Abstract: A unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.

Abstract: A unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.

Abstract: A unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.

Abstract: A unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.

Abstract: A unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.

Abstract: A unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.

Abstract: A unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.

Abstract: A server has a processor and a memory storing instructions executed by the processor to access scheduling tools including an entity workload profile with a work flow tasks schedule and work flow task dependencies. Processed data associated with a work flow task within the entity workload profile is identified. The work flow task dependencies are analyzed to alter the work flow tasks schedule to prioritize work flow tasks that rely upon the processed data.

Abstract: A server has a processor and a memory storing instructions executed by the processor to access scheduling tools including an entity workload profile with a work flow tasks schedule and work flow task dependencies. Processed data associated with a work flow task within the entity workload profile is identified. The work flow task dependencies are analyzed to alter the work flow tasks schedule to prioritize work flow tasks that rely upon the processed data.