Abstract: A computer program product that includes a computer useable storage medium to store a computer readable program that, when executed on a computer, causes the computer to perform operations, including operations to receive a query for which a query execution plan (QEP) is to be computed, divide a search space into a plurality of subproblems for which constituent QEPs are to be created, partition the plurality of subproblems into a plurality of partitions, and allocate each of the plurality of partitions to a thread of a plurality of threads within a multiple thread architecture. Possible QEPs describe a search space. Each subproblem references one or more quantifiers and each of the subproblems within a partition references the same number of quantifiers. A partition containing subproblems referencing fewer quantifiers is executed before a partition containing subproblems referencing more quantifiers.

Abstract: A computer program product that includes a computer useable storage medium to store a computer readable program that, when executed on a computer, causes the computer to perform operations, including operations to receive a query for which a query execution plan (QEP) is to be computed, divide a search space into a plurality of subproblems for which constituent QEPs are to be created, partition the plurality of subproblems into a plurality of partitions, and allocate each of the plurality of partitions to a thread of a plurality of threads within a multiple thread architecture. Possible QEPs describe a search space. Each subproblem references one or more quantifiers and each of the subproblems within a partition references the same number of quantifiers. A partition containing subproblems referencing fewer quantifiers is executed before a partition containing subproblems referencing more quantifiers.

Abstract: A method for predictable query execution through early materialization is provided. The method deals with the problem of cardinality misestimation in query execution plans, by pre-executing sub-plans on a query execution plan that have questionable estimates and collecting statistics on the output of these sub-plans. If needed, the overall query execution plan is changed in light of these statistics, before optimizing and executing the remainder of the query.

Abstract: A way for progressively refining a query execution plan during query execution in a federated data system is provided. Re-optimization constraints are placed in the query execution plan during query compilation. When a re-optimization constraint is violated during query execution, a model of the query execution plan is refined using a partially executed query to form a new query execution plan. The new query execution plan is compiled. The compiled new query execution plan is executed.

Abstract: A method, computer program product, and system for managing statistical views in a database system are provided. The method, computer program product, and system provide for collecting data relating to optimization and execution of a workload in the database system and automatically generating a set of one or more statistical views based on the collected optimization and execution data.

Abstract: A novel method is employed for collecting optimizer statistics for optimizing database queries by gathering feedback from the query execution engine about the observed cardinality of predicates and constructing and maintaining multidimensional histograms. This makes use of the correlation between data columns without employing an inefficient data scan. The maximum entropy principle is used to approximate the true data distribution by a histogram distribution that is as “simple” as possible while being consistent with the observed predicate cardinalities. Changes in the underlying data are readily adapted to, automatically detecting and eliminating inconsistent feedback information in an efficient manner. The size of the histogram is controlled by retaining only the most “important” feedback.

Abstract: A method for consistent selectivity estimation based on the principle of maximum entropy (ME) is provided. The method efficiently exploits all available information and avoids the bias problem. In the absence of detailed knowledge, the ME approach reduces to standard uniformity and independence assumptions. The disclosed method, based on the principle of ME, is used to improve the optimizer's cardinality estimates by orders of magnitude, resulting in better plan quality and significantly reduced query execution times.

Abstract: A method and system for method for accelerating execution of a query on a federated database system is disclosed. The federated database system is associated with an external data source, which is used by the query. The query is performed based upon a query execution plan. The method and system include generating an optimizer query for the external data source utilized by the query. The optimizer query is based on the query and obtains data related to the external data source. The method and system further include providing the optimizer query to the external data source and collecting at least one resultant from the optimizer query for use in generating a future query execution plan.

Abstract: An autonomic system directed to opportunistically store captured data from at least two writer processes executing in an autonomic system. The method includes: creating a pool of storage locations in which data can be stored by the at least two writing processes, one of the at least two writer processes capturing data to be stored; selecting a storage location from the pool for the one of said at least two writer processes; and determining if the selected file is available for writing by the one of the at least two writer processes and writing the captured data to the storage location if it is available.

Abstract: A system and a priori method of discovering dependencies between relational database column pairs and application of discoveries to query optimization is provided. For each candidate column pair remaining after simultaneously generating column pairs, pruning pairs not satisfying specified heuristic constraints, and eliminating pairs with trivial instances of correlation, a random sample of data values is collected. A candidate column pair is tested for the existence of a soft functional dependency (FD), and if a dependency is not found, statistically tested for correlation using a robust chi-squared statistic. Column pairs for which either a soft FD or a statistical correlation exists are prioritized for recommendation to a query optimizer, based on any of: strength of dependency, degree of correlation, or adjustment factor; statistics for recommended columns pairs are tracked to improve selectivity estimates.

Abstract: “Determining Validity Ranges of Query Plans Based on Suboptimality” A method for approximating a validity range for a domain of cardinalities of input to an optimal query plan is provided. Such a validity range is iteratively approximated using a modified Newton-Raphson method to find roots of cost functions for optimal and alternative query plans, respectively. The Newton-Raphson method is combined with a method of incrementing roots of cost functions, known as input cardinalities, such that discontinuous and non-differentiable points in cost functions are avoided. In this manner, input cardinalities remain within a domain for which a valid range can be specified. Additionally, a robustness measure is determined by a sensitivity analysis performed on an approximated validity range.

Abstract: An optimizer function of a Relational Database Management System (RDBMS) generates alternative query execution plans (QEPs) for executing a query, provides an execution model of each of the QEPs, chooses one of the QEPs for execution based on the model associated therewith, and exploits an empirical measurement from the execution of the chosen QEP to validate the model associated therewith, by determining whether the model is in error, and by computing one or more adjustments to the model to correct the determined error.

Abstract: An optimizer function of a Relational Database Management System (RDBMS) generates alternative query execution plans (QEPs) for executing a query, provides an execution model of each of the QEPs, chooses one of the QEPs for execution based on the model associated therewith, and exploits an empirical measurement from the execution of the chosen QEP to validate the model associated therewith, by determining whether the model is in error, and by computing one or more adjustments to the model to correct the determined error.