Data Model

Data modeling focuses on creating structured representations of data to facilitate efficient storage, analysis, and utilization. Current research emphasizes improving data model robustness, particularly addressing issues like bias, heterogeneity, and dataset drift, often employing techniques like formal concept analysis, network graph analysis, and data selection algorithms to optimize model performance and fairness. These advancements are crucial for enhancing data quality, enabling more reliable machine learning applications, and improving data governance across diverse fields, including healthcare, cultural heritage, and energy.