Performance Enhancement of XML Parsing Using Regression and Parallelism  

作　　者：Muhammad Ali Minhaj Ahmad Khan 

机构地区：[1]Department of Computer Science,Bahauddin Zakariya University,Multan,60000,Pakistan

出　　处：《Computer Systems Science & Engineering》2024年第2期287-303,共17页计算机系统科学与工程（英文）

摘　　要：The Extensible Markup Language(XML)files,widely used for storing and exchanging information on the web require efficient parsing mechanisms to improve the performance of the applications.With the existing Document Object Model(DOM)based parsing,the performance degrades due to sequential processing and large memory requirements,thereby requiring an efficient XML parser to mitigate these issues.In this paper,we propose a Parallel XML Tree Generator(PXTG)algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework(RXPF)that analyzes and predicts performance through profiling,regression,and code generation for efficient parsing.The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel.The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX,SAX,DOM,JDOM,and PXTG on different cores by using multiple file sizes.The regression phase produces the prediction model,based on which the final code for efficient parsing of XML files is produced through the code generation phase.The RXPF framework has shown a significant improvement in performance varying from 9.54%to 32.34%over other existing models used for parsing XML files.

关 键 词：Regression parallel parsing multi-cores XML 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]