An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics
Athanasios Kiourtis, Panagiotis Karamolegkos, Andreas Karabetian, Konstantinos Voulgaris, Yannis Poulakis, Argyro Mavrogiorgou, Dimosthenis Kyriazis
Abstract
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.