IoT Data Quality

Abstract

Data quality issues have been widely recognized in IoT data, and prevent the downstream applications. In this tutorial, we review the state-of-the-art techniques for IoT data quality management. In particular, we discuss how the dedicated approaches improve various data quality dimensions, including validity, completeness and consistency. Among others, we further highlight the recent advances by deep learning techniques for IoT data quality. Finally, we indicate the open problems in IoT data quality management, such as benchmark or interpretation of data quality issues.