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2018 IEEE International Conference on Web Services (ICWS) (2018)
San Francisco, CA, USA
Jul 2, 2018 to Jul 7, 2018
ISBN: 978-1-5386-7247-1
pp: 42-49
Driven by the widespread application of Service-Oriented Architecture (SOA), the quantity of web services and their users keeps increasing in the service ecosystem. Since services are hosted by service providers, it will be very helpful to predict the tendency of services invocation for service providers, so that proper actions may be taken to ensure the quality of services. Two major challenges exist in predicting the tendency of services invocation, however. First, different service invocation sequences may bear different and complicated characteristics, which is hard to be modeled generally. Second, the intricate relations between service invocation sequences are valuable but hard to be discriminated and utilized. To address these issues, a deep neural network, named Piecewise Recurrent Neural Network (PRNN), is developed by taking both generality and pertinence into consideration. For generality, PRNN extracts complicated characteristics of all service invocation sequences through Long Short-Term Memory (LSTM) units. For pertinence, PRNN develops a piecewise mechanism, through which service invocation sequences can be clustered automatically and predicted discriminatingly. Extensive experiments in real-world dataset show that PRNN outperforms baseline methods in predicting the tendency of services invocation.
recurrent neural nets, service-oriented architecture, Web services

H. Lin, Y. Fan, J. Zhang and B. Bai, "PRNN: Piecewise Recurrent Neural Networks for Predicting the Tendency of Services Invocation," 2018 IEEE International Conference on Web Services (ICWS), San Francisco, CA, USA, 2018, pp. 42-49.
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