Abstract: Systems and methods are disclosed for responsive interfacing and/or metric tracking. A system receives a query through an interface. In some examples, the system modifies the query based on context data and a database schema to include some of the context data and to more closely align with the database schema. The system processes sub-queries associated with the modified query to generate respective responses to aspects of the query, which the system combines to synthesize an output response that answers the query. In some examples, the query identifies a metric to be tracked. The system analyzes datasets based on the metric and query context to identify data types accessible in the datasets and a calculation to calculate the metric using the data types. The system retrieves historical data from the datasets and generates a visualization that tracks the metric across a time period using the historical data and the calculation.

Abstract: Systems and methods are disclosed for responsive interfacing and/or metric tracking. A system receives a query through an interface. In some examples, the system modifies the query based on context data and a database schema to include some of the context data and to more closely align with the database schema. The system processes sub-queries associated with the modified query to generate respective responses to aspects of the query, which the system combines to synthesize an output response that answers the query. In some examples, the query identifies a metric to be tracked. The system analyzes datasets based on the metric and query context to identify data types accessible in the datasets and a calculation to calculate the metric using the data types. The system retrieves historical data from the datasets and generates a visualization that tracks the metric across a time period using the historical data and the calculation.

Abstract: Systems and methods for error resolution are disclosed herein. The systems and methods may monitor an identity dataset associated with one or more entities. The systems and methods may generate a normalized dataset based on the identity dataset and an input format associated with a trained machine learning model. The systems and methods may process the normalized dataset using the trained machine learning model to identify one or more errors within the normalized dataset. The systems and methods may generate a data quality score for the normalized dataset based on the one or more errors identified within the normalized dataset using the trained machine learning model. The systems and methods may output the data quality score for the normalized dataset.

Abstract: An entity resolution method and system may obtain historical entity data from one or more databases to generate an entity matrix based on a clustering of the historical entity data. The entity resolution method and system may partition the entity matrix into one or more disjointed groups of records based on multi-pass blocking of the attributes associated with the one or more entities and cluster the one or more disjointed groups of records based on one or more similarity metrics between each of the one or more disjointed groups of records. The entity resolution method and system may generate an entity graph for the one or more entities based on the clustering and may create an entity index for an entity based on the entity graph. Ultimately, the entity resolution method and system resolves an entity query of a requestor as matching the entity.

Abstract: An entity resolution method and system may obtain historical entity data from one or more databases to generate an entity matrix based on a clustering of the historical entity data. The entity resolution method and system may partition the entity matrix into one or more disjointed groups of records based on multi-pass blocking of the attributes associated with the one or more entities and cluster the one or more disjointed groups of records based on one or more similarity metrics between each of the one or more disjointed groups of records. The entity resolution method and system may generate an entity graph for the one or more entities based on the clustering and may create an entity index for an entity based on the entity graph. Ultimately, the entity resolution method and system resolves an entity query of a requestor as matching the entity.

Abstract: Apparatus, systems, and methods may operate to monitor database system resource consumption over various time periods, in conjunction with scheduled data loading, data export, and query operations. Additional activities may include generating a database system resource consumption map based on the monitoring, and adjusting database system workload throttling to accommodate predicted database system resource consumption based on the resource consumption map and current system loading, prior to the current database resource consumption reaching a predefined critical consumption level. The current system loading may be induced by data loading, data export, or query activity. Other apparatus, systems, and methods are disclosed.

Abstract: A method and apparatus are provided for facilitating performance monitoring of a large database system. The apparatus comprises a processor and a storage device communicatively coupled with the processor. The processor is programmed to (i) retrieve resource usage data points, (ii) calculate an outlier threshold value based upon values of data points, and (iii) determine if the value of each data point is outside of the threshold value. The processor is further programmed to (iv) plot the value of the data point in histogram with a first color when the data point value is determined to be outside of the threshold value, (v) plot the data point value in histogram with a second color when the data point value is determined to be not outside of the threshold value, and (vi) display the histogram containing plotted data point values of the first color and plotted data point values of the second color to enable a user to quickly and easily identify a potential skew condition.

Abstract: A method and apparatus are provided for facilitating performance monitoring of a large database system. The apparatus comprises a processor and a storage device communicatively coupled with the processor. The processor is programmed to (i) retrieve resource usage data points, (ii) calculate an outlier threshold value based upon values of data points, and (iii) determine if the value of each data point is outside of the threshold value. The processor is further programmed to (iv) plot the value of the data point in histogram with a first color when the data point value is determined to be outside of the threshold value, (v) plot the data point value in histogram with a second color when the data point value is determined to be not outside of the threshold value, and (vi) display the histogram containing plotted data point values of the first color and plotted data point values of the second color to enable a user to quickly and easily identify a potential skew condition.

Abstract: Apparatus, systems, and methods may operate to monitor database system resource consumption over various time periods, in conjunction with scheduled data loading, data export, and query operations. Additional activities may include generating a database system resource consumption map based on the monitoring, and adjusting database system workload throttling to accommodate predicted database system resource consumption based on the resource consumption map and current system loading, prior to the current database resource consumption reaching a predefined critical consumption level. The current system loading may be induced by data loading, data export, or query activity. Other apparatus, systems, and methods are disclosed.