Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy
The Fourth Industrial Revolution, also named Industry 4.0, is leveraging several modern computing fields. Industry 4.0 comprises automated tasks in manufacturing facilities, which generate massive quantities of data through sensors. These data contribute to the interpretation of industrial operation...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/11/5010 |
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| author | Helder Moreira Arruda Rodrigo Simon Bavaresco Rafael Kunst Elvis Fernandes Bugs Giovani Cheuiche Pesenti Jorge Luis Victória Barbosa |
| author_facet | Helder Moreira Arruda Rodrigo Simon Bavaresco Rafael Kunst Elvis Fernandes Bugs Giovani Cheuiche Pesenti Jorge Luis Victória Barbosa |
| author_sort | Helder Moreira Arruda |
| collection | DOAJ |
| description | The Fourth Industrial Revolution, also named Industry 4.0, is leveraging several modern computing fields. Industry 4.0 comprises automated tasks in manufacturing facilities, which generate massive quantities of data through sensors. These data contribute to the interpretation of industrial operations in favor of managerial and technical decision-making. Data science supports this interpretation due to extensive technological artifacts, particularly data processing methods and software tools. In this regard, the present article proposes a systematic literature review of these methods and tools employed in distinct industrial segments, considering an investigation of different time series levels and data quality. The systematic methodology initially approached the filtering of 10,456 articles from five academic databases, 103 being selected for the corpus. Thereby, the study answered three general, two focused, and two statistical research questions to shape the findings. As a result, this research found 16 industrial segments, 168 data science methods, and 95 software tools explored by studies from the literature. Furthermore, the research highlighted the employment of diverse neural network subvariations and missing details in the data composition. Finally, this article organized these results in a taxonomic approach to synthesize a state-of-the-art representation and visualization, favoring future research studies in the field. |
| first_indexed | 2024-03-11T02:58:02Z |
| format | Article |
| id | doaj.art-c8be95464a31424481618d7534d05711 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:58:02Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c8be95464a31424481618d7534d057112023-11-18T08:31:18ZengMDPI AGSensors1424-82202023-05-012311501010.3390/s23115010Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and TaxonomyHelder Moreira Arruda0Rodrigo Simon Bavaresco1Rafael Kunst2Elvis Fernandes Bugs3Giovani Cheuiche Pesenti4Jorge Luis Victória Barbosa5Applied Computing Graduate Program, University of Vale do Rio dos Sinos, 950, Unisinos Av., São Leopoldo 93022-000, RS, BrazilApplied Computing Graduate Program, University of Vale do Rio dos Sinos, 950, Unisinos Av., São Leopoldo 93022-000, RS, BrazilApplied Computing Graduate Program, University of Vale do Rio dos Sinos, 950, Unisinos Av., São Leopoldo 93022-000, RS, BrazilHT Micron Semiconductors S.A., 1550, Unisinos Av., São Leopoldo 93022-750, RS, BrazilHT Micron Semiconductors S.A., 1550, Unisinos Av., São Leopoldo 93022-750, RS, BrazilApplied Computing Graduate Program, University of Vale do Rio dos Sinos, 950, Unisinos Av., São Leopoldo 93022-000, RS, BrazilThe Fourth Industrial Revolution, also named Industry 4.0, is leveraging several modern computing fields. Industry 4.0 comprises automated tasks in manufacturing facilities, which generate massive quantities of data through sensors. These data contribute to the interpretation of industrial operations in favor of managerial and technical decision-making. Data science supports this interpretation due to extensive technological artifacts, particularly data processing methods and software tools. In this regard, the present article proposes a systematic literature review of these methods and tools employed in distinct industrial segments, considering an investigation of different time series levels and data quality. The systematic methodology initially approached the filtering of 10,456 articles from five academic databases, 103 being selected for the corpus. Thereby, the study answered three general, two focused, and two statistical research questions to shape the findings. As a result, this research found 16 industrial segments, 168 data science methods, and 95 software tools explored by studies from the literature. Furthermore, the research highlighted the employment of diverse neural network subvariations and missing details in the data composition. Finally, this article organized these results in a taxonomic approach to synthesize a state-of-the-art representation and visualization, favoring future research studies in the field.https://www.mdpi.com/1424-8220/23/11/5010Industry 4.0data sciencemachine learningliterature reviewtaxonomy |
| spellingShingle | Helder Moreira Arruda Rodrigo Simon Bavaresco Rafael Kunst Elvis Fernandes Bugs Giovani Cheuiche Pesenti Jorge Luis Victória Barbosa Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy Sensors Industry 4.0 data science machine learning literature review taxonomy |
| title | Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy |
| title_full | Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy |
| title_fullStr | Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy |
| title_full_unstemmed | Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy |
| title_short | Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy |
| title_sort | data science methods and tools for industry 4 0 a systematic literature review and taxonomy |
| topic | Industry 4.0 data science machine learning literature review taxonomy |
| url | https://www.mdpi.com/1424-8220/23/11/5010 |
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