Generative policy framework for AI training data curation
Valentina Salapura, David Wood, et al.
SMARTCOMP 2019
With the growing importance of data in all aspects of the functioning of an enterprise, having good quality of data is crucial in support of business processes. However, there do not exist good metrics to measure the quality of data that is available within an enterprise. While there are several data quality standards, their complexity and their required customization makes them difficult to use in real-world industrial scenarios. In this paper, we discuss the challenges encountered in measuring data quality within asset management systems. We propose a policy-based approach for measuring data quality, and show how such an approach can be customized and interpreted easily by practitioners in the field.
Valentina Salapura, David Wood, et al.
SMARTCOMP 2019
Qiang Zeng, Mingyi Zhao, et al.
IEEE TKDE
Ian Molloy, Hong Chen, et al.
ACM TISSEC
Xiping Wang, Cesar Gonzales, et al.
SPIE Defense + Security 2012