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Abstract <p>The paper analyzes the limitations of conventional methods for assessing the maturity of technology, such as the <i>S</i>-curve, technology readiness level (TRL), Gartner’s hype cycle and their dependence on experts’ opinions. Current approaches to this task based on big text data analysis and machine learning algorithms are reviewed, and their advantages are demonstrated. As a result of existing research systematization, the prospects of transition to automated technology maturity assessment using machine learning methods are revealed.</p>

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Prospects for Big Text Data Application in Technology Maturity Assessment (Publications Review)

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • I. V. Loginova,
  • F. M. Grozovskiy,
  • A. S. Piekalnits

摘要

Abstract

The paper analyzes the limitations of conventional methods for assessing the maturity of technology, such as the S-curve, technology readiness level (TRL), Gartner’s hype cycle and their dependence on experts’ opinions. Current approaches to this task based on big text data analysis and machine learning algorithms are reviewed, and their advantages are demonstrated. As a result of existing research systematization, the prospects of transition to automated technology maturity assessment using machine learning methods are revealed.