An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics
Athanasios Kiourtis, Panagiotis Karamolegkos, Andreas Karabetian, Konstantinos A. Voulgaris, Yannis Poulakis, Argyro Mavrogiorgou, Dimosthenis Kyriazis · Studies in health technology and informatics · 2022
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.