Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing
This addresses the problem of ensuring model robustness and reliability for practitioners in production and manufacturing, but it appears incremental as it reviews and extends existing frameworks.
The paper tackles the challenge of implementing robust and reliable data-driven knowledge discovery models in production and manufacturing by proposing a systematic approach based on the CRISP-DM framework, but it does not report any concrete results or numbers.
The implementation of robust, stable, and user-centered data analytics and machine learning models is confronted by numerous challenges in production and manufacturing. Therefore, a systematic approach is required to develop, evaluate, and deploy such models. The data-driven knowledge discovery framework provides an orderly partition of the data-mining processes to ensure the practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data-- and model-development--related issues. These issues should be carefully addressed by allowing a flexible, customized, and industry-specific knowledge discovery framework; in our case, this takes the form of the cross-industry standard process for data mining (CRISP-DM). This framework is designed to ensure active cooperation between different phases to adequately address data- and model-related issues. In this paper, we review several extensions of CRISP-DM models and various data-robustness-- and model-robustness--related problems in machine learning, which currently lacks proper cooperation between data experts and business experts because of the limitations of data-driven knowledge discovery models.