Combining Multidimensional Modelling with Frequent Pattern Mining Paradigms to Enhance Big Multidimensional Data Analytics Tools over Emerging Big Data Domains

Starting from the growing availability of heterogeneous big data, which presents new opportunities for knowledge discovery, particularly in healthcare and epidemiology. In this paper, we present an innovative big data analytics framework designed for the analysis and visualization of big data sets of emerging domains. This framework integrates Online Analytical Processing (OLAP) based multidimensional modelling with advanced frequent patterns mining to enable scalable and efficient analysis and pattern discovery. Our framework identifies frequent patterns and sub-patterns to reveal trends in disease prevalence and employs machine learning to predict outcomes based on historical data.