Abstract: An Application Programming Interface (API) processes object storage requests in an object storage format that are received from client applications. A mapper maps the object storage formatted requests into database statements and executes the statements within a database management system. Results returned from the statements are mapped by the mapper to object-storage constructs and provided to the API in object storage format. The object storage formatted requests are provided from the API to the client applications to satisfy the original client applications' requests.

Abstract: A multi-staged sample and seed machine-learning training technique is presented. A sample proportion of a training data set is fed to a machine-learning algorithm (MLA) for purposes of configuring functions of the MLA to predict an output with a desired degree of accuracy. When iterating the sample proportion, if a deviation in an incrementally produced current accuracy of the MLA does not exceed a threshold, the sampled proportion is increased. This continues until the current degree of accuracy meets or exceeds the desired degree of accuracy, which is an indication that the functions of the MLA are configured as a desired model for producing the predicted output when the MLA is presented with input that may or may not have been associated with the training data set.

Abstract: An Application Programming Interface (API) processes object storage requests in an object storage format that are received from client applications. A mapper maps the object storage formatted requests into database statements and executes the statements within a database management system. Results returned from the statements are mapped by the mapper to object-storage constructs and provided to the API in object storage format. The object storage formatted requests are provided from the API to the client applications to satisfy the original client applications' requests.

Abstract: Methods and systems receive a query, receive a control statement that represents at least a portion of a desired execution plan associated with the query, and produce an execution plan that conforms to the desired plan to the extent that the produced plan preserves the semantics of the query, without requiring an exhaustive search. These methods also enable realization of bushy shaped plans that are outside the scope of the optimizer search space.

Abstract: Methods and systems receive a normalized query tree and analyze the tree to collect information about join operators and their children, and tables in an associated query. This information is then made available to a rule based optimizer that is configured to produce, from the normalized query tree, an execution plan. In addition, in at least some embodiments, an extensible framework is provided for join order optimization via the use of a multi-join operator and multi-join rules as part of the general framework of a query optimizer.

Abstract: Methods and systems receive a normalized query tree and analyze the tree to collect information about join operators and their children, and tables in an associated query. This information is then made available to a rule based optimizer that is configured to produce, from the normalized query tree, an execution plan. In addition, in at least some embodiments, an extensible framework is provided for join order optimization via the use of a multi-join operator and multi-join rules as part of the general framework of a query optimizer.

Abstract: Methods and systems receive a query, receive a control statement that represents at least a portion of a desired execution plan associated with the query, and produce an execution plan that conforms to the desired plan to the extent that the produced plan preserves the semantics of the query, without requiring an exhaustive search. These methods also enable realization of bushy shaped plans that are outside the scope of the optimizer search space.

Abstract: A system that provides compressed histogram data comprises a compiler histogram interface that obtains histogram data about a table in a database, and a dynamic histogram compressor that receives output from the compiler histogram interface, input regarding a database query and table information to produce compressed histogram data for use by an optimizer.