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Adjustable Data Cleaning Towards Extracting Statistical Information.

Argyro Mavrogiorgou1, Athanasios Kiourtis1, George Manias1

  • 1Department of Digital Systems, University of Piraeus, Greece.

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This study introduces a Data Cleaning method to handle Big Data challenges. The approach filters irrelevant information, improving analysis for crucial areas like national healthcare policy.

Keywords:
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Area of Science:

  • Data Science
  • Public Health
  • Computational Social Science

Background:

  • Big Data presents significant analytical challenges across various sectors.
  • Effective data analysis is crucial for informed decision-making and policy development.
  • Existing methods may struggle with the volume and complexity of modern datasets.

Purpose of the Study:

  • To propose and evaluate a novel Data Cleaning approach.
  • To address the challenge of filtering non-important data within large datasets.
  • To assess the utility of enhanced data for policy-making in sensitive areas.

Main Methods:

  • Development of a specific Data Cleaning algorithm.
  • Application and testing of the algorithm on Global Terrorism Data.
  • Comparative analysis of data before and after cleaning.

Main Results:

  • The proposed Data Cleaning method effectively filters non-essential data.
  • Improved data quality facilitates more accurate subsequent analyses.
  • The cleaned dataset provides a clearer picture of terrorism's impact.

Conclusions:

  • Data Cleaning is a vital step in Big Data analysis.
  • This method enhances the understanding of complex issues like terrorism's effect on national healthcare.
  • The findings support the development of data-driven policies.