Abstract: A method of accessing objects with fine-grained access control (FGAC) in a relational database management system (RDBMS) storing a segmented column-major database. For each object with access restrictions, an artificial neural network (ANN), is trained by generating an equally distributed segment map of segmented data entries, so that the map reproduces the row disposition in the unsegmented object. When a user access request is received, these ANNs are referred to determine if any of the objects to be accessed are subject to access restrictions. If that is the case, then the ANN creates a pseudo-view construct of its associated object which is limited to data entries that the user has permission to access. The pseudo-views are then injected into the user access request to embed the fine-grained access controls for subsequent processing of the request, which can then proceed without further regard to user-specific access restrictions.

Abstract: Embodiments of the present invention provide systems and methods for associating rule mining in data comprising first and second variables, by generating a first and second filtering bitmap. The first filtering bitmap represents a set of values for the second variable for each of a plurality of values of the first variable. The second filtering bitmap represents, associations between frequent values of the second variable, which enables the identification of frequent 2-pattern itemsets. Thus, by performing first logical operations on the frequent 2-pattern itemsets, frequent high order-pattern itemsets may be identified. A max pattern itemset may be identified among the frequent high order-pattern itemsets. As a result, embodiments may mitigate performance and stability problems associated with conventional association rule mining approaches.

Abstract: Embodiments of the present invention provide systems and methods for associating rule mining in data comprising first and second variables, by generating a first and second filtering bitmap. The first filtering bitmap represents a set of values for the second variable for each of a plurality of values of the first variable. The second filtering bitmap represents, associations between frequent values of the second variable, which enables the identification of frequent 2-pattern itemsets. Thus, by performing first logical operations on the frequent 2-pattern itemsets, frequent high order-pattern itemsets may be identified. A max pattern itemset may be identified among the frequent high order-pattern itemsets. As a result, embodiments may mitigate performance and stability problems associated with conventional association rule mining approaches.

Abstract: Embodiments of the present invention provide systems and methods for associating rule mining. By way of example, embodiments obviate or mitigate performance and stability problems associated with conventional association rule mining approaches, by proposing concepts implementing association rule mining using rolling filtering bitmaps over dynamic data.

Abstract: Embodiments of the present invention provide systems and methods for associating rule mining. By way of example, embodiments obviate or mitigate performance and stability problems associated with conventional association rule mining approaches, by proposing concepts implementing association rule mining using rolling filtering bitmaps over dynamic data.

Abstract: A method of accessing objects with fine-grained access control (FGAC) in a relational database management system (RDBMS) storing a segmented column-major database. For each object with access restrictions, an artificial neural network (ANN), is trained by generating an equally distributed segment map of segmented data entries, so that the map reproduces the row disposition in the unsegmented object. When a user access request is received, these ANNs are referred to determine if any of the objects to be accessed are subject to access restrictions. If that is the case, then the ANN creates a pseudo-view construct of its associated object which is limited to data entries that the user has permission to access. The pseudo-views are then injected into the user access request to embed the fine-grained access controls for subsequent processing of the request, which can then proceed without further regard to user-specific access restrictions.

Abstract: Provided are a system, method and computer program product for redistribution of data in an online shared nothing database, said shared nothing database comprising a plurality of original partitions and at least one new partition.

Abstract: Provided are a system, method and computer program product for redistribution of data in an online shared nothing database, said shared nothing database comprising a plurality of original partitions and at least one new partition.

Abstract: Provided are a system, method and computer program product for redistribution of data in an online shared nothing database, said shared nothing database comprising a plurality of original partitions and at least one new partition.

Abstract: Provided are a system, method and computer program product for redistribution of data in an online shared nothing database, said shared nothing database comprising a plurality of original partitions and at least one new partition.

Abstract: A computer-implemented method of testing a database can include the steps of constructing one or more data generation rules, receiving one or more user-defined objects associated with the database, and generating a set of data entries using the generation rules, wherein at least a portion of the generated data is correlated data.

Abstract: A method for real time device evaluation starts a sampling tool for a sample, receives sample data from a sample data structure and sends the sample data to a target device. The method saves sample data operation results from the target device to form a sample result, and in response to determining that the sample is complete, calculates a target device transfer rate in real time using the sample result and calculates a target device overhead in real time using the target device transfer rate and the sample result, sending the target device transfer rate and the target device overhead.

Abstract: A method for real time device evaluation starts a sampling tool for a sample, receives sample data from a sample data structure and sends the sample data to a target device. The method saves sample data operation results from the target device to form a sample result, and in response to determining that the sample is complete, calculates a target device transfer rate in real time using the sample result and calculates a target device overhead in real time using the target device transfer rate and the sample result, sending the target device transfer rate and the target device overhead.

Abstract: Database partition monitoring and dynamic logical partition reconfiguration in support of an autonomic self-tunable database management system are provided by an automated monitor that monitors one or more resource parameters in a logical partition running a database application in a logically partitioned data processing host. The monitor initiates dynamic logical partition reconfiguration in the event that the parameters vary from predetermined parameter values. In particular, the monitor can initiate removal of resources if one of the resource parameters is being underutilized and initiate addition of resources if one of the resource parameters is being overutilized. The monitor can also calculate an amount of resources to be removed or added. The monitor can interact directly with a dynamic logical partition reconfiguration function of the data processing host or it can utilize an intelligent intermediary that listens for a partition reconfiguration suggestion from the monitor.

Abstract: A method for real time device evaluation starts a sampling tool for a sample, receives sample data from a sample data structure and sends the sample data to a target device. The method saves sample data operation results from the target device to form a sample result, and in response to determining that the sample is complete, calculates a target device transfer rate in real time using the sample result and calculates a target device overhead in real time using the target device transfer rate and the sample result, sending the target device transfer rate and the target device overhead.

Abstract: Illustrative embodiments provide a computer implemented method, a data processing system and a computer program product for scalable scheduling of tasks in heterogeneous systems is provided. According to one embodiment, the computer implemented method comprises fetching a set of tasks to form a received input, estimating run times of tasks, calculating average estimated completion times of tasks, producing a set of ordered tasks from the received input to form a task list, identifying a machine to be assigned, and assigning an identified task from the task list to an identified machine.

Abstract: A present invention for estimating a confidence interval includes: receiving a dataset comprising information on software product defects found by a first customer and a second customer, the information comprising numbers of cases of software product defects found by the first customer and the second customer; determining a probability for each case of found software product defect using the frequencies and a total number of software product defects found; determining a plurality of bootstrapping draws from the cases of found software product defects in the dataset; determining an array of conditional probabilities that the first customer may find a given software product defect given that the second customer found the given software product defect, using the numbers of the cases and the probabilities corresponding to the cases in each bootstrapping draw; and determining a confidence interval for the determined plurality of arrays of conditional probabilities.

Abstract: A method for real time device evaluation starts a sampling tool for a sample, receives sample data from a sample data structure and sends the sample data to a target device. The method saves sample data operation results from the target device to form a sample result, and in response to determining that the sample is complete, calculates a target device transfer rate in real time using the sample result and calculates a target device overhead in real time using the target device transfer rate and the sample result, sending the target device transfer rate and the target device overhead.

Abstract: An automated monitor monitors one or more resource parameters in a logical partition running a database application in a logically partitioned data processing host. The monitor initiates dynamic logical partition reconfiguration in the event that the parameters vary from predetermined parameter values. In particular, the monitor can initiate removal of resources if one of the resource parameters is being underutilized and initiate addition of resources if one of the resource parameters is being overutilized. The monitor can also calculate an amount of resources to be removed or added. The monitor can interact directly with a dynamic logical partition reconfiguration function of the data processing host or it can utilize an intelligent intermediary that listens for a partition reconfiguration suggestion from the monitor. In the latter configuration, the listener can determine where available resources are located and attempt to fully or partially satisfy the resource needs suggested by the monitor.

Abstract: An automated monitor monitors one or more resource parameters in a logical partition running a database application in a logically partitioned data processing host. The monitor initiates dynamic logical partition reconfiguration in the event that the parameters vary from predetermined parameter values. In particular, the monitor can initiate removal of resources if one of the resource parameters is being underutilized and initiate addition of resources if one of the resource parameters is being overutilized. The monitor can also calculate an amount of resources to be removed or added. The monitor can interact directly with a dynamic logical partition reconfiguration function of the data processing host or it can utilize an intelligent intermediary that listens for a partition reconfiguration suggestion from the monitor. In the latter configuration, the listener can determine where available resources are located and attempt to fully or partially satisfy the resource needs suggested by the monitor.