Abstract: A technique that uses a weighted divide and conquer approach for clustering a set S of n data points to find k final centers. The technique comprises 1) partitioning the set S into P disjoint pieces S1, . . . , Sp; 2) for each piece Si, determining a set Di of k intermediate centers; 3) assigning each data point in each piece Si to the nearest one of the k intermediate centers; 4) weighting each of the k intermediate centers in each set Di by the number of points in the corresponding piece Si assigned to that center; and 5) clustering the weighted intermediate centers together to find said k final centers, the clustering performed using a specific error metric and a clustering method A.

Abstract: A method of global data placement. The method includes assigning one or more workloads to one or more compute servers such that each workload flows to one compute server, assigning the data chunks that the workloads accesses to one or more storage servers, and determining how the workloads access the data.

Abstract: A technique that uses a weighted divide and conquer approach for clustering a set S of n data points to find k final centers. The technique comprises 1) partitioning the set S into P disjoint pieces S1, . . . , SP; 2) for each piece Si, determining a set Di of k intermediate centers; 3) assigning each data point in each piece Si to the nearest one of the k intermediate centers; 4) weighting each of the k intermediate centers in each set Di by the number of points in the corresponding piece Si assigned to that center; and 5) clustering the weighted intermediate centers together to find said k final centers, the clustering performed using a specific error metric and a clustering method A.

Abstract: A method for predicting parts for onsite repair which takes into account a repair history and the costs associated with mis-predictions. Parts for onsite repair of a product are predicted by determining an expected waste for one or more parts of the product. The parts having a lowest expected waste are selected and sent to the onsite repair. The expected waste indicates parts that are responsible for high support costs and highlights the mistakes being made and scores the mistakes by actual cost.

Abstract: A technique that uses a weighted divide and conquer approach for clustering a set S of n data points to find k final centers. The technique comprises 1) partitioning the set S into P disjoint pieces S1, . . . , SP; 2) for each piece S1, determining a set D1 of k intermediate centers; 3) assigning each data point in each piece Si to the nearest one of the k intermediate centers; 4) weighting each of the k intermediate centers in each set D1 by the number of points in the corresponding piece S1 assigned to that center; and 5) clustering the weighted intermediate centers together to find said k final centers, the clustering performed using a specific error metric and a clustering method A.

Abstract: A divide and conquer method clusters a set of points to identify at least one centroid. A plurality of “r” subsets of points from the set of points are assigned into “r” partitions. The computer processes the subset of points to generate a plurality of partition centroids, Q, k for each of the r partitions, so as to optimize a clustering metric. The partition centroids are merged into a set of partition centroids, and are stored in main memory of the computer. Thereafter, the partition centroids are processed by accessing the memory to generate a plurality of centroids, c, one for each of the k clusters, so as to optimize a clustering metric. Data incremental and feature incremental divide and conquer techniques are disclosed. Furthermore, the divide and conquer technique has application for implementation in parallel processing computer systems.