Realization of a system of efficient querying of hierarchical data transformed into a quasi-relational model
Author(s) -
Adam Furmanek,
Jakub Tokaj,
R. W. Marcjan,
Leszek Siwik
Publication year - 2016
Publication title -
computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.145
H-Index - 5
eISSN - 2300-7036
pISSN - 1508-2806
DOI - 10.7494/csci.2016.17.3.353
Subject(s) - computer science , xquery , xpath , xml database , sql , relational database , xslt , programming language , streaming xml , xml , information retrieval , database , world wide web
Extensible Markup Language was mainly designed to easily represent documents; however, it has evolved and is now widely used for the representation of arbitrary data structures. There are many Application Programming Interfaces (APIs) to aid software developers with processing XML data. There are also many languages for querying and transforming XML, such as XPath or XQuery, which are widely used in this field. However, because of the great flexibility of XML documents, there are no unified data storing and processing standards, tools, or systems. On the other hand, a relational model is still the most-commonly and widely used standard for storing and querying data. Many Database Management Systems consist of components for loading and transforming hierarchical data. DB2 pureXML or Oracle SQLX are some of the most-recognized examples. Unfortunately, all of them require knowledge of additional tools, standards, and languages dedicated to accessing hierarchical data (for example, XPath or XQuery). Transforming XML documents into a (quasi)relational model and then querying (transformed) documents with SQL or SQL–like queries would significantly simplify the development of data-oriented systems and applications. In this paper, an implementation of the SQLxD query system is proposed. The XML documents are converted into a quasi-relational model (preserving their hierarchical structure), and the SQL–like language based on SQL-92 allows for efficient data querying.
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