Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method for scheduling a single subset of jobs of a set of jobs satisfying a range constraint of number of jobs, wherein the jobs of the set of jobs share resources in a computing system, each job being assigned a weight, w, indicative of the memory usage of the job in case of its execution in the computer system, the method including: for each number of jobs, x, satisfying the range constraint, determining from the set of jobs a first subset of jobs using a knapsack problem, wherein the knapsack problem is adapted to select by using the weights the first subset of jobs having the number of jobs and having a maximal total memory usage below the current available memory of the computer system, and selecting the single subset from the first subset.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: Joining sets of tables on a join attribute using a predefined amount of memory. A set of tables is stored on disk storage. The largest table is a streaming table. The remaining tables form a subset of tables. For each subset table, a hash table is generated, resulting in a subset of hash tables. A group of not yet joined hash tables is determined such that the size of the tables is smaller than the amount of memory. The tables are loaded to the memory. A hash join is performed between the streaming table and the group of tables, forming a temporal joined table. If the subset includes a not yet joined table with the streaming table, a further set of tables with the not yet joined and the temporal joined table is formed. The above operations are repeated using the further set of tables as the set of tables.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: A method of bootstrap sampling a dataset is described. With a process node, a series of random integers is generated. An assignment map is created. The assignment map includes a row identifier for each row of data of the dataset. A plurality of bootstrap sample identifiers defined by the series are assigned to at least one row identifier. An output table created from the assignment map. Rows of the output table include each instance of the bootstrap sample identifiers, the row identifier assigned with the bootstrap sample identifier, and data of the row.

Abstract: One or more processors receives data that includes a plurality of light paths of an optical network. The one or more processors partition the plurality of light paths into a plurality of abutting segments such that a given pair of abutting segments have a combined length of, at most, a maximum distance a signal can travel in the light path of the pair before the signal suffers one or both of dispersion and attenuation in excess of a threshold. The One or more processors determine optical regenerator placement in the optical network using a first polynomial-time algorithm. The placement optical regenerators in the network is based, at least in part, on the partitioning.

Abstract: One or more processors receives data that includes a plurality of light paths of an optical network. The one or more processors partition the plurality of light paths into a plurality of abutting segments such that a given pair of abutting segments have a combined length of, at most, a maximum distance a signal can travel in the light path of the pair before the signal suffers one or both of dispersion and attenuation in excess of a threshold. The One or more processors determine optical regenerator placement in the optical network using a first polynomial-time algorithm. The placement optical regenerators in the network is based, at least in part, on the partitioning.

Abstract: A method for scheduling a single subset of jobs of a set of jobs satisfying a range constraint of number of jobs, wherein the jobs of the set of jobs share resources in a computing system, each job being assigned a weight, w, indicative of the memory usage of the job in case of its execution in the computer system, the method including: for each number of jobs, x, satisfying the range constraint, determining from the set of jobs a first subset of jobs using a knapsack problem, wherein the knapsack problem is adapted to select by using the weights the first subset of jobs having the number of jobs and having a maximal total memory usage below the current available memory of the computer system, and selecting the single subset from the first subset.