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Efficient processing of complex XSD using Hive and Spark.

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Summary
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This study introduces a novel approach for processing complex eXtensible Markup Language (XML) files using Big Data tools like Apache Hive and Apache Spark. The method effectively handles real-world data, offering insights into mobile network performance management.

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Complex XSDHiveMobile networkPerformance managementSparkXML

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Area of Science:

  • Computer Science
  • Data Engineering
  • Big Data Analytics

Background:

  • eXtensible Markup Language (XML) files are prevalent across industries, including finance, social networks, and mobile networks, often featuring complex schemas.
  • Existing Big Data tools like Apache Hive and Apache Spark are used for XML parsing, but often focus on simpler schemas, overlooking real-world complexity.
  • There is a need for efficient methods to process large-scale, complex XML data within Big Data environments.

Purpose of the Study:

  • To present a novel approach for processing complex XML schemas using Big Data tools.
  • To demonstrate the effectiveness of this approach on real-world datasets, specifically from mobile network performance management.
  • To provide a comparative analysis of processing performance between Apache Hive and Apache Spark.

Main Methods:

  • Developed a three-technique approach: cataloging, deserialization, and positional explode for XML parsing.
  • Cataloging involves mapping XML schema elements into categories: root, arrays, structures, values, and attributes.
  • Deserialization and positional explode methods are implemented based on the cataloged elements.

Main Results:

  • Validated the proposed method across different versions of Apache Hive and Apache Spark.
  • Measured query execution times for Apache Hive internal/external tables and Apache Spark DataFrames.
  • Provided a performance comparison between Apache Hive and Apache Spark for complex XML data processing.

Conclusions:

  • The proposed method is effective for processing complex XML schemas with Big Data tools.
  • The study offers a practical solution for data analysis in mobile network performance management systems.
  • The findings contribute to optimizing Big Data processing of intricate XML datasets.