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Atzeni D, Ramjattan R, Figliè R, Baldi G, Mazzei D. Data-Driven Insights through Industrial Retrofitting: An Anonymized Dataset with Machine Learning Use Cases. Sensors (Basel) 2023; 23:6078. [PMID: 37447927 DOI: 10.3390/s23136078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Small and medium-sized enterprises (SMEs) often encounter practical challenges and limitations when extracting valuable insights from the data of retrofitted or brownfield equipment. The existing literature fails to reflect the full reality and potential of data-driven analysis in current SME environments. In this paper, we provide an anonymized dataset obtained from two medium-sized companies leveraging a non-invasive and scalable data-collection procedure. The dataset comprises mainly power consumption machine data collected over a period of 7 months and 1 year from two medium-sized companies. Using this dataset, we demonstrate how machine learning (ML) techniques can enable SMEs to extract useful information even in the short term, even from a small variety of data types. We develop several ML models to address various tasks, such as power consumption forecasting, item classification, next machine state prediction, and item production count forecasting. By providing this anonymized dataset and showcasing its application through various ML use cases, our paper aims to provide practical insights for SMEs seeking to leverage ML techniques with their limited data resources. The findings contribute to a better understanding of how ML can be effectively utilized in extracting actionable insights from limited datasets, offering valuable implications for SMEs in practical settings.
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Affiliation(s)
- Daniele Atzeni
- Department of Computer Science, University of Pisa, 56126 Pisa, Italy
| | - Reshawn Ramjattan
- Department of Computer Science, University of Pisa, 56126 Pisa, Italy
| | - Roberto Figliè
- Department of Computer Science, University of Pisa, 56126 Pisa, Italy
| | | | - Daniele Mazzei
- Department of Computer Science, University of Pisa, 56126 Pisa, Italy
- Zerynth, 56124 Pisa, Italy
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Dec G, Stadnicka D, Paśko Ł, Mądziel M, Figliè R, Mazzei D, Tyrovolas M, Stylios C, Navarro J, Solé-Beteta X. Role of Academics in Transferring Knowledge and Skills on Artificial Intelligence, Internet of Things and Edge Computing. Sensors (Basel) 2022; 22:s22072496. [PMID: 35408110 PMCID: PMC9002995 DOI: 10.3390/s22072496] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 05/22/2023]
Abstract
Universities play an essential role in preparing human resources for the industry of the future. By providing the proper knowledge, they can ensure that graduates will be able to adapt to the ever-changing industrial sector. However, to achieve this, the courses provided by academia must cover the current and future industrial needs by considering the trends in scientific research and emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC). This work presents the survey results conducted among academics to assess the current state of university courses, regarding the level of knowledge and skills provided to students about the Internet of Things, Artificial Intelligence, and Edge Computing. The novelty of the work is that (a) the research was carried out in several European countries, (b) the current curricula of universities from different countries were analyzed, and (c) the results present the teachers' perspective. To conduct the research, the analysis of the relevant literature took place initially to explore the issues of the presented subject, which will increasingly concern the industry in the near future. Based on the literature review results and analysis of the universities' curricula involved in this study, a questionnaire was prepared and shared with academics. The outcomes of the analysis reveal the areas that require more attention from scholars and possibly modernization of curricula.
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Affiliation(s)
- Grzegorz Dec
- Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland
- Correspondence:
| | - Dorota Stadnicka
- Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, 35-959 Rzeszów, Poland; (D.S.); (Ł.P.); (M.M.)
| | - Łukasz Paśko
- Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, 35-959 Rzeszów, Poland; (D.S.); (Ł.P.); (M.M.)
| | - Maksymilian Mądziel
- Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, 35-959 Rzeszów, Poland; (D.S.); (Ł.P.); (M.M.)
| | - Roberto Figliè
- Computer Science Department, University of Pisa, 56127 Pisa, Italy; (R.F.); (D.M.)
| | - Daniele Mazzei
- Computer Science Department, University of Pisa, 56127 Pisa, Italy; (R.F.); (D.M.)
| | - Marios Tyrovolas
- Laboratory of Knowledge & Intelligent Computing, Department of Informatics and Telecommunications, University of Ioannina, GR-47150 Arta, Greece; (M.T.); (C.S.)
| | - Chrysostomos Stylios
- Laboratory of Knowledge & Intelligent Computing, Department of Informatics and Telecommunications, University of Ioannina, GR-47150 Arta, Greece; (M.T.); (C.S.)
- Athena Research Center, Industrial Systems Institute, GR-26504 Patras, Greece
| | - Joan Navarro
- Research Group in Internet Technologies & Storage, La Salle Campus Barcelona, Universitat Ramon Llull, 08022 Barcelona, Spain; (J.N.); (X.S.-B.)
| | - Xavier Solé-Beteta
- Research Group in Internet Technologies & Storage, La Salle Campus Barcelona, Universitat Ramon Llull, 08022 Barcelona, Spain; (J.N.); (X.S.-B.)
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