Machine learning based process and quality monitoring system

Abstract

This technical solution relates to big data computer processing field, in particular, to the system of automatic quality monitoring of data obtained from different sources in real time. The technical result is improving quality and accuracy of analysis of data obtained from different sources in real time. The computer-assisted system of automatic quality monitoring of data obtained from different sources in real time is claimed, comprising: web application module, configured to add new monitoring sources to the system, configure advanced monitoring options, browse through event history and monitoring reports, and also visualize the detected data deviations; integration connector module, configured to connect the system to different sources for obtaining data, and configured to transform these data into common internal format for further uniform processing; machine learning module, which is self-learning to evaluate quality of data obtained in real time, during which: receives from connectors the transformed data from different sources within the specified time period and saves them into total sample; starts initialization of the saved sample monitoring by defining data change scales, while statistics are calculated for each sample indicator in accordance with a specific scale, and algorithm for check initialization is started for each indicator based on the calculated statistics; after completion of learning the learned model parameters are saved in the database; machine learning module uses the learned model parameters for subsequent monitoring new data received according to the specified schedule, while, the machine learning module is continuously relearned with new corrected data, if the current model improperly recognizes dependencies in new data, the module is fully relearned; if some deviations are detected after monitoring of new data received, the notification aggregation module composes a text with errors; information channel integration module sends the text with errors to corresponding users.

Dependencies are inferred from the claim text.