Data Management

Data management is evolving rapidly to meet the demands of increasingly complex data-driven applications across diverse fields, from industrial AI and business process management to scientific research and large language models. Current research emphasizes efficient data handling strategies throughout the entire data lifecycle, including data collection, integration, processing, and governance, often incorporating techniques like semantic modeling and data augmentation to improve data quality and usability. These advancements are crucial for enhancing the reliability, reproducibility, and efficiency of machine learning models and other data-intensive processes, ultimately impacting the accuracy, scalability, and trustworthiness of applications across various sectors.