Universal Knowledge Discovery from Big Data: Towards a Paradigm Shift from 'Knowledge Discovery' to 'Wisdom Discovery'

TL;DR

This paper introduces the concept of universal knowledge discovery from big data, emphasizing the shift from traditional knowledge discovery to extracting deep, valuable insights and patterns that can inform scientific understanding.

Contribution

It proposes the UKD paradigm and the iBEST@SEE methodology to systematically uncover universal knowledge in big data, integrating techniques from data mining and complex systems science.

Findings

Defines universal knowledge as patterns, rules, correlations, models, mechanisms

Proposes a unified paradigm for big data knowledge discovery

Lays foundation for future research in universal knowledge extraction

Abstract

Many people hold a vision that big data will provide big insights and have a big impact in the future, and big-data-assisted scientific discovery is seen as an emerging and promising scientific paradigm. However, how to turn big data into deep insights with tremendous value still remains obscure. To meet the challenge, universal knowledge discovery from big data (UKD) is proposed. The new concept focuses on discovering universal knowledge, which exists in the statistical analyses of big data and provides valuable insights into big data. Universal knowledge comes in different forms, e.g., universal patterns, rules, correlations, models and mechanisms. To accelerate big data assisted universal knowledge discovery, a unified research paradigm should be built based on techniques and paradigms from related research domains, especially big data mining and complex systems science. Therefore, I propose an iBEST@SEE methodology. This study lays a solid foundation for the future development of universal knowledge discovery, and offers a pathway to the discovery of "treasure-trove" hidden in big data.