Data Modelless Microservices to increase Multi-Tenancy in BaaS and SaaS Providers with Application to a Covid-19 Data-Lake

  • Júlio G. S. F. da Costa UFRN
  • Francinaldo A. Pereira UFRN
  • Davi H. dos Santos UFRN
  • Samuel X. de Souza UFRN
  • Luiz M. G. Gonçalves UFRN


This work is the result of the joint efforts of professionals encouraged to build a solution to predict the contagion and death curves of the Covid-19 pandemic, through the use of data-oriented solutions. This strategy is fundamentally dependent on collection. Regarding this particular aspect, the difficulty is manifested due to the fact that such data exist scattered in different repositories, in different formats, commonly available through files, in addition to being frequently updated. However, in this context, small data scientist teams with few resources suffer in this scenario forced to personally concern themselves with these difficulties. Here, we present a platform that helps these professionals to hide the complexities of having to deal with these issues themselves. This is done by creating and helping to manage, in an automated way, repositories for your users for simplified data consumption and distribution.

Palavras-chave: MDE, Covid-19, Pandemic, Data Repository, Multi-tenant Service, Model Interpretation


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COSTA, Júlio G. S. F. da; PEREIRA, Francinaldo A.; SANTOS, Davi H. dos; SOUZA, Samuel X. de; GONÇALVES, Luiz M. G.. Data Modelless Microservices to increase Multi-Tenancy in BaaS and SaaS Providers with Application to a Covid-19 Data-Lake. In: WORKSHOP DE ANÁLISE DE PADRÕES E DADOS DA DINÂMICA DE PANDEMIAS - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 177-182. DOI:

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