Cross-Domain Transfer Learning for Data Quality Anomaly Detection in Emerging Domains

Many advanced anomaly detection frameworks (Elouataoui et al., 2023; Li et al., 2024) require extensive labeled data, which is scarce in new application areas (e.g., novel IoT deployments, emerging biomedical sensors). Building on Li et al.’s (2024) self-supervised and contrastive learning for multisource data, this research would investigate transfer learning architectures that leverage “knowledge” of quality anomalies from established domains (e.g., finance, supply chain) to bootstrap detection in new sectors, adjusting for domain-specific features and quality dimensions. This approach could dramatically accelerate quality assurance in fast-moving fields, reduce annotation costs, and ensure early-stage data is trustworthy—unlocking new applications for machine learning and analytics where data quality is a persistent bottleneck.

References:

1. Quality Anomaly Detection Using Predictive Techniques: An Extensive Big Data Quality Framework for Reliable Data Analysis. Elouataoui Widad, Elmendili Saida, Youssef Gahi (2023). IEEE Access.
2. Self-Supervised Learning and Multisource Heterogeneous Information Fusion Based Quality Anomaly Detection for Heavy-Plate Shape. Datong Li, Jun Lu, Tongkang Zhang, Jinliang Ding (2024). IEEE Transactions on Automation Science and Engineering.