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Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
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On the Discovery of Evolving Truth.

Yaliang Li1, Qi Li1, Jing Gao1

  • 1SUNY Buffalo, Buffalo, NY USA.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|December 26, 2015
PubMed
Summary

This study introduces an incremental truth discovery framework to handle evolving data. It dynamically updates object truths and source reliability for improved accuracy in big data scenarios.

Keywords:
Dynamic DataSource ReliabilityTruth Discovery

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Area of Science:

  • Data Science
  • Information Science
  • Computer Science

Background:

  • Big data presents challenges with conflicting information from multiple sources.
  • Truth discovery methods integrate multi-source data by assessing source reliability.
  • Existing methods struggle with dynamically evolving truths and source reliability over time.

Purpose of the Study:

  • To address the limitations of existing truth discovery methods in handling sequential and evolving data.
  • To propose an incremental framework for dynamic truth discovery.
  • To investigate temporal relations in object truths and source reliability.

Main Methods:

  • Developed an incremental truth discovery framework.
  • Incorporated temporal dynamics of object truths and source reliability.
  • Designed a method for dynamically updating object truths and source weights with new data arrivals.

Main Results:

  • The proposed framework dynamically updates object truths and source weights.
  • Theoretical analysis guarantees fast convergence rates.
  • Experiments show advantages over state-of-the-art truth discovery methods on real-world and synthetic data.

Conclusions:

  • The incremental truth discovery framework effectively handles evolving data.
  • The method provides a robust solution for dynamic truth discovery in big data.
  • The approach demonstrates superior performance compared to existing methods.