Odisseu: um modelo para serviços inteligentes na indústria 4.0 baseado em análise de históricos de contextos

Abstract

The Fourth Industrial Revolution, also called Industry 4.0, has been leveraging many fields of computing today. Industry 4.0 comprises automated tasks in the manufacturing industry that generate large amounts of data obtained through sensors. These data contribute to the interpretation of industrial operations in favor of managerial and technical decision-making. Data Science supports this interpretation due to significant technological advances, particularly data processing methods and software tools. In this sense, this thesis presents a model entitled Odisseu that focuses on supporting the development of intelligent services aimed at Industry 4.0, using context histories, which represent data from a given entity over a certain period of time. The model proposes an ontology that acts as a link between data science methods and smart services. Compared to other models, Odisseu seeks to fill a gap that involves monitoring data from input to storage in context histories format, in addition to proposing an ontology and a model for generic support to intelligent services in the industry. To evaluate the model, two intelligent services are proposed, the first aimed at locating people in an industry and the second aiming to estimate the subjective wellbeing of employees. The services used data from mobile and fixed beacons, vital signs such as blood volume pulse and electrodermal activity, as well as data from self-report questions focused on well-being. The location service achieved 100% accuracy with both the Random Forest and Multilayer Perceptron algorithms. The well-being service achieved the best performance with the Random Forest algorithm, reaching 74% accuracy.