A Web Service Recommendation Approach Based on QoS Prediction Using Fuzzy Clustering

Zhang, Meng; Liu, Xudong; Zhang, Richong; Sun, Hailong

doi:10.1109/scc.2012.24

Cited by 46 publications

(17 citation statements)

References 18 publications

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“…Ref. [59] proposes a web services' recommendation system which ranks web services based on their predicted QoS. The QoS prediction is performed based on its patterns.…”

Section: Non-functional-based Web Services Clusteringmentioning

confidence: 99%

Fuzzy-based Clustering of Web Services’ Quality of Service: A Review

Hasan¹,

Jaafar²,

Hassan³

2014

JCM

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Clustering has been proposed in numerous researches on web services due to its ability in enhancing computational efficiency. The objective of this paper is to present a review on web services clustering and to propose fuzzy clustering of web services' based on quality of service (QoS). A total of 49 manuscripts, collected from ISI, SCOPUS and Google Scholar indexing databases, were critically reviewed. The review revealed that there is no report has been published before regarding fuzzy-based clustering of web services' QoS. It also revealed the importance of clustering in ensuring the efficiency of web services delivery particularly in their discovery, selection and recommendation processes. The results of the proposed fuzzy clustering can be used as reference to guide requestors to subscribe suitable services. They could also be implemented as the inference components for fuzzybased web services applications. For future work, the generated clustering results will be implemented in web services QoS monitoring application. Index Terms-Clustering, fuzzy clustering, Fuzzy C-Means, QoS clustering, web services clustering.

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“…Ref. [59] proposes a web services' recommendation system which ranks web services based on their predicted QoS. The QoS prediction is performed based on its patterns.…”

Section: Non-functional-based Web Services Clusteringmentioning

confidence: 99%

Fuzzy-based Clustering of Web Services’ Quality of Service: A Review

Hasan¹,

Jaafar²,

Hassan³

2014

JCM

View full text Add to dashboard Cite

show abstract

“…Another group of researchers were considering on the non-functional aspects such as web service selection and recommendation based on quality of service (QoS) [10], customer ratings [11] or service popularity [12]. However, the non-functional information is also always not available and these methods can only serve as a complement of service discovery.…”

Section: A Service Discoverymentioning

confidence: 99%

A Probabilistic Approach for Web Service Discovery

Zhang

Huai

et al. 2013

2013 IEEE International Conference on Services Computing

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Web service discovery is a vital problem in service computing with the increasing number of services. Existing service discovery approaches merely focus on WSDLbased keyword search, semantic matching based on domain knowledge or ontologies, or QoS-based recommendations. The keyword search omits the underlying correlations and semantic knowledge or QoS information is not always available. In this paper, we propose a probabilistic service discovery approach to help web service users to retrieve related services and to improve the search performance. Specifically, we apply a probabilistic model to characterize the latten topics between services and queries, and then propose a matching method based on the topic relevance. Experiments on services from a real service repository confirm the feasibility and efficiency of this proposed method.

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“…Similar to comparisons in other literature, we compare our method with common prediction methods like use-based algorithm using PCC (UPCC), item-based algorithm using PCC (IPCC), usermean (UMEAN) and item-mean (IMEAN) to evaluate its prediction performance. Algorithm details of UPCC, IPCC, UMEAN and IMEAN can be found in [3,4,5,13], and we do not repeat here. We vary the number of convergent users as 1, 3, 5 and 8 at the beginning of the recommendation process to study the prediction performance.…”

Section: Qos Value Prediction Accuracy Evaluationmentioning

confidence: 99%

“…QoS is a broad concept that encompasses a number of properties such as price, availability, reliability, and reputation [2]. There are several approaches [3,4,5] that use contextual information (e.g., location, time) for collaborative filtering to predict QoS values of Web services and have achieved accuracy improvement.…”

Section: Introductionmentioning

confidence: 99%

A Web Service Recommendation Approach Based on Situation Awareness

Liu

Lin

Xiong

2013

2013 IEEE International Conference on Services Computing

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With the development of Service-Oriented Technologies, the amount of Web services grows rapidly. Situation awareness, as a very important computing paradigm which can provide more reasonable and complete representation of the user's environment, can be benefit to the discovering of the service user's requirement. Therefore in this paper, we explore to use the situational information to recommend services which satisfy the requirement of users better and meanwhile improve the QoS prediction accuracy. Both the convergences between users' service selections in different situations and their QoS experiences have been taken into account. Moreover, we investigate the potential relationships between the situations structure and the services selection. Experiment results indicate that our method achieves ideal performance.

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A Web Service Recommendation Approach Based on QoS Prediction Using Fuzzy Clustering

Cited by 46 publications

References 18 publications

Fuzzy-based Clustering of Web Services’ Quality of Service: A Review

Fuzzy-based Clustering of Web Services’ Quality of Service: A Review

A Probabilistic Approach for Web Service Discovery

A Web Service Recommendation Approach Based on Situation Awareness

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