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 system has a master node with instructions executed by a master node processor to receive a query over a network from a client machine and distribute query segments over the network. Worker nodes receive the query segments. Each worker node includes instructions executed by a worker node processor to construct from a columnar file a term map characterizing a term from the columnar file, row identifications from the columnar file and a Boolean indicator for each row identification that characterizes whether the term is present in the row specified by the row identification. The term map is cached in dynamic memory. Values responsive to the query segment are collected from the term map. The values are sent to the master node. The master node aggregates values from the worker nodes to form a result that is returned to the client machine over the network.

Abstract: A system has a master node with instructions executed by a master node processor to receive a query over a network from a client machine and distribute query segments over the network. Worker nodes receive the query segments. Each worker node includes instructions executed by a worker node processor to construct from a columnar file a term map characterizing a term from the columnar file, row identifications from the columnar file and a Boolean indicator for each row identification that characterizes whether the term is present in the row specified by the row identification. The term map is cached in dynamic memory. Values responsive to the query segment are collected from the term map. The values are sent to the master node. The master node aggregates values from the worker nodes to form a result that is returned to the client machine over the network.