Data mining algorithms for Web-services classification

Author

Mustafa, A. Syed ; Kumaraswamy, Y.S.

Author_Institution

Dept. of CSE, Sathyabama Univ., Chennai, India

fYear

2014

fDate

27-29 Nov. 2014

Firstpage

951

Lastpage

956

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Contemporary Computing and Informatics (IC3I), 2014 International Conference on

Conference_Location

Mysore

Type

conf

DOI

10.1109/IC3I.2014.7019644

Filename

7019644