服务发现是面向服务的网络软件开发过程的关键阶段,同时也是影响服务组合效率的关键因素.针对当前服务发现自动化程度低下、准确性不高的现状,从两个方面提高服务组合效率:首先,提出一种自动组合服务发现模式,支持流程粒度的组合服务发现及复用；其次,在发现过程中设计了一种量化的相似性评估算法,综合考虑组合流程的静态结构与动态行为特征,以提高服务发现的准确率；最后,结合以上两方面形成一个基于流程相似性的自动服务发现框架(automatic service discovery framework based on business process similarity,简称AutoDisc).利用真实数据完成的评估实验结果表明,AutoDisc的准确性优于单纯考虑结构或行为的发现方法,在所给出的应用案例中,使服务组合效率提高75.5％,具有较好的可扩展性.%Service discovery is a critical stage of the development for Internet-scale software produced through service composition. Presently, development efficiency of composite service is confined by a low degree of automation and accuracy of service discovery. This paper propose the AutoDisc (automatic service discovery framework based on business process similarity) schema to improve development efficiency through two aspects. One is to improve the efficiency of discovery by automatic recommendation. The other is to improve the accuracy of service discovery by combining the structural and behavioral factors of services. Through the proposed approach, this paper automatically models the requirements of service discovery and recommends the most appropriate composite services to developers. Finally, the paper illustrates the effectiveness of AutoDisc with a set of experimental evaluations which show that AutoDisc can increase the development efficiency by 75.5% in the evaluating context.
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