Workflow Discovery

2011 ◽  
pp. 465-491
Author(s):  
Antoon Goderis ◽  
Peter Li ◽  
Carole Goble
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Web Service Discovery ◽  
Design And Implementation ◽  
Tool Evaluation ◽  
Automated Composition ◽  
User Experiment

Much has been written on the promise of Web service discovery and (semi-) automated composition. In this discussion, the value to practitioners of discovering and reusing existing service compositions, captured in workflows, is mostly ignored. We present the case for workflows and workflow discovery in science and develop one discovery solution. Through a survey with 21 scientists and developers from the myGrid/Taverna workflow environment, workflow discovery requirements are elicited. Through a user experiment with 13 scientists, an attempt is made to build a benchmark for workflow ranking. Through the design and implementation of a workflow discovery tool, a mechanism for ranking workflow fragments is provided based on graph sub-isomorphism detection. The tool evaluation, drawing on a corpus of 89 public workflows and the results of the user experiment, finds that, for a simple showcase, the average human ranking can largely be reproduced.

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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