Outlier detection with ontology-driven fault contextualization in the Industry 4.0 - Miodutzki, Dionei; Tacla, Cesar; Gomes-Jr, Luiz (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2022: Anais do XXXVII Simpósio Brasileiro de Bancos de Dados, 2022)
In the industrial sector, outlier detection makes it possible to quickly identify equipment failures. The evolution of Industry 4.0 is bringing challenges previously uncommon in the area. The large number of data constantly generated represents a processing challenge and can lead to the identification of a large number of outliers simultaneously. This scenario slows the troubleshooting process, delaying the identification of the source of the fault. This work presents a solution to support decision-making in a widespread failure scenario. Dependencies are represented using ontologies, to provide a clear and user-facilitated interpretation. An inference engine is used to identify the most probable causes of the failure. Performance tests demonstrate its scalability.