Na Li had an accepted paper in IEEE BigData conference 2023

Abstract:

The Argo network, dedicated to ocean profiling, generates a vast volume of observatory data. However, data quality issues from sensor malfunctions and transmission errors necessitate stringent quality assessment. Existing methods, including machine learning, fall short due to limited labeled data and imbalanced datasets. To address these challenges, we propose an ODEAL framework for ocean data quality assessment, employing active learning to reduce human experts’ workload in the quality assessment workflow and leveraging outlier detection algorithms for effective model initialization. The results suggest that our framework enhances quality assessment efficiency by up to 465.5% with the uncertainty-based query strategy compared to random sampling and minimizes overall annotation costs by up to 76.9% using the initial set built with outlier detectors. We thank EuroArgo (Mr Thierry Carval and Mr Jean-Marie Baudet) and MARIS (Mr Peter Thijsse) for discussing the selected data sets, quality control processes, and data labels. This work has been partially funded by the European Union’s Horizon research and innovation program by the CLARIFY (860627), BLUECLOUD 2026 (101094227), ENVRI-FAIR (824068) and ARTICONF (825134), by the LifeWatch ERIC, and by the NWO LTER-LIFE project.