International Business Machines Corporation

Repsol, S.a.

Kamal Das

Navin Twarakavi

Fred Ochieng Otieno

Jagabondhu Hazra

Shantanu Ravindra Godbole

Sachin Gupta

Ulisses Mello

Ankur Narang

Eduardo Rocha Rodrigues

Ranjini Bangalore Guruprasad

Jitendra Singh

Komminist Weldemariam

Carmen Nilda Mena Paz

Aanchal Goyal

Andrew Kinai

Jose Maria Segura Serra

Pablo Enrique Vargas Mendoza

Paula Aida Sesini

Nubia Aurora Gonzalez Molano

Roberto Federico Ausas

Freddy Ernesto Mackay Espindola

Lashmikantha Mookanahallipatna Ramasesha

Methods, systems, and computer program products for stochastic compression of raster data are provided herein. A computer-implemented method includes obtaining at least one compression ratio and at least one error value for a given set of raster data; compressing at least a portion of the given set of raster data based at least in part on the at least one compression ratio and the at least one error value; transmitting the compressed raster data, to at least one given destination, based at least in part on a given transmission speed variable; and performing one or more automated actions based at least in part on the transmitted compressed raster data.

Stochastic compression of raster data

A system, computer program product, and method are presented for forecasting a spatio-temporal calendar including predicted regions of interest based on time dependent factors such as long-term weather predictions, time-independent factors, and travel constraints. The method includes collecting information and constraints with respect to service visits. At least a portion of the collected information and constraints are directed toward weather and climate. The method also includes predicting weather and climate impacts on at least one geographical region of interest. The method further includes predicting, subject to the predictions of weather and climate impacts, one or more locations of interest within the at least one geographical region of interest that would be impacted by one or more service visits. The method also includes generating one or more spatio-temporal calendars that include the one or more locations of interest scheduled for the one or more service visits.

Spatio-temporal calendar generation

Methods, systems, and computer program products for dynamic determination of irrigation-related data using machine learning techniques are provided herein. A computer-implemented method includes obtaining irrigation-related data pertaining to a region of interest; determining temporal values corresponding to irrigation activity at the region of interest by performing spatiotemporal analysis of the irrigation-related data; determining amounts of water utilized in connection with the irrigation activity corresponding to the temporal values by applying machine learning techniques to the irrigation-related data; determining types of irrigation activity attributed to the irrigation activity by applying machine learning techniques to the irrigation-related data and determined amounts of water; determining irrigation-related variables pertaining to the region of interest by executing a physical model using, as inputs, the determined temporal values, amounts of water, and types of irrigation activity, wherein the irrigation-related variables include an extent of irrigation activity; and outputting the determined irrigation-related variables to a user.

Dynamic determination of irrigation-related data using machine learning techniques

A system, method and program product for managing hydrocarbon energy production. A hydrocarbon field modeler models physical characteristics of a hydrocarbon energy field. A load predictor predicts processing workload in modeling the hydrocarbon energy field, and identifying a balanced modeling unit distribution across multiple processors simulating field production. A load distribution unit distributes the modeling units across the processors for a balanced modeling unit distribution. The load predictor and load distribution unit proactively shifts loads to maintain workload balanced throughout the simulation.

Managing hydrocarbon energy production while proactively maintaining a balanced workload

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New Delhi, India

Kamal Das