On Flexible Web Services Composition Networks
📝 Original Info
- Title: On Flexible Web Services Composition Networks
- ArXiv ID: 1305.0688
- Date: 2015-02-04
- Authors: Researchers from original ArXiv paper
📝 Abstract
The semantic Web service community develops efforts to bring semantics to Web service descriptions and allow automatic discovery and composition. However, there is no widespread adoption of such descriptions yet, because semantically defining Web services is highly complicated and costly. As a result, production Web services still rely on syntactic descriptions, key-word based discovery and predefined compositions. Hence, more advanced research on syntactic Web services is still ongoing. In this work we build syntactic composition Web services networks with three well known similarity metrics, namely Levenshtein, Jaro and Jaro-Winkler. We perform a comparative study on the metrics performance by studying the topological properties of networks built from a test collection of real-world descriptions. It appears Jaro-Winkler finds more appropriate similarities and can be used at higher thresholds. For lower thresholds, the Jaro metric would be preferable because it detect less irrelevant relationships.💡 Deep Analysis
Deep Dive into On Flexible Web Services Composition Networks.The semantic Web service community develops efforts to bring semantics to Web service descriptions and allow automatic discovery and composition. However, there is no widespread adoption of such descriptions yet, because semantically defining Web services is highly complicated and costly. As a result, production Web services still rely on syntactic descriptions, key-word based discovery and predefined compositions. Hence, more advanced research on syntactic Web services is still ongoing. In this work we build syntactic composition Web services networks with three well known similarity metrics, namely Levenshtein, Jaro and Jaro-Winkler. We perform a comparative study on the metrics performance by studying the topological properties of networks built from a test collection of real-world descriptions. It appears Jaro-Winkler finds more appropriate similarities and can be used at higher thresholds. For lower thresholds, the Jaro metric would be preferable because it detect less irrelevant