How to assure Quality of Service (QoS) of the third-party services is very important for the SOA. Effective monitoring technique towards QoS, which is an important measurement for third-party service quality, is necessary to ensure quality of Web service. Current monitoring approaches do not consider the influences of environment factors such as the position of server, user usage, and the load at runtime. Ignoring these influences, which do exist among the monitoring process, may cause existing monitoring approaches producing unpredictable monitoring results. In order to overcome this limitation, this paper proposes a novel Web Service QoS (WS-Qos) monitoring approach sensitive to environmental factors called weighted Bayesian Runtime Monitor (wBSRM) based on weighted naïve Bayesian classifiers and Term Frequency-Inverse Document Frequency (TF-IDF) algorithm. wBSRM constructs weighted naïve Bayesian classifier by learning a part of samples to classify the monitoring results. The results meeting QoS standard are classified as [Formula: see text] and the one that does not meet is classified as [Formula: see text]. Classifier can also output ratio between posterior probability of [Formula: see text] and [Formula: see text], and consequently the analysis can lead to three monitoring results including [Formula: see text], [Formula: see text] or inconclusive. A set of dedicated experiments are conducted to validate wBSRM. The experiments are based on a public dataset and a simulated dataset under the given standard. The experimental results demonstrate that wBSRM is better than previous approaches.
Constructing a Global Social Service Network for Better Quality of Web Service Discovery