2021
DOI: 10.1109/access.2021.3101938
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An Ontological-Based Model to Data Governance for Big Data

Abstract: Nowadays, companies and official bodies are using the data as a principal asset to take strategic decision. The advances in big data processing, storage and analysis techniques have allowed to manage the continuous increase in the volume of data. This increase in the volume of data together with its high variability and the large number of sources lead to a constant growing of the complexity of the data management environment. Data governance is the key for simplifying that complexity: it is the element that c… Show more

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Cited by 19 publications
(3 citation statements)
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“…In addition, existing data stewardship frameworks do not involve users in data security to promote participatory innovation in data governance. A collaborative implementation is created by involving all stakeholders [147]. Finally, an operational and automation framework with data lake architecture comprising a landing, formatted zones, and metadata artifacts is required.…”
Section: Smart Governancementioning
confidence: 99%
“…In addition, existing data stewardship frameworks do not involve users in data security to promote participatory innovation in data governance. A collaborative implementation is created by involving all stakeholders [147]. Finally, an operational and automation framework with data lake architecture comprising a landing, formatted zones, and metadata artifacts is required.…”
Section: Smart Governancementioning
confidence: 99%
“…e proposed reference framework improves the performance and reliability of industry 4.0. Castro et al [17] proposed a new ontology-based data governance model for the big data analysis process. Distributed component-based autonomous system is presented for data governance.…”
Section: Related Workmentioning
confidence: 99%
“…Most data are expected to be static, being manually entered into spreadsheets with little real‐time acquisition of data streaming into the hub. Unlike GenBank (https://www.ncbi.nlm.nih.gov/genbank/), data variety are anticipated to be high including soil, plant, animal, and environmental information (Castro et al., 2021). Authors anticipated granularity to vary, with plot‐level data being common and most flexible regarding re‐purposing, but also accommodated curation of treatment mean data with estimates of variance (e.g., standard error, standard deviation, and least significant difference).…”
Section: Introductionmentioning
confidence: 99%