Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/14/8/242 |
| _version_ | 1797525005696761856 |
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| author | Anestis Kousis Christos Tjortjis |
| author_facet | Anestis Kousis Christos Tjortjis |
| author_sort | Anestis Kousis |
| collection | DOAJ |
| description | Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart cities applications. The study aims to identify the main DM techniques used in the context of smart cities and how the research field of DM for smart cities evolves over time. We adopted both qualitative and quantitative methods to explore the topic. We used the Scopus database to find relative articles published in scientific journals. This study covers 197 articles published over the period from 2013 to 2021. For the bibliometric analysis, we used the Biliometrix library, developed in R. Our findings show that there is a wide range of DM technologies used in every layer of a smart city project. Several ML algorithms, supervised or unsupervised, are adopted for operating the instrumentation, middleware, and application layer. The bibliometric analysis shows that DM for smart cities is a fast-growing scientific field. Scientists from all over the world show a great interest in researching and collaborating on this interdisciplinary scientific field. |
| first_indexed | 2024-03-10T09:05:41Z |
| format | Article |
| id | doaj.art-d62d9f7ae4284c3ab3966ddc1dee951b |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T09:05:41Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-d62d9f7ae4284c3ab3966ddc1dee951b2023-11-22T06:27:49ZengMDPI AGAlgorithms1999-48932021-08-0114824210.3390/a14080242Data Mining Algorithms for Smart Cities: A Bibliometric AnalysisAnestis Kousis0Christos Tjortjis1Department of Science and Technology, International Hellenic University, 14th km Thessaloniki-N. Moudania National Road, 57001 Thermi, GreeceDepartment of Science and Technology, International Hellenic University, 14th km Thessaloniki-N. Moudania National Road, 57001 Thermi, GreeceSmart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart cities applications. The study aims to identify the main DM techniques used in the context of smart cities and how the research field of DM for smart cities evolves over time. We adopted both qualitative and quantitative methods to explore the topic. We used the Scopus database to find relative articles published in scientific journals. This study covers 197 articles published over the period from 2013 to 2021. For the bibliometric analysis, we used the Biliometrix library, developed in R. Our findings show that there is a wide range of DM technologies used in every layer of a smart city project. Several ML algorithms, supervised or unsupervised, are adopted for operating the instrumentation, middleware, and application layer. The bibliometric analysis shows that DM for smart cities is a fast-growing scientific field. Scientists from all over the world show a great interest in researching and collaborating on this interdisciplinary scientific field.https://www.mdpi.com/1999-4893/14/8/242data miningmachine learningsmart citiesbig databibliometrics |
| spellingShingle | Anestis Kousis Christos Tjortjis Data Mining Algorithms for Smart Cities: A Bibliometric Analysis Algorithms data mining machine learning smart cities big data bibliometrics |
| title | Data Mining Algorithms for Smart Cities: A Bibliometric Analysis |
| title_full | Data Mining Algorithms for Smart Cities: A Bibliometric Analysis |
| title_fullStr | Data Mining Algorithms for Smart Cities: A Bibliometric Analysis |
| title_full_unstemmed | Data Mining Algorithms for Smart Cities: A Bibliometric Analysis |
| title_short | Data Mining Algorithms for Smart Cities: A Bibliometric Analysis |
| title_sort | data mining algorithms for smart cities a bibliometric analysis |
| topic | data mining machine learning smart cities big data bibliometrics |
| url | https://www.mdpi.com/1999-4893/14/8/242 |
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