Drill-CODA: A Framework for Supporting Drill-Across Multidimensional Big Data Analytics Over Big Co-Occurrence Aggregate Hierarchical Data

Abstract This paper proposes and experimentally assesses the Drill -CODA framework, which allows us to support drill-across multidimensional big data analytics over big co-occurrence aggregate hierarchical data, with also privacy-preservation features . Drill -CODA is a composite framework that combines several data processing and analytics metaphors over hierarchical data, all in a multidimensional fashion, with the goal of providing useful insights over large-scale big data repositories, while protecting their privacy. The latter is, as for now, a critical challenge in the big data research community, which arises in a plethora of emerging big data application scenarios, ranging from urban analytics to social network analysis , from bio-medical tools to industry 4.0 prognostic tools , and so forth. This is because data from real-life settings are hierarchical by nature. To validate its effectiveness, we conducted three experimental evaluations using six real-life public health datasets, including Mental Disorders , C15 Plus , Substance Use , Narcan Administration , Diabetes , and Cancer Deaths . The results highlight the framework ability to uncover strong correlations across heterogeneous data domains: for example, Pearson correlation values reached up to 0.92 (with corresponding Spearman coefficients around 0.89) in mental disorder/substance use analysis, while diabetes/cancer mortality correlations ranged from 0.80 to 1.00 across countries such as Italy, Germany, and France. Furthermore, the cancer incidence/mental disorder analysis revealed heterogeneous patterns, with South/Central America exhibiting strong correlations ( $$ \approx 0.85-0.95$$ ), whereas North America showed weaker values ( $$ \approx 0.20-0.40$$ ) in certain years, which confirms the benefits derived from our proposed framework.