Anomaly detection using autoencoder for data quality monitoring in cloud

Abstract

Many works are dedicated to solving the data quality problem, a number of standards have been developed, but the problem has not been solved for decades. Moreover, the problem now requires a more complex solution due to processing large amounts of unstructured data in the cloud. This work presents the original project Autoencoder that focuses on the technology of analysis, detection and forecasting poor-quality data transmission based on machine learning and the use of neural networks.