the performance of B2B EMs. Organizational capabilities included service provision capability and its enabling capabilities, entrepreneurial orientation and human resource capability, whereas market opportunity was modeled as market size and e-commerce awareness of the industry. Data were collected from 128 B2B EMs in China and analyzed using Partial Least Squares. Results suggest that the research model explains the performance of B2B EMs well. More specifically, among the two service capabilities studied, service width contributes significantly to EM performance, while the effects of service depth are yet to be seen. Moreover, the enabling organizational capabilities and market opportunity factors affect EM performance both directly and indirectly through their enhancement of EM service provision capability.     

一种基于群体智能的多主体聚类算法*

目前聚类算法普遍存在对初始化参数和异常数据敏感,难以找到最优聚类以及聚类的有效性等问题.利用群体智能和多主体系统具有的自组织性、健壮性、可扩展性和简单性等优点,给出了一种新型的优化聚类算法.在三维空间搭建主体运行环境,丰富主体的记忆、通信以及信息协调能力,增强主体的分析和判断能力.实验证明,该新型聚类算法具有运行速度快,准确性高以及对数据的输入顺序不敏感,能应付异常数据,处理高维、高复杂性数据等优点,可应用于图像处理、模式识别、文档归类等多个领域.