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With the rapid development of services computing in the past decade, automatic selection of QoS-aware Web service out from numerous candidates with similar functions is becoming a hot yet thorny issue. Conducting warming up tests on numerous candidate services for quality evaluation is extremely time-consuming and expensive, making it vital to generate highly accurate predictions for missing QoS data based on known ones. Since QoS data are time-dependent, it is vital to consider the temporal dynamic patterns hidden in historical ones when building a QoS-estimator. For addressing this issue, this study invents a Kalman filter-incorporated Latent Factor Analysis (KLFA)-based QoS-estimator, which precisely models the temporal patterns hidden in dynamic QoS data. Experimental results based on large-scale and real-world Web service QoS data demonstrate that compared with state-of-the-art temporal-aware QoS-estimators, a KLFA-based one achieves significantly higher prediction accuracy for missing QoS data.
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