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引用次数: 0
摘要
学术界和工业界对各种web服务的集成表现出极大的兴趣。将众多相应的web服务合并为一个的结果,从而导致发现新的信息类型。随着Web 2.0的出现,电子商务网站的开发和使用有了实质性的增长。但这也导致了新的需求领域的出现,比如需要一个高效的数据挖掘技术来搜索、健壮和可靠的基础设施。web 2.0和集成web mashup服务的融合为用户提供了优势。因此,用户可以根据感兴趣的应用程序轻松地与不同类型的服务进行交互。集成mashup服务系统采用用户行为分析对数据进行聚类。利用欧几里得距离算法对各种web服务的数据进行聚类。最后,基于特定用户的多个预测,向用户显示结构化的输出。本文采用服务中的知识发现(Knowledge Discovery in Services, KDS)流程,从而发现新的数据类型。
Implementation of integrated mashup service a concepts
Academia and industries are showing great deal of interest in integration of various web services. Result of numerous corresponding web services merged into one, which leads to discovery of new types of information. With the advent of Web 2.0, there has been a substantial increase in development and usage of E-commerce websites. But this has also lead to new arenas of requirements that open up such has an efficient data mining technology for searching, robust & reliable infrastructure. Amalgamation of web 2.0 and integrated web mashup services give advantages to user. As a result, user can interact easily with different types of services as per application of interest. Integrated mashup service system used user behavior analysis for clustering of data. Data of various web services is clustered by using Euclidean distance algorithm. Finally structured output is shown to the user based multiple predictions of particular user. This paper adopts Knowledge Discovery in Services (KDS) process, which leads to discover new type of data.