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DecentralDC: Assessing data contribution under decentralized sharing and exchange blockchain.

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This study introduces DecentralDC, a novel method for assessing data contribution by analyzing behavior patterns. This approach enhances data pricing strategies, significantly reducing regret and improving data sharing efficiency.

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Area of Science:

  • Data Science
  • Information Systems
  • Computer Science

Background:

  • Data quality is crucial for effective data sharing and economic value, yet current assessments overlook data behavior in specific scenarios.
  • Existing data quality dimensions (intrinsic, contextual, presentational, accessibility) do not capture the dynamic participation of data.
  • Lack of behavioral analysis hinders optimization of resource deployment and business processes reliant on data.

Purpose of the Study:

  • To propose a novel data contribution assessment method, DecentralDC, that incorporates data behavior within application scenarios.
  • To address the limitations of current data quality assessment by considering the degree of data participation and support.
  • To improve data pricing mechanisms and overall data sharing efficiency.

Main Methods:

  • Developed DecentralDC, a three-step method: mining maximal sequential patterns of behavior paradigms, determining paradigm weights, and calculating data contribution.
  • Utilized sharing and exchange behavior paradigms to quantify data's role in specific scenarios.
  • Integrated data volume with the calculated contribution for a comprehensive assessment.

Main Results:

  • Experimental validation in two distinct scenarios demonstrated the effectiveness of the DecentralDC method.
  • Introduction of data contribution significantly reduced cumulative regret and regret rate in data pricing.
  • Achieved improvements of 6% and 8% in mean average precision compared to the leading baseline method.

Conclusions:

  • DecentralDC offers a robust approach to data contribution assessment by incorporating behavioral analysis.
  • The method effectively enhances data pricing strategies, leading to more efficient data sharing and resource allocation.
  • Open-sourced code and simulation scenarios facilitate further research and application of the proposed method.