Abstract: A system and method for or facilitating correction of data in documents is disclosed. The method includes receiving one or more documents from and scanning the received one or more documents by using a document processing system for obtaining one or more mis-captured data fields. The method further includes obtaining a historical correction data and determining one or more deltas based on the obtained historical correction data by using a trained data correction-based ML model. Further, the method includes parsing the determined one or more deltas into one or more datasets, generating one or more correct data fields corresponding to the one or more mis-captured data fields and automatically replacing the one or more mis-captured data fields with the generated one or more correct data fields based on one or more predefined rules.

Abstract: A system and method for or facilitating correction of data in documents is disclosed. The method includes receiving one or more documents from and scanning the received one or more documents by using a document processing system for obtaining one or more mis-captured data fields. The method further includes obtaining a historical correction data and determining one or more deltas based on the obtained historical correction data by using a trained data correction-based ML model. Further, the method includes parsing the determined one or more deltas into one or more datasets, generating one or more correct data fields corresponding to the one or more mis-captured data fields and automatically replacing the one or more mis-captured data fields with the generated one or more correct data fields based on one or more predefined rules.

Abstract: Techniques and structures to facilitate anomaly detection within a networking system, including receiving a plurality of performance metric messages at a database system, extracting a plurality of anomaly detection messages included in the performance metric messages, storing the plurality of anomaly detection messages in an in-memory database and executing a machine learning model to process the plurality of anomaly detection messages in the in-memory database to detect whether anomalous usage of the networking system has been detected.

Abstract: Methods and systems are described for creating and/or processing a definition for a dashboard with manually input code and user selections in a graphical user interface. In some implementations, user interface elements are displayed and selections from a user accepted. Responsive to the selections, definitions for one or more visualizations and datasources are automatically generated and displayed in a user interface element that accepts manually input customizations and code from the user. The resulting combination of code and definitions is stored as a definition for the dashboard, which when processed will transform data received from the at least one of the datasources, combine the transformed data with data from another of the datasources, and cause the display of a visualization based on the combined data.

Abstract: Methods and systems are described for creating and/or processing a definition for a dashboard with manually input code and user selections in a graphical user interface. In some implementations, user interface elements are displayed and selections from a user accepted. Responsive to the selections, definitions for one or more visualizations and datasources are automatically generated and displayed in a user interface element that accepts manually input customizations and code from the user. The resulting combination of code and definitions is stored as a definition for the dashboard, which when processed will transform data received from the at least one of the datasources, combine the transformed data with data from another of the datasources, and cause the display of a visualization based on the combined data.

Abstract: System and methods are described for a monitoring system to monitor operations of a distributed computing system and provide feedback to subscribers. The monitoring system includes a data collection engine to receive data items comprising events, metrics, or one or more configurations about a distributed computing system being monitored, and to store selected data items into a message queue. The monitoring system also includes a data processing engine to receive the selected data items from the queue, transform the selected data items into transformed data items based at least in part on one or more settings for a subscriber and the one or more configurations of the distributed computing system, and store the transformed data items into one or more of a database and a cache.

Abstract: Techniques and structures to facilitate anomaly detection within a networking system, including receiving a plurality of performance metric messages at a database system, extracting a plurality of anomaly detection messages included in the performance metric messages, storing the plurality of anomaly detection messages in an in-memory database and executing a machine learning model to process the plurality of anomaly detection messages in the in-memory database to detect whether anomalous usage of the networking system has been detected

Abstract: Matching knowledge providers with knowledge consumers. Information corresponding to knowledge providers and information corresponding to knowledge consumers is managed in a database. Messages from remote electronic devices corresponding to the knowledge providers and the knowledge consumers are received. Subsequent information corresponding to the knowledge providers and the knowledge consumers is gathered from a plurality of remote platforms. Knowledge power agents configured to operate through the plurality of interfaces match knowledge providers with knowledge consumers based, at least in part, on shared interests, location and availability. In response to a match between at least one knowledge provider and at least one knowledge consumer, communicating via at least one of the plurality of interfaces information about the match to at least one knowledge consumer and at least one knowledge provider for which the match has been made.