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Related Experiment Video

Updated: Sep 6, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics.

Athanasios Kiourtis1, Panagiotis Karamolegkos1, Andreas Karabetian1

  • 1Department of Digital Systems, University of Piraeus, Greece.

Studies in Health Technology and Informatics
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Summary

This study introduces Diastema, a domain-agnostic Big Data analytics platform simplifying complex data analysis for all users. It enhances accessibility and scalability, proving effective in healthcare applications like COVID-19 prediction.

Keywords:
analyticsbig datacloud computinggraph modellinguser experience

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Area of Science:

  • Computer Science
  • Data Science
  • Bioinformatics

Background:

  • Traditional Big Data architectures struggle with vast and complex datasets, hindering efficient data analysis.
  • Existing analytics platforms are often domain-specific and technically complex, limiting accessibility for diverse stakeholders.
  • There is a growing need for user-friendly, scalable, and domain-agnostic Big Data analytics solutions.

Purpose of the Study:

  • To present Diastema, a novel domain-agnostic, single-access, autoscaling Big Data analytics platform.
  • To demonstrate how Diastema supports both technical and non-technical users through user-friendly analytics and graph data modeling.
  • To evaluate the platform's applicability and effectiveness in a real-world healthcare scenario.

Main Methods:

  • Development of Diastema as a collection of efficient and scalable components.
  • Implementation of graph data modeling for intuitive data analysis.
  • Evaluation of Diastema using a predictive classifier on a COVID-19 dataset within real-world constraints.

Main Results:

  • Diastema provides a unified, autoscaling solution for Big Data analytics, overcoming traditional architectural limitations.
  • The platform demonstrates user-friendliness and accessibility for both technical and non-technical users.
  • Successful application in healthcare for COVID-19 data analysis, validating its real-world utility.

Conclusions:

  • Diastema offers a significant advancement in Big Data analytics by providing a domain-agnostic, scalable, and user-friendly platform.
  • The platform effectively bridges the gap between complex data and stakeholder needs, enhancing data-driven decision-making.
  • Diastema's successful healthcare application highlights its potential across various scientific and industrial domains.