Abstract
With the great development of cloud manufacturing (CMfg), currently accurate prediction about quality-of-service (QoS) has become a hot issue. However, as task diversity increases, most existing QoS prediction methods mainly focus on the similarity measure between users and services, and thus ignore the impact of task characteristics in CMfg. Therefore, to solve above problem, a task-driven QoS prediction model with the case library is established to predict unknown QoS value. First, we present a similarity measure method in the case library including service similarity and task similarity, to search similar services and corresponding historical tasks. Then, QoS prediction model is established considering task similarity and the time decay function as well as service similarity. According to the experiments, our model outperforms current methods with respect to prediction accuracy, and the key parameters have also been studied.
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Acknowledgments
This project was supported by the National Natural Science Foundation of China under grant No. 71271224. The authors would like to appreciate the constructive and helpful comments from the editors and anonymous reviewers.
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Liu, J., Chen, Y., Wang, L., Niu, Y., Zuo, L., Ling, L. (2018). Task-Driven QoS Prediction Model Based on the Case Library in Cloud Manufacturing. In: Wang, S., Price, M., Lim, M., Jin, Y., Luo, Y., Chen, R. (eds) Recent Advances in Intelligent Manufacturing . ICSEE IMIOT 2018 2018. Communications in Computer and Information Science, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-13-2396-6_26
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DOI: https://doi.org/10.1007/978-981-13-2396-6_26
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