Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics

TL;DR

This paper emphasizes the importance of contextualizing geometric data analysis within big data analytics, highlighting case studies and technical insights to enhance understanding and application.

Contribution

It introduces a virtual microscope approach for big data analytics, integrating qualitative context with quantitative geometric analysis, especially via Correspondence Analysis.

Findings

Case studies demonstrating geometric data analysis in big data contexts

Technical insights into the implementation of the virtual microscope

Relevance of contextualization for emerging analytical needs

Abstract

The relevance and importance of contextualizing data analytics is described. Qualitative characteristics might form the context of quantitative analysis. Topics that are at issue include: contrast, baselining, secondary data sources, supplementary data sources, dynamic and heterogeneous data. In geometric data analysis, especially with the Correspondence Analysis platform, various case studies are both experimented with, and are reviewed. In such aspects as paradigms followed, and technical implementation, implicitly and explicitly, an important point made is the major relevance of such work for both burgeoning analytical needs and for new analytical areas including Big Data analytics, and so on. For the general reader, it is aimed to display and describe, first of all, the analytical outcomes that are subject to analysis here, and then proceed to detail the more quantitative outcomes that fully support the analytics carried out.