Abstract: The disclosed systems and methods for intelligent heterogeneous computation directed to receiving monitoring data and a set of training data, wherein the monitoring data includes an occupancy rate of a preprocessed data queue and a utilization factor of accelerating devices, generating a resource computation job list in accordance with the monitoring data, forwarding jobs, in the resource computation job list to be executed on a central processing unit (CPU), to a CPU worker queue, forwarding control messages to the CPU worker queue, wherein the control messages are associated with jobs in the resource computation job list to be executed on the accelerating devices, and executing, by the accelerating devices, jobs in the resource computation job list to be executed on the accelerating devices.

Abstract: The disclosed systems and methods are directed to generating cache IDs for each of a plurality of AI training pipelines, accessing training data elements included in a training data set, generating IDs corresponding to the training data elements, receiving the data IDs and an associated cache ID, randomizing the data IDs, selecting a subset of the randomized data IDs, fetching the training data elements previously cached by a cache node, receiving a portion of the training data elements present in the caching server corresponding to the subset of randomized data IDs, forwarding the portion of the training data elements present in the caching server to at least one consumer node, fetching the remaining training data elements associated with the subset of randomized data IDs from the training data set, and forwarding the remaining training data elements to at least one transformation node for training the neural network.

Abstract: A method for transforming a scalar sub-query to join with non-provable singleton output, responsive to receiving a parsed representation of a statement, containing a scalar operation, which is correlated to predetermined types of objects, by analyzing the parsed representation to determine eligibility for transformation of any scalar operations. Responsive to a determination of eligibility, a query engine injects additional processing into the parsed representation to defer checking of a 1 row condition required by statement semantics. The query engine further injects processing to utilize a result of the additional processing to validate a single row is produced for each GROUPING/PARTITION key. The parsed representation is modified to remove the 1 row condition. The query engine decorrelates the parsed representation using a predetermined method absent a requirement for a unique key to be present in a predicate of the scalar operation.

Abstract: A method, computer system, and a computer program product for transforming a scalar sub-query to join with non-provable singleton output, responsive to receiving a parsed representation of a statement, containing a scalar operation, which is correlated to predetermined types of objects, by analyzing the parsed representation to determine eligibility for transformation of any scalar operations. Responsive to a determination of eligibility, a query engine injects additional processing into the parsed representation to defer checking of a 1 row condition required by statement semantics. The query engine further injects processing to utilize a result of the additional processing to validate a single row is produced for each GROUPING/PARTITION key. The parsed representation is modified to remove the 1 row condition. The query engine decorrelates the parsed representation using a predetermined method absent a requirement for a unique key to be present in a predicate of the scalar operation.

Abstract: A method for transforming a scalar sub-query to join with non-provable singleton output, responsive to receiving a parsed representation of a statement, containing a scalar operation, which is correlated to predetermined types of objects, by analyzing the parsed representation to determine eligibility for transformation of any scalar operations. Responsive to a determination of eligibility, a query engine injects additional processing into the parsed representation to defer checking of a 1 row condition required by statement semantics. The query engine further injects processing to utilize a result of the additional processing to validate a single row is produced for each GROUPING/PARTITION key. The parsed representation is modified to remove the 1 row condition. The query engine decorrelates the parsed representation using a predetermined method absent a requirement for a unique key to be present in a predicate of the scalar operation.

Abstract: A method, computer system, and a computer program product for transforming a scalar sub-query to join with non-provable singleton output, responsive to receiving a parsed representation of a statement, containing a scalar operation, which is correlated to predetermined types of objects, by analyzing the parsed representation to determine eligibility for transformation of any scalar operations. Responsive to a determination of eligibility, a query engine injects additional processing into the parsed representation to defer checking of a 1row condition required by statement semantics. The query engine further injects processing to utilize a result of the additional processing to validate a single row is produced for each GROUPING/PARTITION key. The parsed representation is modified to remove the 1 row condition. The query engine decorrelates the parsed representation using a predetermined method absent a requirement for a unique key to be present in a predicate of the scalar operation.

Abstract: A computer-implemented method includes receiving an artifact and a problem pattern, transforming the artifact into an abstracted artifact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artifact structure. Any matched portions of the abstracted artifact structure are related back to corresponding result portions of the artifact. The corresponding result portions of the artifact are returned. The method may be embodied in a corresponding computer system or computer program product.

Abstract: A computer-implemented method includes receiving an artifact and a problem pattern, transforming the artifact into an abstracted artifact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artifact structure. Any matched portions of the abstracted artifact structure are related back to corresponding result portions of the artifact. The corresponding result portions of the artifact are returned. The method may be embodied in a corresponding computer system or computer program product.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: A computer-implemented method includes receiving an artefact and a problem pattern, transforming the artefact into an abstracted artefact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artefact structure. Any matched portions of the abstracted artefact structure are related back to corresponding result portions of the artefact. The corresponding result portions of the artefact are returned. The method may be embodied in a corresponding computer system or computer program product.

Abstract: A computer-implemented method includes receiving an artefact and a problem pattern, transforming the artefact into an abstracted artefact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artefact structure. Any matched portions of the abstracted artefact structure are related back to corresponding result portions of the artefact. The corresponding result portions of the artefact are returned. The method may be embodied in a corresponding computer system or computer program product.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: Disclosed is a data processing system implemented method, a data processing system and an article of manufacture for executing a query having a union operator. A data processing system implemented method direct the data processing system to execute a query against a database having data objects. The query has sub-queries and having a union operator. The union operator is operable on sub-queries associated with the query. The database is operatively coupled to the data processing system.

Abstract: Disclosed is a data processing system, a data processing system implemented method and an article of manufacture for executing a query having a union all operator. The data processing system implemented method directs the data processing system to execute a query against a data object. The query has a union all operator and has a set of data modifying operators. The set of data modifying operators is associated with the union all operator. The union all operator references the data object. The data processing system implemented method includes preventing the union all operator from being applied to the data object, and applying the set of data modifying operators against the data object.

Abstract: Disclosed is a data processing system implemented method, a data processing system and an article of manufacture for executing a query having a union operator. The data processing system implemented method directs the data processing system to process a query against data objects. The data objects are operatively coupled to the data processing system. The query includes a parent operator. The parent operator references a union operator. The union operator references sub-queries. The sub-queries reference the data objects. The data processing system implemented method includes noting a set of partitionings for the union operator, the noted set of partitionings being based on the sub-queries and being based on the data objects reference by the sub-queries, and executing the query having the union operator, the execution of the query being based on the noted set of partitionings and the parent operator.

Abstract: Methods and systems are provided to facilitate the optimization process of existing relational database managers when processing complex queries. Specialized constraining clauses are inserted in or added to SQL queries, which do not affect the semantics of the queries. This operation causes the RDBMS to partition the query into sub-queries, and to apply a more efficient optimization for each sub-query. A condition in which the execution time of the modified query substantially exceeds that of the original query may indicate a design flaw in the RDBMS query optimizer.