Abstract: Methods and systems are provided for tuning memory allocated among a plurality of applications in a data processing system. In one implementation, the method includes generating memory benefit data for the plurality of applications, comparing the generated memory benefit data associated with each of the plurality of applications, and dynamically reallocating memory from one or more of the plurality of applications to one or more other of the plurality of applications based on the comparison. A method and system is also provided for tuning memory allocated among a plurality of individual memory consumers for a given application.

Abstract: Methods and systems are provided for executing a simulation of an operation in a data processing system. In one implementation, the method includes executing an operation under a first set of conditions, determining a characteristic associated with the execution of the operation under the first set of conditions, and executing a simulation of the operation under a second set of conditions different from the first set of conditions. The simulation of the operation is constrained by the determined characteristic. The method can further include determining a cost/benefit of executing the operation under a set of conditions different from the first set of conditions based at least in part on the simulation.

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 system and method of evaluating queries in distributed databases with MQTs comprises deriving MQTs; replicating the derived MQTs from a local server to at least one remote server; and distributing data and replicated derived MQTs to a plurality of other remote servers, wherein the distributing increases overall query execution efficiency. The databases may comprise heterogeneous databases. The query execution efficiency comprises observed response time at a frontend database and associated costs comprising computational central processing unit costs, input/output costs, and network communication costs. All of the associated costs comprise statistically estimated costs. The method further comprises running a MQT advisor at a frontend database, and considering the costs of at least one MQT placed at the frontend database. The method further comprises running a MQT advisor at a non-frontend database.

Abstract: Disclosed is a method for directing a data processing system, the data processing system and an article of manufacture. The data processing system has memory for storing a database. The method directs the data processing system to configuring the database based on a database workload having a performance metric. The method includes selecting a plurality of database characteristics based upon the database workload, each of the database characteristics having an associated estimated performance metric for the database workload, selecting a set of database characteristics from the plurality of database characteristics for which the associated estimated performance metric is lower than the database workload performance metric, and configuring the database using the selected set of database characteristics.

Abstract: Disclosed is a data processing system implemented method, a data processing system and an article of manufacture for improving execution efficiency of a database workload to be executed against a database. The database includes database tables, and the database workload identifies at least one of the database tables. The data processing system includes an identification module for identifying candidate database tables being identifiable in the database workload, the identified candidate database tables being eligible for organization under a clustering schema, a selection module for selecting the identified candidate tables according to whether execution of the database workload is improved if the selected identified candidate table is organized according to the clustering scheme, and an organization module for organizing the clustering schema of the selected organized identified candidate tables prior to the database workload being execution against the database.

Abstract: A method, apparatus, and computer instructions for selecting and recommending of clustering indexes are provided. Baseline run time cost estimates are calculated based on database designs. Workload benefit of a candidate clustering design is calculated and compared to the baseline costs. If the workload benefit outweighs the baseline costs, clustering dimension solutions originated from the record identifier (RID) based index are identified. A clustering design is selected based on the identified clustering dimension solutions and the total number of recommended clustering dimensions for a given table. Based on the number of dimensions observed, either a multidimensional clustering solution or a RID based clustering index is recommended.

Abstract: The present invention provides a method, system and program for optimizing compression of a workload processed by a database management system. In an embodiment of the present invention a method of optimizing the compression of database workloads is provided. Initially, an estimate of a cost of execution for each query according to a defined metric such as execution time or memory consumption is determined. A sub-set of queries is then selected from the workload in order of the most costly to least costly relative to the defined metric for compression according to either a predetermined compression threshold percentage or a threshold percentage derived from an allotted workload execution time. Compression is then performed on the selected sub-set of queries (i.e. those that will benefit the most from the compression) to achieve a net beneficial trade-off between the cost of workload compression and the cost of workload execution.