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Data Cleaning Model for XML Datasets using Conditional Dependencies

Author

Listed:
  • Mohammed Ragheb Hakawati

    (CETIC Research Centre, Belgium.)

  • Yasmin Yacob

    (University Malaysia Perlis, Malaysia.)

  • Rafikha Aliana A. Raof

    (University Malaysia Perlis, Malaysia.)

  • Mustafa M.Khalifa Jabiry

    (Management & Science University (MSU), Malaysia.)

  • Eiad Syaf Alhudiani

    (Whysie information Technology, Amman, Jordan.)

Abstract

Data Cleaning as an essential phase to enhance the overall quality used for decades with different data models, the majority handled a relational dataset as the most dominant data model. However, the XML data model, besides the relational data model considered the most data model commonly used for storing, retrieving, and querying valuable data. In this paper, we introduce a model for detecting and repairing XML data inconsistencies using a set of conditional dependencies. Detecting inconsistencies will be done by joining the existed data source with a set of patterns tableaus as conditional dependencies and then update these values to match the proper patterns using a set of SQL statements. This research considered the final phase for a cleaning model introduced for XML datasets by firstly mapping the XML document to a set of related tables then discovering a set of conditional dependencies (Functional and Inclusions) and finally then applying the following algorithms as a closing step of quality enhancement.

Suggested Citation

  • Mohammed Ragheb Hakawati & Yasmin Yacob & Rafikha Aliana A. Raof & Mustafa M.Khalifa Jabiry & Eiad Syaf Alhudiani, 2020. "Data Cleaning Model for XML Datasets using Conditional Dependencies," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(1), January.
    • Handle: RePEc:epw:ejece0:v:4:y:2020:i:1:id:19163
    DOI: 10.24018/ejece.2020.4.1.163
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