Publicação
Extracting temporal patterns from smart city data
| Resumo: | In the modern world data and information become a powerful instrument of management, business, safety, medicine and others. The most fashionable sciences are the sciences which allow us to extract valuable knowledge from big volumes of information. Novel data processing techniques remains a trend for the last five years, in a way that continues to provide interesting results. This paper investigates the algorithms and approaches for processing smart city data, in particular, water consumption data for the city of Bragança, Portugal. Data from the last seven years was processed according to a rigorous methodology, that includes five stages: cleaning, preparation, exploratory analysis, identification of patterns and critical interpretation of the results. After understanding the data and choosing the best algorithms, a web-based data visualizing tools was developed, providing dashboards to geospatial data representation, useful in the decision making of municipalities. |
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| Autores principais: | Gubareva, Regina |
| Assunto: | Data analysis Clustering Big data Dater consumption |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | In the modern world data and information become a powerful instrument of management, business, safety, medicine and others. The most fashionable sciences are the sciences which allow us to extract valuable knowledge from big volumes of information. Novel data processing techniques remains a trend for the last five years, in a way that continues to provide interesting results. This paper investigates the algorithms and approaches for processing smart city data, in particular, water consumption data for the city of Bragança, Portugal. Data from the last seven years was processed according to a rigorous methodology, that includes five stages: cleaning, preparation, exploratory analysis, identification of patterns and critical interpretation of the results. After understanding the data and choosing the best algorithms, a web-based data visualizing tools was developed, providing dashboards to geospatial data representation, useful in the decision making of municipalities. |
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