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Service recommendation in a Web of services with uncertain QoS is a challenging problem. In this paper, we propose a reputation-based service recommendation framework. We formally define a service reputation model that analyzes the relations between uncertain QoS and reputation. We also devise a two-phase planning approach to constructing a composite(More)
For a number of services with similar functionality reputation has been regarded as one of the most important methods to identify good ones from bad ones. However, a composite service, which is composed of multiple component services, obtains only one score (or feedback) after every invocation. In order to compute the reputation of each component service,(More)
Reputation is useful for establishing trust between Web service (WS) providers and WS consumers. In the context of WS composition, a challenging issue of reputation management is to propagate a user's impression of a composite WS (i.e., the user's feedback rating) to its component WSs. In this paper, we propose a Shapley value based approach which can(More)
Most of the existing domain adaptation learning (DAL) methods relies on a single source domain to learn a classifier with well-generalized performance for the target domain of interest, which may lead to the so-called negative transfer problem. To this end, many multi-source adaptation methods have been proposed. While the advantages of using multi-source(More)
As composite web services are often long lasting, loosely coupled, cross application and administrative boundaries, transactional support to integrated business via composing individual web services is a critical issue. Currently, WS-BPEL which is more expressive than traditional workflow language, has been the de facto standard for web service composition.(More)
Traditional service selection schemes require users to define a utility function by assigning weights to each quality-of-service (QoS) metric. To relieve users from the professional knowledge, skyline techniques have been studied recently by several researchers. However, the size of skyline services is sometimes not easy controlled due to intrinsic(More)