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Leveraging Data Augmentation for Service QoS Prediction in Cyber-physical Systems

Published:08 March 2021Publication History
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Abstract

With the fast-developing domain of cyber-physical systems (CPS), constructing the CPS with high-quality services becomes an imperative task. As one of the effective solutions for information overload in CPS construction, quality of service (QoS)-aware service recommendation has drawn much attention in academia and industry. However, the lack of most QoS values limits the recommendation performance and it is time-consuming for users to get the QoS values by invoking all the services. Therefore, a powerful prediction model is required to predict the unobserved QoS values. Considering the fact that most existing QoS prediction models are unable to effectively address the data-sparsity problem, a novel two-stage framework called AgQ is proposed for QoS prediction. Specifically, a data augmentation strategy is designed in the first stage to enlarge the training set by drawing additional virtual instances. In the second stage, a prediction model is applied that considers both virtual and factual instances during the training procedure. We conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the our QoS prediction framework and verify that the data augmentation strategy can indeed alleviate the data-sparsity problem. In terms of mean absolute error, taking the Multilayer Perceptron model as an example, the maximum improvement achieves 5% under 5% sparsity.

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      • Published in

        cover image ACM Transactions on Internet Technology
        ACM Transactions on Internet Technology  Volume 21, Issue 2
        June 2021
        599 pages
        ISSN:1533-5399
        EISSN:1557-6051
        DOI:10.1145/3453144
        • Editor:
        • Ling Liu
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 8 March 2021
        • Accepted: 1 September 2020
        • Revised: 1 August 2020
        • Received: 1 June 2020
        Published in toit Volume 21, Issue 2

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