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.