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FDTool: a Python application to mine for functional dependencies and candidate keys in tabular data.

Matt Buranosky1, Elmar Stellnberger2, Emily Pfaff3

  • 1National Health and Environmental Effects Research Laboratory, United States Environmental Protection Agency, Chapel Hill, NC, USA.

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|May 10, 2019
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Summary
This summary is machine-generated.

FDTool discovers minimal functional dependencies (FDs) and candidate keys in datasets. The number of attributes significantly impacts performance more than row count for FD discovery.

Keywords:
Data miningElectronic health recordsFDToolFunctional dependenciesRelational databaseRule discovery

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Area of Science:

  • Database Management
  • Data Mining
  • Algorithm Analysis

Background:

  • Functional dependencies (FDs) and candidate keys are crucial for database normalization, decomposition, and data cleansing.
  • Efficient algorithms are needed to discover these structures in large tabular datasets.
  • Existing research highlights FD_Mine as efficient for long, narrow datasets.

Purpose of the Study:

  • To introduce FDTool, a command-line Python application for discovering minimal FDs and inferring candidate keys.
  • To analyze the performance of FD discovery algorithms, focusing on FDTool's re-implementation of FD_Mine.
  • To provide insights into the practical application and development of FD discovery tools.

Main Methods:

  • FDTool implements and enhances the FD_Mine algorithm for discovering minimal FDs.
  • The study analyzes runtime and memory costs of seven FD discovery algorithms.
  • Experimental evaluation of FDTool on 13 diverse datasets was conducted.

Main Results:

  • FDTool effectively discovers minimal FDs and infers candidate keys.
  • The number of attributes in a dataset has a greater impact on FDTool's performance than the number of rows.
  • FDTool's performance and features are detailed, along with its accessibility and development path.

Conclusions:

  • FDTool is a valuable tool for database architecture tasks, particularly for datasets with many attributes.
  • The study provides a comprehensive performance analysis of FD discovery algorithms.
  • FDTool offers improved performance and automation for discovering functional dependencies and candidate keys.