ABSTRACT

Service-oriented architecture (SOA) provides a flexible IT infrastructure in order to deal with global competition. However, real-world services change their architectures and functions frequently. This work uses data integration, information synthesis, and knowledge fusion to analyze SOA evolution and complexity. A quantificational approach is provided to estimate the change effects on a service-oriented system. We established a cloud service community to provide rich cloud services and a service mashup platform that connects API service providers and service users. The platform user can invoke all API services on the platform without jumping to the third-party website. The platform users can filter and accept the services recommended by the platform, and also freely compose the services which are real-world services. Data integration, information synthesis, and knowledge fusion are applied to mashups that users constructed, helping users avoid complexity and unreliability that is our objective. We also showed that our method achieved significant quantitative results.

摘要

面向服务的架构 (SOA) 提供了灵活的 IT 基础架构以应对全球竞争。然而，现实世界的服务经常改变它们的架构和功能。这项工作使用数据集成、信息合成和知识融合来分析 SOA 的演变和复杂性。提供了一种量化方法来估计对面向服务的系统的变化影响。我们建立了云服务社区，提供丰富的云服务和连接API服务商和服务用户的服务混搭平台。平台用户无需跳转第三方网站即可调用平台上的所有API服务。平台用户可以过滤和接受平台推荐的服务，也可以自由组合属于现实世界的服务。数据整合，将信息合成和知识融合应用于用户构建的 mashup 中，帮助用户避免复杂性和不可靠性，这是我们的目标。我们还表明，我们的方法取得了显着的定量结果。