Publication
IIoT Data Ness: From Streaming to Added Value
| datacite.subject.fos | Informática | pt_PT |
| dc.contributor.advisor | Sousa, Cristóvão Dinis | |
| dc.contributor.advisor | Carneiro, Davide Rua | |
| dc.contributor.author | Correia, Ricardo André Araújo | |
| dc.date.accessioned | 2023-03-15T15:13:55Z | |
| dc.date.available | 2023-03-15T15:13:55Z | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022 | |
| dc.description.abstract | In the emerging Industry 4.0 paradigm, the internet of things has been an innovation driver, allowing for environment visibility and control through sensor data analysis. However the data is of such volume and velocity that data quality cannot be assured by conventional architectures. It has been argued that the quality and observability of data are key to a project’s success, allowing users to interact with data more effectively and rapidly. In order for a project to become successful in this context, it is of imperative importance to incorporate data quality mechanisms in order to extract the most value out of data. If this goal is achieved one can expect enormous advantages that could lead to financial and innovation gains for the industry. To cope with this reality, this work presents a data mesh oriented methodology based on the state-of-the-art data management tools that exist to design a solution which leverages data quality in the Industrial Internet of Things (IIoT) space, through data contextualization. In order to achieve this goal, practices such as FAIR data principles and data observability concepts were incorporated into the solution. The result of this work allowed for the creation of an architecture that focuses on data and metadata management to elevate data context, ownership and quality. | pt_PT |
| dc.description.abstract | O conceito de Internet of Things (IoT) é um dos principais fatores de sucesso para a nova Indústria 4.0. Através de análise de dados sobre os valores que os sensores coletam no seu ambiente, é possível a construção uma plataforma capaz de identificar condições de sucesso e eventuais problemas antes que estes ocorram, resultando em ganho monetário relevante para as empresas. No entanto, este caso de uso não é de fácil implementação, devido à elevada quantidade e velocidade de dados proveniente de um ambiente de IIoT (Industrial Internet of Things). | pt_PT |
| dc.identifier.tid | 203173015 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/22521 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Observabilidade de dados | pt_PT |
| dc.subject | Qualidade de dados | pt_PT |
| dc.subject | FAIR data | pt_PT |
| dc.subject | Data Mesh | pt_PT |
| dc.subject | Data Fabric | pt_PT |
| dc.subject | IIoT | pt_PT |
| dc.title | IIoT Data Ness: From Streaming to Added Value | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Engenharia Informática | pt_PT |
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