Publicação
Cloud Rule-based System for Analysis of IoT Data in a Big Data Context
| Resumo: | Nowadays, enormous amounts of information are produced, on a daily basis, by sensors. Information which, after being analysed, is transformed from simple data into knowledge which, in itself, can be an asset to those who can take advantage of that knowledge. An example of this situation is the data being generated by sensors installed on trains, that can be analysed to different ends, one of which, the condition-based maintenance of trains. Condition-based maintenance takes advantage of data to understand the current state of mechanical equipment, avoiding unnecessary replacements or preventing accidents consequent of late maintenance. In this dissertation, it is presented an architecture which integrates a rule-based system functioning over cloud applications that analyses all the data that’s being acquired by the trains’ sensors in a way that, whenever a specific set of conditions is met alerts are activated, so the train operators, the mechanics in charge and all their staff know how to proceed. This architecture is to be created on a cloud environment since, with this vast amount of data being generated, these highly scalable environments assure that data processing performance isn’t compromised and that all this data is analysed in a timely manner, taking advantage of all its computational components. The process of creating this architecture is demonstrated step by step and the test results are presented and analysed. |
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| Autores principais: | Grosso, Leonor Rodrigues Galhoz Vieira |
| Assunto: | Big Data Internet of Things Cloud Technologies Industry 4.0 Google Cloud Platform Rule-based System |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Nowadays, enormous amounts of information are produced, on a daily basis, by sensors. Information which, after being analysed, is transformed from simple data into knowledge which, in itself, can be an asset to those who can take advantage of that knowledge. An example of this situation is the data being generated by sensors installed on trains, that can be analysed to different ends, one of which, the condition-based maintenance of trains. Condition-based maintenance takes advantage of data to understand the current state of mechanical equipment, avoiding unnecessary replacements or preventing accidents consequent of late maintenance. In this dissertation, it is presented an architecture which integrates a rule-based system functioning over cloud applications that analyses all the data that’s being acquired by the trains’ sensors in a way that, whenever a specific set of conditions is met alerts are activated, so the train operators, the mechanics in charge and all their staff know how to proceed. This architecture is to be created on a cloud environment since, with this vast amount of data being generated, these highly scalable environments assure that data processing performance isn’t compromised and that all this data is analysed in a timely manner, taking advantage of all its computational components. The process of creating this architecture is demonstrated step by step and the test results are presented and analysed. |
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