Abstract: Embodiment of the invention related to characterization and optimization of tracks on a disk, magnetic or optical by determining an input/output (I/O) characteristics for a plurality of blocks on a disk by a processor, wherein the characteristics comprise at least one of a data size or data type or an association between the data files, and determining a plurality of parameters affecting operation performed on the disk for placement of the plurality of data clusters.

Abstract: Optimization of tracks on a hard disk includes: determining I/O characteristics for data clusters to be stored on the hard disk; generating a set of solutions for each possible placement configuration; for each solution, calculating a plurality of cost functions using the I/O characteristics for the plurality of data clusters; for each solution, calculating a membership value using the cost function values; for each solution, calculating a fitness value using the membership value; retaining the solutions with the fitness value greater than a predetermined threshold; determining whether at least one stopping condition has been met; if not, adding new solutions to the retained solutions to generate the next set of solutions; and repeating the calculating the membership value, the calculating the fitness value, and the retaining the solutions with the fitness value greater than the predetermined threshold until at least one stopping condition has been met.

Abstract: A method, system, and computer program product for mitigating adjacent track erasures in hard disks, includes: determining input/output (I/O) characteristics for a plurality of blocks on a hard disk; assigning the plurality of blocks to a plurality of categories of I/O characteristics by the processor; and clustering content of the blocks assigned to the same category in one or more continuous tracks on the hard disk. Each block is assigned to one category. Blocks with similar I/O characteristics are clustered on one or more continuous tracks. By performing this clustering, blocks with a high number of I/O operations are grouped and stored on fewer tracks than if they were scattered across numerous tracks. This reduces the number of tracks experiencing a high number of I/O operations, and in turn, the amount of refreshing of adjacent tracks is reduced.

Abstract: A system and computer readable medium for discovering gene regulatory models using relational fuzzy models. A system is provided that includes a data selection system that clusters gene expression data into a set of clusters and identifies a representative subset of genes from the set of clusters; and a relational fuzzy modeling system that builds a relational fuzzy model using the representative subset.

Abstract: A system and method for interpreting electrocardiogram data. A system is provided that clusters raw electrocardiogram (EKG) data into clusters of EKG data; generates a predictive model for each cluster of EKG data; compares inputted patient EKG data with the clusters of EKG data to identify a matching cluster of EKG data; applies the predictive model associated with the matching cluster of EKG data to the inputted patient EKG data; and outputs diagnostic data.

Abstract: A system and computer readable medium for discovering gene regulatory models using relational fuzzy models. A system is providing that includes a data selection system that clusters gene expression data into a set of clusters and identifies a representative subset of genes from the set of clusters; and a relational fuzzy modeling system that builds a relational fuzzy model using the representative subset.

Abstract: A method for discovering gene regulatory models using relational fuzzy models. A method is provided that includes clustering gene expression data into a set of clusters and identifying a representative subset of genes from the set of clusters; and building a relational fuzzy model using the representative subset.

Abstract: A system and method for allocating computing resources among a plurality of units in a distributed network. A system is provided that includes: a relational fuzzy modeling system for providing a relational fuzzy model based on historical resource parameter data and historical system parameter data, and for predicting and allocating resource requirements for each unit based on the relational fuzzy model; a normalization system for ensuring that a sum of the predicted resource requirements is no more than a total available amount of actual resources available on the plurality of units; and an extra resource allocation system for allocating extra resources when the sum of the predicted resource requirements is less than the total available amount of resources available on the plurality of units.

Abstract: Systems, methods, and computer products that rank and explain dimensions associated with exceptions in multidimensional data. The present invention assists the data analyst by providing a simplified view of the multidimensional data that enables analysis of the important results of data exception exploration. Further, the preferred embodiment of the present invention incorporates the effect of weighting factors associated with the importance of the data along with an analysis of the numerical contribution from each dimension. The weighting factors may be based on data mining results or may be obtained from the user. This enables data analysts to obtain information about the value of the data that is presented.

Abstract: Systems, methods, and computer products that rank and explain dimensions associated with exceptions in multidimensional data. The present invention assists the data analyst by providing a simplified view of the multidimensional data that enables analysis of the important results of data exception exploration. Further, the preferred embodiment of the present invention incorporates the effect of weighting factors associated with the importance of the data along with an analysis of the numerical contribution from each dimension. The weighting factors may be based on data mining results or may be obtained from the user. This enables data analysts to obtain information about the value of the data that is presented.