基于CBC方法的云上潜在SaaS评估
CBC Approach for Evaluating Potential SaaS on the Cloud
AI总结：
本文针对现有SaaS评估模型仅关注质量属性的不足，提出一种基于约束聚类（CBC）数据挖掘技术的新评估模型，同时考虑服务用户潜在需求与服务质量属性，以支持云上SaaS的潜在质量评估。
摘要：
云计算正在发展成为一种关键的计算平台，用于共享基础设施、平台、软件等资源。这已被证明是扩展现有众多应用的基本需求。软件即服务（SaaS）是指由服务提供商提供的按需软件，其中软件及相关数据托管在云上，服务用户可以通过Web浏览器使用瘦客户端进行访问。SaaS被广泛使用，并为服务用户提供了许多好处。为了实现这些好处，评估SaaS的潜在质量至关重要，不仅对服务用户如此，对服务提供商也是如此。他们必须根据服务用户的需求来评估自己的服务。现有的评估模型仅关注SaaS的质量属性。本文基于约束聚类（CBC）的数据挖掘技术，提出了一种新的评估模型。所提出的模型强调服务用户的潜在需求以及服务的质量属性。
英文摘要：
The cloud computing is evolving as a key computing platform for sharing resources like infrastructure, platform, software etc. This has proven to be an essential requirement for extending many existing applications. Software as a service (SaaS) is referred as on-demand software supplied by service providers in which software and associated data are hosted on the cloud and it can be accessed by service users using a thin client via a web browser. SaaS is commonly utilized and it provides many benefits to service users. To realize these benefits, it is essential to evaluate potential quality of SaaS, not only to the service users but also to the service providers. They have to evaluate their services against requirements of service users. The existing evaluation models are focusing only on quality attributes of SaaS. In this paper, a new evaluation model is proposed based on the data mining technique of Constraint Based Clustering (CBC). The proposed model gives emphasis on potential requirements of service users along with quality attributes of services.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
