Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-17616-6_41
Title: Semantic transformation approach with schema constraints for XPath query axes
Authors: Le, D.X.T.
Bressan, S. 
Pardede, E.
Rahayu, W.
Taniar, D.
Keywords: Query Processing
Semantic XML Query Optimization
XML
XPath
Issue Date: 2010
Source: Le, D.X.T.,Bressan, S.,Pardede, E.,Rahayu, W.,Taniar, D. (2010). Semantic transformation approach with schema constraints for XPath query axes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6488 LNCS : 456-470. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-17616-6_41
Abstract: XPath queries are essentially composed of a succession of axes defining the navigation from a current context node. Among the XPath query axes family, child, descendant, parent can be optionally specified using the path notations {/,//,..} which have been commonly used. Axes such as following-sibling and preceding-sibling have unique functionalities which provide different required information that cannot be achieved by others. However, XPath query optimization using schema constraints does not yet consider these axes family. The performance of queries denoting the same result by means of different axes may significantly differ. The difference in performance can be affected by some axes, but this can be avoided. In this paper, we propose a semantic transformation typology and algorithms that transform XPath queries using axes, with no optional path operators, into semantic equivalent XPath queries in the presence of an XML schema. The goal of the transformation is to replace whenever possible the axes that unnecessarily impact upon performance. We show how, by using our semantic transformation, the accessing of the database using such queries can be avoided in order to boost performance. We implement the proposed algorithms and empirically evaluate their efficiency and effectiveness as semantic query optimization devices. © 2010 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41741
ISBN: 3642176151
ISSN: 03029743
DOI: 10.1007/978-3-642-17616-6_41
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