International Business Machines Corporation

Mellanox Technologies, Ltd.

Jean-Francois Puget

James Steven Luke

Jing-Yun Shyr

Yea-Jane Chu

Richard John Oswald

Eitan Zahavi

Hanan Shteingart

Gary Mataev

Paul Shaw

Renaud Dumeur

Zachi Binshtock

Yoli Shavit

Methods, systems, and machine-readable mediums to perform a neural network to encode log data. In at least one embodiment, a processor comprising one or more circuits to encode at least one log message, at least in part, by encoding a first type of information in the at least one log message to obtain a first encoding, encoding a second type of information in the at least one log message to obtain a second encoding, and obtaining a resultant encoding at least in part by combing at least the first and second encodings.

Using neural networks to encode log data

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.

System and method for combining what-if and goal seeking analyses for prescriptive time series forecasting

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.

Determining connections between nodes in a network

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.

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.

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Saint-Raphael, France

Jean-Francois Puget