Abstract: Aspects of the invention include a computer-implemented method that receives, by a processor, an ensemble decision tree and generates, by the processor, native code from the ensemble decision tree. The method compiles, by the processor, the native code into machine language and scores, by the processor, the execution time of the native code. The method dynamically reoptimizes, by the processor, portions of the native code corresponding to the most traversed portion of the ensemble decision tree.

Abstract: A system and method for assessing Pay-As-You-Go (PAYG) Automatic machine learned (AutoML) model pipeline charge to a user on the basis of performance improvement achieved by configuring a model pipeline with performance enhancements relative to a performance obtained by a base model pipeline. The method performs a ranking of pipelines (customized models) based on a user-specified metric (for example, prediction accuracy, run time, F1 score) or combination of metrics. The price for ranked pipelines is specified based on a “surrogate” model where the surrogate model is fit to the base model price and the maximum price for a model. The base model price relates to use of a current cloud resource utilization-based pricing model. The pricing per model pipeline increments on the basis of performance metric(s) in a linear fashion, e.g., using a linear pricing model, or in an exponential fashion, e.g., using a fixed percentage hike price model.

Abstract: Aspects of the invention include a computer-implemented method that receives, by a processor, an ensemble decision tree and generates, by the processor, native code from the ensemble decision tree. The method compiles, by the processor, the native code into machine language and scores, by the processor, the execution time of the native code. The method dynamically reoptimizes, by the processor, portions of the native code corresponding to the most traversed portion of the ensemble decision tree.

Abstract: A computer-implemented method includes: receiving, by a computing device, data comprising a labeled dataset and an unlabeled dataset; generating, by the computing device, a set of heuristics using the labeled dataset; generating, by the computing device, a vector of initial labels by labeling each point in the unlabeled dataset using the set of heuristics; generating, by the computing device, a refined set of heuristics using data-driven active learning; generating, by the computing device, a vector of training labels by automatically labeling each point in the unlabeled dataset using the refined set of heuristics; and outputting, by the computing device, the vector of training labels to a client device or a data repository.

Abstract: A computer-implemented method for prescriptive time-series forecasting, which combines both what-if analysis and goal-seeking analysis. The method comprises building a model for a target metric with a set of predictors, based on historical time-series data, and computing, using the model, a set of forecast values. Using the set of forecast values with respect to a forecasting period, both a set of goals for the target metric and a set of constraints for the predictors are analyzed. A set of updated forecasts based on the analyses with respect to the forecasting period is determined to meet the goals within the set of constraints. The updated set of forecasts is presented with respect to the forecasting period, e.g., using a table, a visualization, and/or an interactive user interface.

Abstract: A computer-implemented method for prescriptive time-series forecasting, which combines both what-if analysis and goal-seeking analysis. The method comprises building a model for a target metric with a set of predictors, based on historical time-series data, and computing, using the model, a set of forecast values. Using the set of forecast values with respect to a forecasting period, both a set of goals for the target metric and a set of constraints for the predictors are analyzed. A set of updated forecasts based on the analyses with respect to the forecasting period is determined to meet the goals within the set of constraints. The updated set of forecasts is presented with respect to the forecasting period, e.g., using a table, a visualization, and/or an interactive user interface.

Abstract: A system determines groups of nodes within a communication network, each group forming a bi-connected component. A simple path is computed between a source node and a target node. Each group of nodes without a node in the simple path is removed producing a resulting set of nodes. Valid connections between the source node and the target node are determined that include only nodes in the resulting set of nodes. Information is provided from the source node to the target node via one or more of the valid connections. A running time for performing the step of determining the groups of nodes through the step of removing each of the groups of nodes without a node in the simple path is linear with respect to a size of a graph of the communication network. A method and computer program product also are provided.

Abstract: A system determines groups of nodes within a communication network, each group forming a bi-connected component. A simple path is computed between a source node and a target node. Each group of nodes without a node in the simple path is removed producing a resulting set of nodes. Valid connections between the source node and the target node are determined that include only nodes in the resulting set of nodes. Information is provided from the source node to the target node via one or more of the valid connections. A running time for performing the step of determining the groups of nodes through the step of removing each of the groups of nodes without a node in the simple path is linear with respect to a size of a graph of the communication network. A method and computer program product also are provided.

Abstract: According to one embodiment of the present invention, a system determines groups of nodes within a network, each group forming a bi-connected component. The system identifies articulation nodes within the network, where each articulation node resides within each connection between a pair of nodes in the network. The system removes from the determined group each node that includes an articulation node between that node and both the source and target nodes to produce a resulting set of nodes. The system determines connections between the source and target nodes based on the resulting set of nodes. Embodiments of the present invention further include a method and computer program product for determining connections between network nodes in substantially the same manners described above.

Abstract: According to one embodiment of the present invention, a system determines groups of nodes within a network, each group forming a bi-connected component. The system identifies articulation nodes within the network, where each articulation node resides within each connection between a pair of nodes in the network. The system removes from the determined group each node that includes an articulation node between that node and both the source and target nodes to produce a resulting set of nodes. The system determines connections between the source and target nodes based on the resulting set of nodes. Embodiments of the present invention further include a method and computer program product for determining connections between network nodes in substantially the same manners described above.

Abstract: A method, and associated computer system and computer program product. Tracking data relating to movement of two or more entities associated with individuals over a past time period is received. A vector is determined for predicted movement of each entity of the two or more entities over a future time period based on at least part of the received tracking data. A determination is made as to whether a first vector for predicted movement of an entity associated with a first individual is converging in time and space with a second vector for predicted movement of an entity associated with a second individual. An event involving the first and second individuals is predicted in response to determining a convergence of the first and second vectors.

Abstract: A computer-implemented method for prescriptive time-series forecasting, which combines both what-if analysis and goal-seeking analysis. The method comprises building a model for a target metric with a set of predictors, based on historical time-series data, and computing, using the model, a set of forecast values. Using the set of forecast values with respect to a forecasting period, both a set of goals for the target metric and a set of constraints for the predictors are analyzed. A set of updated forecasts based on the analyses with respect to the forecasting period is determined to meet the goals within the set of constraints. The updated set of forecasts is presented with respect to the forecasting period, e.g., using a table, a visualization, and/or an interactive user interface.

Abstract: A computer-implemented method for prescriptive time-series forecasting, which combines both what-if analysis and goal-seeking analysis. The method comprises building a model for a target metric with a set of predictors, based on historical time-series data, and computing, using the model, a set of forecast values. Using the set of forecast values with respect to a forecasting period, both a set of goals for the target metric and a set of constraints for the predictors are analyzed. A set of updated forecasts based on the analyses with respect to the forecasting period is determined to meet the goals within the set of constraints. The updated set of forecasts is presented with respect to the forecasting period, e.g., using a table, a visualization, and/or an interactive user interface.

Abstract: According to one embodiment of the present invention, a system determines groups of nodes within a network, each group forming a bi-connected component. The system identifies articulation nodes within the network, where each articulation node resides within each connection between a pair of nodes in the network. The system removes from the determined group each node that includes an articulation node between that node and both the source and target nodes to produce a resulting set of nodes. The system determines connections between the source and target nodes based on the resulting set of nodes. Embodiments of the present invention further include a method and computer program product for determining connections between network nodes in substantially the same manners described above.

Abstract: According to one embodiment of the present invention, a system determines groups of nodes within a network, each group forming a bi-connected component. The system identifies articulation nodes within the network, where each articulation node resides within each connection between a pair of nodes in the network. The system removes from the determined group each node that includes an articulation node between that node and both the source and target nodes to produce a resulting set of nodes. The system determines connections between the source and target nodes based on the resulting set of nodes. Embodiments of the present invention further include a method and computer program product for determining connections between network nodes in substantially the same manners described above.

Abstract: The present invention relates to methods and systems for applying evolutionary algorithms to generate robust search strategies for problems including decision variables. In one aspect, the invention encodes genomes of at least one triplet comprising a variable, assignment priority, and assigned value. The genome may later be decoded to determine a partial or complete assignment of values to variables. If a partial assignment is reached, a search strategy may be applied to generate a complete or more complete assignment. The genomes may also be evolved to produce offspring genomes. One type of evolutionary operator, called the Lamarckian operator is introduced, wherein the similarities, differences, and unbound variables resulting from the decoding of two or more parent genomes are collected. These collections are then used to encode an offspring genome, building upon the strengths of the parents.

Abstract: The present invention relates to methods and systems for applying evolutionary algorithms to generate robust search strategies for problems including decision variables. In one aspect, the invention encodes genomes of at least one triplet comprising a variable, assignment priority, and assigned value. The genome may later be decoded to determine a partial or complete assignment of values to variables. If a partial assignment is reached, a search strategy may be applied to generate a complete or more complete assignment. The genomes may also be evolved to produce offspring genomes. One type of evolutionary operator, called the Lamarckian operator is introduced, wherein the similarities, differences, and unbound variables resulting from the decoding of two or more parent genomes are collected. These collections are then used to encode an offspring genome, building upon the strengths of the parents.