无数据模型微服务,通过应用到Covid-19数据湖,增加BaaS和SaaS提供商的多租户

Júlio G. S. F. da Costa, F. Pereira, Davi H. dos Santos, Samuel X. de Souza, L. M. Gonçalves
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引用次数: 0

摘要

这项工作是专业人员共同努力的结果,鼓励他们通过使用以数据为导向的解决方案,构建预测Covid-19大流行的传染和死亡曲线的解决方案。这种策略从根本上依赖于收集。在这方面,由于这些数据分散存在于不同的存储库中,格式不同,通常通过文件提供,而且经常更新,因此表现出困难。然而,在这种情况下,拥有很少资源的小型数据科学家团队在这种情况下被迫亲自关注这些困难。在这里,我们提供了一个平台,帮助这些专业人士隐藏必须自己处理这些问题的复杂性。这是通过以自动化的方式为用户创建和帮助管理存储库来实现的,以简化数据的使用和分发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Modelless Microservices to increase Multi-Tenancy in BaaS and SaaS Providers with Application to a Covid-19 Data-Lake
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.
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