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Area of Science:

  • Computer Science
  • Data Management
  • Experimental Evaluation

Background:

  • Database research often uses open data, but replicating empirical results is challenging.
  • Even with accessible source code, reproducibility issues persist due to unaddressed factors.

Purpose of the Study:

  • To highlight the critical role of data preparation in experimental reproducibility.
  • To introduce a model and levels for assessing data reproducibility.
  • To provide best practices for enhancing data reproducibility in research.

Main Methods:

  • Analyzing the impact of data preparation on experimental results.
  • Developing a data reproducibility model.
  • Identifying distinct levels of data reproducibility.

Main Results:

  • Subtle differences in data preparation significantly alter runtime results.
  • A three-level framework for data reproducibility was established.
  • Practical experiences and best practices were documented.

Conclusions:

  • Raw datasets alone are insufficient for ensuring reproducibility.
  • Detailed documentation and standardization of data preparation are crucial.
  • The proposed model aids in achieving higher data reproducibility in database experiments.