System Design for Extensive Exploratory Causal Association Odds Ratio Rule Analysis in a Temporary Database Environment

Abstract

This study will undertake extensive exploratory analysis within a temporary database environment (or data lake), positioned between raw datasets, often sourced from relational databases, and a multidimensional data warehouse supporting diverse analytical perspectives. The primary objective is to generate causal association odds ratio rules that go beyond traditional association rules, aiming to enhance decision-support systems with insights into underlying causal relationships. Such a design allows for a focused interpretability on causal factors influenced by computation across highly complex scenarios, offering effective dimensional references for constructing multidimensional data warehouses and aiding in the selection of valuable validation analyses.