| Title |
Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review |
| ID_Doc |
40091 |
| Authors |
Hurbean, L; Danaiata, D; Militaru, F; Dodea, AM; Negovan, AM |
| Title |
Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review |
| Year |
2021 |
| Published |
Electronics, 10, 23 |
| DOI |
10.3390/electronics10232997 |
| Abstract |
Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects. |
| Author Keywords |
smart city; open data; machine learning; systematic literature review |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000734539800001 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied |
| Research Area |
Computer Science; Engineering; Physics |
| PDF |
https://www.mdpi.com/2079-9292/10/23/2997/pdf?version=1638361827
|