Análise de tradução homem-máquina utilizando mecanismos de atenção para sistemas baseados em SQL para indústria 4.0
The use of relational databases is increasingly present in the industry. Applications in medical, IoT and Industry 4.0 are examples of this. Despite the large capacity and efficiency in storing and retrieving data, this type of database requires technical knowledge in specific query languages to a...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/31668 |
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| Resumo: | The use of relational databases is increasingly present in the industry. Applications in
medical, IoT and Industry 4.0 are examples of this. Despite the large capacity and efficiency in storing and retrieving data, this type of database requires technical knowledge
in specific query languages to access this information, which distances these types of application from the non-expert public. In this work, we propose an application of recent
Deep Learning models in natural language processing that uses attention mechanisms for
translation from natural language in English to SQL applied to a database which stores
data from sensors, focused on the concept of Industry 4.0. Paired examples of natural language phrases were generated with their corresponding SQL query to be used for training
and validation. The model was database scheme agnostic, in a way that it only handles the
input and output sequences regardless of the database structure. Data come from a typical
process historians used in industrial scenarios. By training the deep neural network, it
was obtained a language model with an accuracy of approximately 92% in the validation
set. |
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