Latent Space Explorer: Unsupervised Data Pattern Discovery on the Cloud

TL;DR

This paper presents a cloud-based system that uses unsupervised learning to extract and visualize patterns from raw data, aiding scientific analysis across disciplines.

Contribution

It introduces a novel cloud architecture for unsupervised data pattern discovery and demonstrates its application in astronomical data analysis.

Findings

System successfully identifies data patterns in astronomical datasets

Provides a scalable, cloud-based platform for scientific data exploration

Enhances pattern detection efficiency in large-scale data analysis

Abstract

Extracting information from raw data is probably one of the central activities of experimental scientific enterprises. This work is about a pipeline in which a specific model is trained to provide a compact, essential representation of the training data, useful as a starting point for visualization and analyses aimed at detecting patterns, regularities among data. To enable researchers exploiting this approach, a cloud-based system is being developed and tested in the NEANIAS project as one of the ML-tools of a thematic service to be offered to the EOSC. Here, we describe the architecture of the system and introduce two example use cases in the astronomical context.