Title :
Data mining algorithms for Web-services classification
Author :
Mustafa, A. Syed ; Kumaraswamy, Y.S.
Author_Institution :
Dept. of CSE, Sathyabama Univ., Chennai, India
Abstract :
Web services are software components that communicate using pervasive, standards-based Web technologies including HTTP and XML-based messaging. Web services are designed to be accessed by other applications and vary in complexity from simple operations, such as checking a banking account balance online, to complex processes running Customer Relationship Management (CRM) or Enterprise Resource Planning (ERP) systems. Since they are based on open standards such as HTTP and XML-based protocols including SOAP and WSDL, Web services are hardware, programming language, and operating system independent. In this paper, Naïve Bayes, C4.5 and Random forest methods are used as classifiers for the efficiency of web services classification.
Keywords :
Web services; data mining; learning (artificial intelligence); pattern classification; C4.5 method; CRM; ERP system; HTTP messaging; SOAP protocol; WSDL; Web services classification; XML-based messaging; customer relationship management; data mining algorithm; enterprise resource planning; naive Bayes method; random forest method; standards-based Web technology; Classification algorithms; Quality of service; Semantics; Standards; Vegetation; Web services; C4.5 and Random Forest; Naïve Bayes; QWS dataset; Web services;
Conference_Titel :
Contemporary Computing and Informatics (IC3I), 2014 International Conference on
Conference_Location :
Mysore
DOI :
10.1109/IC3I.2014.7019644
