XML-Relational mapping using production rule system

Abstract

The most efficient information systems are based on the relational data model. extensible Markup Language (XML) structures are machine- and human-readable. XML data can be easily analyzed and exchanged between different systems. Though, XML data is simply understandable, it has hierarchical model, which is quite redundant. Modern systems frequently apply various methods of XML-Relational Database Management System (RDBMS) transformation that assume different degrees of data compression and processing time of the method. However, the existing methods are not always effective. This paper defines new rules of XML-relational mapping, which provides more efficient data representation in both models, and describes the method based on them that lies upon the basic rules-driven principles of artificial intelligence and production system. This method is applied in learning management system of ITMO University, called AcademicNT, which contains a huge amount of educational data and materials.