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Performance Enhancement of XML Parsing Using Regression and Parallelism.

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Title: Performance Enhancement of XML Parsing Using Regression and Parallelism.
Authors: Ali, Muhammad1, Khan, Minhaj Ahmad1 mik@bzu.edu.pk
Source: Computer Systems Science & Engineering. 2024, Vol. 48 Issue 2, p287-303. 17p.
Subjects: XML (Extensible Markup Language), Document Object Model (Web development technology), Algorithms, Regression analysis, Prediction models
Abstract: The Extensible Markup Language (XML) files, widely used for storing and exchanging information on the web require efficient parsingmechanisms 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. [ABSTRACT FROM AUTHOR]
Copyright of Computer Systems Science & Engineering is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22XML+%28Extensible+Markup+Language%29%22">XML (Extensible Markup Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Document+Object+Model+%28Web+development+technology%29%22">Document Object Model (Web development technology)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
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  Data: The Extensible Markup Language (XML) files, widely used for storing and exchanging information on the web require efficient parsingmechanisms 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Systems Science & Engineering is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.32604/csse.2023.043010
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      – Code: eng
        Text: English
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        PageCount: 17
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      – SubjectFull: XML (Extensible Markup Language)
        Type: general
      – SubjectFull: Document Object Model (Web development technology)
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Regression analysis
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      – SubjectFull: Prediction models
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      – TitleFull: Performance Enhancement of XML Parsing Using Regression and Parallelism.
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              Text: 2024
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