Department of Digital Systems, University of Piraeus, Greece.
Stud Health Technol Inform. 2022 Jun 29;295:376-379. doi: 10.3233/SHTI220743.
Big Data has proved to be vast and complex, without being efficiently manageable through traditional architectures, whereas data analysis is considered crucial for both technical and non-technical stakeholders. Current analytics platforms are siloed for specific domains, whereas the requirements to enhance their use and lower their technicalities are continuously increasing. This paper describes a domain-agnostic single access autoscaling Big Data analytics platform, namely Diastema, as a collection of efficient and scalable components, offering user-friendly analytics through graph data modelling, supporting technical and non-technical stakeholders. Diastema's applicability is evaluated in healthcare through a predicting classifier for a COVID19 dataset, considering real-world constraints.
大数据已经证明是庞大而复杂的，如果不通过传统架构进行有效管理，数据分析被认为对技术和非技术利益相关者都至关重要。当前的分析平台针对特定领域进行了隔离，而提高其使用效率和降低技术难度的需求却在不断增加。本文描述了一个与领域无关的单一访问自动扩展大数据分析平台，即 Diastema，它是一系列高效且可扩展的组件的集合，通过图形数据建模提供用户友好的分析，支持技术和非技术利益相关者。Diastema 通过考虑现实世界的约束条件，在医疗保健领域通过对 COVID19 数据集进行预测分类器来评估其适用性。
