Applying Cosine Series to XML Structural Join Size Estimation

As XML has become the de facto standard for data presentation and exchanging on the Web, XML query optimization has emerged as an important research issue. It is widely accepted that structural joins, which evaluate the containment (ancestor-descendant) relationships between XML elements, are import...

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Bibliographic Details
Published inDatabase and Expert Systems Applications pp. 761 - 770
Main Authors Luo, Cheng, Jiang, Zhewei, Hou, Wen-Chi, Zhu, Qiang, Wang, Chih-Fang
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Ancestor Node
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Cosine Series
Descendant Node
Disk Access
Exact sciences and technology
Information systems. Data bases
Memory organisation. Data processing
Path Expression
Software
Tree data structures
Structural join
Database query
Expert system
XML language
Optimization
Cosine transform
Mathematical function
Query processing
World wide web
Database
Internet
Artificial intelligence
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Summary:As XML has become the de facto standard for data presentation and exchanging on the Web, XML query optimization has emerged as an important research issue. It is widely accepted that structural joins, which evaluate the containment (ancestor-descendant) relationships between XML elements, are important to the XML query processing. Estimating structural join size accurately and quickly thus becomes crucial to the success of XML query plan selection. In this paper, we propose to apply Cosine transform to structural join size estimation. Our approach captures structural information of XML data using mathematical functions, which are then approximated by the Cosine series. We derive a simple formula to estimate the structural join size using the Cosine series. Theoretical analyses and extensive experiments have been performed. The experimental results show that, compared with state-of-the-art IM-DA-Est method, our method is several order faster, requires less memory, and yields better or comparable estimates.
ISBN:3540378715
9783540378716
ISSN:0302-9743
1611-3349
DOI:10.1007/11827405_74