Web Service Community Discovery Based on Spectrum Clustering

Zhang, Xizhe; Yin, Ying; Zhang, Mingwei; Bin, Zhang

doi:10.1109/cis.2009.162

Cited by 13 publications

(9 citation statements)

References 9 publications

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“…1. Clustering is the task of grouping a set of objects in such a way that objects in the same cluster are more similar to each other than to those in other clusters [14].…”

Section: Density Based Clusteringmentioning

confidence: 99%

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Density Based Clustering Approach for Efficient Webservices Discovery

Rajeswari¹,

K.²

2022

INDJCSE

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A huge number of Web Services accessible on the Internet has made service discovery a challenging task. Efficient way to minimise the search space is by organising Web Services into similar clusters. This paper presents an efficient Web Service Discovery approach based on Density Based (DB) clustering algorithm. Word frequency-based similarity calculation method in word score calculation is proposed. Clusters are formed using k-means and redefined using DB algorithm. The results obtained from DB is high accurate clusters of WSDL. The performance of the proposed DB approach is evaluated by comparing the accuracy of the service discovery with the existing results and found that DB outperforms than the others.

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“…1. Clustering is the task of grouping a set of objects in such a way that objects in the same cluster are more similar to each other than to those in other clusters [14].…”

Section: Density Based Clusteringmentioning

confidence: 99%

“…The algorithm iterates these steps until all centre points remain unchanged. The capability of a web service search engine to fetch the closely related service would enhance by area defined clustering of web services [14].…”

Section: Introductionmentioning

confidence: 99%

Density Based Clustering Approach for Efficient Webservices Discovery

Rajeswari¹,

K.²

2022

INDJCSE

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“…In addition, a few other works also present the web services functional-based clustering, which include the clustering based on trust [52], registries [53], WSDL documents [54], task objects [55], geotaggedphotos [56] and clustering to solve web services discovery on MANET [57], [58].…”

Section: B 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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“…With the rapid development of the Internet, a variety of Web services is gradually known and applied, and the myriad of Web services is released every day to meet the needs of people. It is very necessary to manage Web services through applications which are based Web services such as service discovery [2], [3], service composition [4], [5], and so on. Hence, how to find suitable Web services effectively has become one of the core problems in the field of service-oriented computing.…”

Section: Introductionmentioning

confidence: 99%

Web Services Classification Based on Wide & Bi-LSTM Model

Cao

Peng

et al. 2019

IEEE Access

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With the rapid growth of Web services on the Internet, it becomes a great challenge for Web services discovery. Classifying Web services with similar functions is an effective method for service discovery and management. However, the functional description documents of Web services usually are short in their length, with sparse features and less information, which makes most topic models unable to model the short text well, consequently affecting the Web service classification. To solve this problem, a Web service classification method based on Wide & Bi-LSTM model is proposed in this paper. In this method, first, all the discrete features in the description documents of Web services are combined to perform the breadth prediction of Web service category by exploiting the wide learning model. Second, the word order and context information of the words in the description documents of Web services are mined by using the Bi-LSTM model to perform the depth prediction of the Web service category. Third, it uses the linear regression algorithm to integrate the breadth and depth prediction results of Web service categories as the final result of the service classification. Finally, compared with six Web service classification methods based on TF-IDF, LDA, WE-LDA, LSTM, Wide&Deep, and Bi-LSTM, respectively, the experimental results show that our approach achieves a better performance in the accuracy of Web service classification.INDEX TERMS Wide learning model, Bi-LSTM model, linear regression, web service classification.

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Web Service Community Discovery Based on Spectrum Clustering

Cited by 13 publications

References 9 publications

Density Based Clustering Approach for Efficient Webservices Discovery

Density Based Clustering Approach for Efficient Webservices Discovery

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

Web Services Classification Based on Wide & Bi-LSTM Model

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