Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Waeal J. Obidallah ◽  
Bijan Raahemi ◽  
Umar Ruhi
Keyword(s):  
Literature Review ◽  
Web Service ◽  
Association Rules ◽  
Systematic Literature Review ◽  
Service Discovery ◽  
Web Service Discovery

Web Service Discovery Approaches for Dynamic Mobile Environment

2021 ◽  
Vol 13 (4) ◽  
pp. 16-38
Author(s):  
Salisu Garba ◽  
Radziah Mohamad ◽  
Nor Azizah Saadon
Keyword(s):  
Literature Review ◽  
Web Service ◽  
Systematic Literature Review ◽  
Service Discovery ◽  
Research Trends ◽  
Mobile Environment ◽  
Essential Information ◽  
Mobile Web ◽  
Web Service Discovery ◽  
Depth Analysis

Mobile web service (MWS) discovery is taking a new direction due to the explosion of users accessing mobile services using diverse mobile devices, coupled with the persistent changes in a dynamic mobile environment (DME). This leads to renewed adoption of lightweight solutions for the identification of the most suitable web services that correspond with the service requests. Contemporary mobile web service discovery approaches are plagued with performance and accuracy problems and are rarely compatible with the DME. The objective of this systematic literature review is to develop a more rigorous understanding and identify recent research trends in mobile web service discovery techniques in a dynamic mobile environment. This review followed the systematic literature review (SLR) guidelines. Essential information was extracted from the 76 relevant articles in line with the formulated questions and finally reported after in-depth analysis. The results of this study discuss the critical contributions and limitations of the proposed approaches.

A Study on Different Web Service Discovery Approaches

2018 ◽  
Vol 6 (9) ◽  
pp. 311-314
Author(s):  
Rahul P. Mirajkar ◽  
Nikhil D. Karande ◽  
Surendra Yadav
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Web Service Discovery

Incorporating LDA With Word Embedding for Web Service Clustering

2018 ◽  
Vol 15 (4) ◽  
pp. 29-44 ◽  
Author(s):  
Yi Zhao ◽  
Chong Wang ◽  
Jian Wang ◽  
Keqing He
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Word Embedding ◽  
The Internet ◽  
Word Embeddings ◽  
Training Process ◽  
Web Service Discovery ◽  
Processing Data ◽  
Clustering Approach ◽  
Service Clustering

With the rapid growth of web services on the internet, web service discovery has become a hot topic in services computing. Faced with the heterogeneous and unstructured service descriptions, many service clustering approaches have been proposed to promote web service discovery, and many other approaches leveraged auxiliary features to enhance the classical LDA model to achieve better clustering performance. However, these extended LDA approaches still have limitations in processing data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering. Especially, the semantically relevant words of service keywords by Word2vec were used to train the word embeddings and then incorporated into the LDA training process. Finally, experiments conducted on a real-world dataset published on ProgrammableWeb show that the authors' proposed approach can achieve better clustering performance than several classical approaches.

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