Next generation data harmonization

Advancements and Challenges in Data Harmonization: A Comprehensive Review

“…This methodology emphasizes manual categorization and alignment efforts, showcasing the challenges of scalability and adaptability in rapidly changing market environments [2]. CUBRC's semantic concept identification and tool development for data harmonization represent a blend of traditional and advanced methodologies [3]. The study focuses on semantic analysis and the development of graphical user interfaces for mapping data points towards an evolving approach that incorporates elements of automation and user-driven data interaction.…”

“…The trend toward scalability in data harmonization processes is critical in an era characterized by exponential data growth [3]. As organizations and institutions generate and collect data at an unprecedented scale, the ability to efficiently harmonize large datasets becomes a fundamental requirement.…”

“…Automation stands out as a key trend, aiming to reduce the manual effort involved in data harmonization tasks [3]. By leveraging machine learning algorithms and artificial intelligence, the process of matching, cleaning, and integrating data from various sources can be significantly streamlined.…”

Social and political event data to support Army requirements : volume 1

“…However, because ICEWS is automated (as are other CAMEO-based systems), and it was designed primarily for forecasting, it tolerates and includes substantial noise, including large volumes of miscoded events, duplicate events, and limited actor information. As described in Armstrong et al (2015), analysts seeking to understand complex violent events often work with multiple data sources, because within and between data sources, records might mention, refer to, or be related to one another. However, as each dataset typically has its strengths and its own schema for organizing the information, it can be difficult to gather and reason across these records.…”