A Network-Based Analysis of a Worksite Canteen Dataset
The provision of wellness in workplaces gained interest in recent decades. A factor that contributes significantly to workers’ health is their diet, especially when provided by canteen services. The assessment of such a service involves questions as food cost, its sustainability, quality, nutritiona...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/5/1/11 |
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| author | Vincenza Carchiolo Marco Grassia Alessandro Longheu Michele Malgeri Giuseppe Mangioni |
| author_facet | Vincenza Carchiolo Marco Grassia Alessandro Longheu Michele Malgeri Giuseppe Mangioni |
| author_sort | Vincenza Carchiolo |
| collection | DOAJ |
| description | The provision of wellness in workplaces gained interest in recent decades. A factor that contributes significantly to workers’ health is their diet, especially when provided by canteen services. The assessment of such a service involves questions as food cost, its sustainability, quality, nutritional facts and variety, as well as employees’ health and disease prevention, productivity increase, economic convenience vs. eating satisfaction when using canteen services. Even if food habits have already been studied using traditional statistical approaches, here we adopt an approach based on Network Science that allows us to deeply study, for instance, the interconnections among people, company and meals and that can be easily used for further analysis. In particular, this work concerns a multi-company dataset of workers and dishes they chose at a canteen worksite. We study eating habits and health consequences, also considering the presence of different companies and the corresponding contact network among workers. The macro-nutrient content and caloric values assessment is carried out both for dishes and for employees, in order to establish when food is balanced and healthy. Moreover, network analysis lets us discover hidden correlations among people and the environment, as communities that cannot be usually inferred with traditional or methods since they are not known a priori. Finally, we represent the dataset as a tripartite network to investigate relationships between companies, people, and dishes. In particular, the so-called network <i>projections</i> can be extracted, each one being a network among specific kind of nodes; further community analysis tools will provide hidden information about people and their food habits. In summary, the contribution of the paper is twofold: it provides a study of a real dataset spanning over several years that gives a new interesting point of view on food habits and healthcare, and it also proposes a new approach based on Network Science. Results prove that this kind of analysis can provide significant information that complements other traditional methodologies. |
| first_indexed | 2024-03-09T05:04:18Z |
| format | Article |
| id | doaj.art-a4e4095133cf4072a55cf3063daae09f |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-09T05:04:18Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-a4e4095133cf4072a55cf3063daae09f2023-12-03T12:56:56ZengMDPI AGBig Data and Cognitive Computing2504-22892021-03-01511110.3390/bdcc5010011A Network-Based Analysis of a Worksite Canteen DatasetVincenza Carchiolo0Marco Grassia1Alessandro Longheu2Michele Malgeri3Giuseppe Mangioni4Dipartimento di Matematica e Informatica, Universitá degli Studi di Catania, I95125 Catania, ItalyDipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universitá degli Studi di Catania, I95125 Catania, ItalyDipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universitá degli Studi di Catania, I95125 Catania, ItalyDipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universitá degli Studi di Catania, I95125 Catania, ItalyDipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universitá degli Studi di Catania, I95125 Catania, ItalyThe provision of wellness in workplaces gained interest in recent decades. A factor that contributes significantly to workers’ health is their diet, especially when provided by canteen services. The assessment of such a service involves questions as food cost, its sustainability, quality, nutritional facts and variety, as well as employees’ health and disease prevention, productivity increase, economic convenience vs. eating satisfaction when using canteen services. Even if food habits have already been studied using traditional statistical approaches, here we adopt an approach based on Network Science that allows us to deeply study, for instance, the interconnections among people, company and meals and that can be easily used for further analysis. In particular, this work concerns a multi-company dataset of workers and dishes they chose at a canteen worksite. We study eating habits and health consequences, also considering the presence of different companies and the corresponding contact network among workers. The macro-nutrient content and caloric values assessment is carried out both for dishes and for employees, in order to establish when food is balanced and healthy. Moreover, network analysis lets us discover hidden correlations among people and the environment, as communities that cannot be usually inferred with traditional or methods since they are not known a priori. Finally, we represent the dataset as a tripartite network to investigate relationships between companies, people, and dishes. In particular, the so-called network <i>projections</i> can be extracted, each one being a network among specific kind of nodes; further community analysis tools will provide hidden information about people and their food habits. In summary, the contribution of the paper is twofold: it provides a study of a real dataset spanning over several years that gives a new interesting point of view on food habits and healthcare, and it also proposes a new approach based on Network Science. Results prove that this kind of analysis can provide significant information that complements other traditional methodologies.https://www.mdpi.com/2504-2289/5/1/11network sciencedata analysishealth informatics |
| spellingShingle | Vincenza Carchiolo Marco Grassia Alessandro Longheu Michele Malgeri Giuseppe Mangioni A Network-Based Analysis of a Worksite Canteen Dataset Big Data and Cognitive Computing network science data analysis health informatics |
| title | A Network-Based Analysis of a Worksite Canteen Dataset |
| title_full | A Network-Based Analysis of a Worksite Canteen Dataset |
| title_fullStr | A Network-Based Analysis of a Worksite Canteen Dataset |
| title_full_unstemmed | A Network-Based Analysis of a Worksite Canteen Dataset |
| title_short | A Network-Based Analysis of a Worksite Canteen Dataset |
| title_sort | network based analysis of a worksite canteen dataset |
| topic | network science data analysis health informatics |
| url | https://www.mdpi.com/2504-2289/5/1/11 |
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