Richong Zhang

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Mashing up Web services and RESTful APIs is a novel programming approach to develop new applications. As the number of available resources is increasing rapidly, to discover potential services or APIs is getting difficult. Therefore, it is vital to relieve mashup developers of the burden of service discovery. In this paper, we propose a probabilistic model(More)
Web services, as loosely-coupled software systems, are increasingly being published to the web and there are a large number of services with similar functions. Therefore, service users compare the non-functional properties of services, e.g., Quality of Service (QoS), when they make service selection. This paper aims at generating a more comprehensive web(More)
Web service discovery is a vital problem in service computing with the increasing number of services. Existing service discovery approaches merely focus on WSDL-based keyword search, semantic matching based on domain knowledge or ontologies, or QoS-based recommendations. The keyword search omits the underlying correlations and semantic knowledge or QoS(More)
With the popularity of social network, the demand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintaining and processing of dynamic graph is significantly high. In this paper, we design iGraph, an incremental graph processing system for dynamic(More)
E-commerce web sites, such as, provide platforms for consumers to review products and share their opinions. However, it is impossible for consumers to read throughout the huge amount of available reviews. In addition, the quality and helpfulness of reviews are unavailable unless consumers have to read through them.This paper proposes an(More)
The prediction of trust relationships in social networks plays an important role in the analytics of the networks. Although various link prediction algorithms for general networks may be adapted for this purpose, the recent notion of “trust propagation” has been shown to effectively capture the trust-formation mechanisms and resulted in an(More)
This paper studies the quality of web service prediction problem. We formalize the QoS prediction problem by incorporating multiple contextual characteristics via collective matrix factorization that simultaneously factor the user-service quality matrix and contextual information matrices. Using the service category and location context, we develop three(More)