Prediction of atomic web services reliability based on k-means clustering

Silic, Mario; Delač, Goran; Srbljic, S.

doi:10.1145/2491411.2491424

Cited by 40 publications

(25 citation statements)

References 29 publications

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“…⇀ denotes the cluster center, which is a vector generated by an iterative means of averaging a set of descriptions from the same category of services. K was determined experimentally in this study, an approach that facilitated more rigorous classification than that found in other existing works [27,28]. The clustering process was used to classify a large number of TISs and keep each center's information separate.…”

Section: Semantic-based Matching Model Of Tissmentioning

confidence: 99%

Hierarchical Matching of Traffic Information Services Using Semantic Similarity

Duan

Tang

Kou

et al. 2018

Journal of Advanced Transportation

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Service matching aims to find the information similar to a given query, which has numerous applications in web search. Although existing methods yield promising results, they are not applicable for transportation. In this paper, we propose a multilevel matching method based on semantic technology, towards efficiently searching the traffic information requested. Our approach is divided into two stages: service clustering, which prunes candidate services that are not promising, and functional matching. The similarity at function level between services is computed by grouping the connections between the services into inheritance and noninheritance relationships. We also developed a three-layer framework with a semantic similarity measure that requires less time and space cost than existing method since the scale of candidate services is significantly smaller than the whole transportation network. The OWL TC4 based service set was used to verify the proposed approach. The accuracy of offline service clustering reached 93.80%, and it reduced the response time to 651 ms when the total number of candidate services was 1000. Moreover, given the different thresholds for the semantic similarity measure, the proposed mixed matching model did better in terms of recall and precision (i.e., up to 72.7% and 80%, respectively, for more than 1000 services) compared to the compared models based on information theory and taxonomic distance. These experimental results confirmed the effectiveness and validity of service matching for responding quickly and accurately to user queries.

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Section: Semantic-based Matching Model Of Tissmentioning

confidence: 99%

Hierarchical Matching of Traffic Information Services Using Semantic Similarity

Duan

Tang

Kou

et al. 2018

Journal of Advanced Transportation

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“…In recent years, service computing [10], [11], [12], [13], [14] has attracted more and more attention from industry communities as well as academic circles. Among all topics in service computing, QoS-aware service selection and service composition are studied in a large number of literatures [15], [16], [17], [18], whose goal is to decide which candidate services to be used as components in complex systems.…”

Section: Related Workmentioning

confidence: 99%

Location-Based Hierarchical Matrix Factorization for Web Service Recommendation

Zhu

Zheng

et al. 2014

2014 IEEE International Conference on Web Services

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Web service recommendation is of great importance when users face a large number of functionally-equivalent candidate services. To recommend Web services that best fit a user's need, QoS values which characterize the non-functional properties of those candidate services are in demand. But in reality, the QoS information of Web service is not easy to obtain, because only limited historical invocation records exist. To tackle this challenge, in recent literature, a number of QoS prediction methods are proposed, but they still demonstrate disadvantages on prediction accuracy. In this paper, we design a location-based hierarchical matrix factorization (HMF) method to perform personalized QoS prediction, whereby effective service recommendation can be made. We cluster users and services into several user-service groups based on their location information, each of which contains a small set of users and services. To better characterize the QoS data, our HMF model is trained in a hierarchical way by using the global QoS matrix as well as several location-based local QoS matrices generated from userservice clusters. Then the missing QoS values can be predicted by compactly combining the results from local matrix factorization and global matrix factorization. Comprehensive experiments are conducted on a real-world Web service QoS dataset with 1,974,675 real Web service invocation records. The experimental results show that our HMF method achieves higher prediction accuracy than the state-of-the-art methods.

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“…The composition process of web services is actually a workflow process; then a workflow-based model is also a usual method to predict the reliability [26]. Moreover, there are also other approaches to estimate the reliability of composite services from the ports, message delivery, or network transition [27][28][29][30][31]. Web services are different from general software system, as they are dynamic composition.…”

Section: Related Workmentioning

confidence: 99%

A Dynamical Reliability Prediction Algorithm for Composite Service

Xie

Ren

2014

Mathematical Problems in Engineering

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Dynamic selection and dynamic binding and rebinding at runtime are new characters of composite services. The traditional static reliability prediction models are unsuitable to dynamic composite services. A new reliability predicting algorithm for composite services is proposed in this paper. Firstly, a composite service is decomposed into composition unites (executing path, composite module and atomic service) according to their constituents. Consequently, a hierarchical graph of all composite units is constructed. Lastly, a new dynamic reliability prediction algorithm is presented. Comparing with the traditional reliability model, the new dynamic reliability approach is more flexible, which does not recompute reliability for all composite units and only computes the reliability of the effected composite units. In addition, an example to show how to measure the reliability based on our algorithm is designed. The experimental results show our proposed methods can give an accurate estimation of reliability. Furthermore, a more flexible sensitivity analysis is performed to determine which service component has the most significant impact on the improvement of composite service reliability.

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Prediction of atomic web services reliability based on k-means clustering

Cited by 40 publications

References 29 publications

Hierarchical Matching of Traffic Information Services Using Semantic Similarity

Hierarchical Matching of Traffic Information Services Using Semantic Similarity

Location-Based Hierarchical Matrix Factorization for Web Service Recommendation

A Dynamical Reliability Prediction Algorithm for Composite Service

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