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Big data (BD) claims require revision. Merging BD with big theory can create a new scientific paradigm to address complex systems challenges like nonlinearity and hyperdimensions.

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Area of Science:

  • Complex systems science
  • Scientific methodology
  • Data science

Background:

  • The modern scientific method, established by Galileo, faces limitations with complex systems.
  • Challenges include nonlinearity, non-locality, and hyperdimensions in multi-scale modeling.
  • Extravagant claims of big data (BD) need reevaluation.

Purpose of the Study:

  • To revise and temper the ambitious claims of big data.
  • To explore the potential of integrating big data with big theory.
  • To propose a new scientific paradigm for studying complex systems.

Main Methods:

  • Analysis of lessons from complex systems science.
  • Critique of current big data methodologies.
  • Conceptual framework for synergistic merging of big data and big theory.

Main Results:

  • Discarding unrealistic big data claims is necessary.
  • A combined approach of big data and big theory offers significant potential.
  • This synergy can overcome barriers in the scientific method.

Conclusions:

  • Revising big data's scope is crucial for scientific advancement.
  • Integrating big data with theoretical frameworks can foster a new scientific paradigm.
  • This integrated approach is vital for tackling complex systems research.