Calculating word similarity for context aware web service clustering

2013 ◽  
Author(s):  
Hiroki Ohashi ◽  
Incheon Paik ◽  
Banage T. G. S. Kumara
Keyword(s):  
Web Service ◽  
Context Aware ◽  
Word Similarity ◽  
Service Clustering

Context Aware Post-filtering for Web Service Clustering

2014 ◽  
Author(s):  
Banage T. G. S. Kumara ◽  
Incheon Paik ◽  
Hiroki Ohashi ◽  
Wuhui Chen ◽  
Koswatte R. C. Koswatte
Keyword(s):  
Web Service ◽  
Context Aware ◽  
Service Clustering

Context-Aware Web Service Clustering and Visualization

2020 ◽  
Vol 17 (4) ◽  
pp. 32-54
Author(s):  
Banage T. G. S. Kumara ◽  
Incheon Paik ◽  
Yuichi Yaguchi
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Search Space ◽  
Context Aware ◽  
Specific Context ◽  
Web Service Discovery ◽  
Domain Specific ◽  
Clustering Approach ◽  
Service Clustering

With the large number of web services now available via the internet, web service discovery has become a challenging and time-consuming task. Organizing web services into similar clusters is a very efficient approach to reducing the search space. A principal issue for clustering is computing the semantic similarity between services. Current approaches do not consider the domain-specific context in measuring similarity and this has affected their clustering performance. This paper proposes a context-aware similarity (CAS) method that learns domain context by machine learning to produce models of context for terms retrieved from the web. To analyze visually the effect of domain context on the clustering results, the clustering approach applies a spherical associated-keyword-space algorithm. The CAS method analyzes the hidden semantics of services within a particular domain, and the awareness of service context helps to find cluster tensors that characterize the cluster elements. Experimental results show that the clustering approach works efficiently.

scholarly journals Context aware benchmarking and tuning of a TByte-scale air quality database and web service

2021 ◽  
Author(s):  
Clara Betancourt ◽  
Björn Hagemeier ◽  
Sabine Schröder ◽  
Martin G. Schultz
Keyword(s):  
Air Quality ◽  
Web Service ◽  
Statistical Processing ◽  
Quality Metrics ◽  
Context Aware ◽  
Near Surface ◽  
On Demand ◽  
Air Quality Measurements ◽  
Present Context ◽  
And Performance

AbstractWe present context-aware benchmarking and performance engineering of a mature TByte-scale air quality database system which was created by the Tropospheric Ozone Assessment Report (TOAR) and contains one of the world’s largest collections of near-surface air quality measurements. A special feature of our data service https://join.fz-juelich.de is on-demand processing of several air quality metrics directly from the TOAR database. As a service that is used by more than 350 users of the international air quality research community, our web service must be easily accessible and functionally flexible, while delivering good performance. The current on-demand calculations of air quality metrics outside the database together with the necessary transfer of large volume raw data are identified as the major performance bottleneck. In this study, we therefore explore and benchmark in-database approaches for the statistical processing, which results in performance enhancements of up to 32%.

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.

Context-Aware Web Service Development

2010 ◽  
pp. 3-29 ◽  
Author(s):  
Georgia Kapitsaki ◽  
George Prezerakos ◽  
Nikolaos Tselikas
Keyword(s):  
Web Service ◽  
Service Development ◽  
Context Aware
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