Big Data—Knowledge Discovery in Production Industry Data Storages—Implementation of Best Practices

• Published in: Applied sciences 2021-08, Vol.11 (16), p.7648
• Main Authors: Abasova, Jela, Tanuska, Pavol, Rydzi, Stefan
• Format: Article
• Language: English
• Subjects: 19th century; Best practice; Bibliometrics; Big Data; Case studies; CRISP-DM; Data analysis; Data processing; Data storage; Datasets; Factories; Globalization; Industry 4.0; Industry standards; Internet of Things; knowledge discovery; Manufacturing; Methods; predictive maintenance; production industry; Scientists; Software; Sustainability; Trends; Virtual reality
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11167648

CRISP-DM (cross-industry standard process for data mining) methodology was developed as an intuitive tool for data scientists, to help them with applying Big Data methods in the complex technological environment of Industry 4.0. The review of numerous recent papers and studies uncovered that most of papers focus either on the application of existing methods in case studies, summarizing existing knowledge, or developing new methods for a certain kind of problem. Although all of these types of research are productive and required, we identified a lack of complex best practices for a specific field. Therefore, our goal is to propose best practices for the data analysis in production industry. The foundation of our proposal is based on three main points: the CRISP-DM methodology as the theoretical framework, the literature overview as an expression of current needs and interests in the field of data analysis, and case studies of projects we were directly involved in as a source of real-world experience. The results are presented as lists of the most common problems for selected phases (‘Data Preparation’ and ‘Modelling’), proposal of possible solutions, and diagrams for these phases. These recommendations can help other data scientists avoid certain problems or choose the best way to approach them.