International Business Machines Corporation

Mark Purcell

Francesco Fusco

Seshu Tirupathi

Robert Gormally

Emanuele Ragnoli

Fearghal O'Donncha

Killian Levacher

Ambrish Rawat

Bradley Eck

Marco Simioni

Michael Barry

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to facilitating a process to compensate a service being provided over a network connection. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a determination component that determines a network connection between a server and a client node, and a predictive component that predicts, employing machine learning, a graphical representation update to a service provided by the server over the network connection. The predictive component can generate the prediction in response to a decrease in bandwidth and/or an increase in latency of a network connection. A training component can train a machine learning model employed by the predictive component based on historical data of the service provided by the server.

AI-based compensation of resource constrained communication

Various embodiments are provided for intelligent management of data flows in a computing environment by a processor. One or more data transformation in time-series data applications templates may be created and managed according to concepts, one or more instances of the concepts, relationships between the concepts, and a mapping of the concepts to one or more data sources.

Automated management of data transformation flows based on semantics

A computer-implemented method, a computer program product, and a computer system for selecting predictions by models. A computer receives a request for a forecast of a dependent variable in a time domain, where the time domain includes first time periods that have normal labels due to normal predictor variable data and second time periods that have anomalous labels due to anomalous predictor variable data. The computer retrieves accuracy scores and robustness scores of models, where the accuracy scores indicate forecasting accuracy in the first time periods and the robustness scores indicate forecasting accuracy in the second time periods. For predictions in the first time period, the computer selects dependent variable values predicted by a first model that has highest values of the accuracy scores. For predictions in the second time periods, the computer selects dependent variable values predicted by a second model that has highest values of the robustness scores.

Selecting forecasting models by machine learning based on analysis of model robustness

Embodiments for managing aquatic infrastructure by one or more processors are described. A vehicle is detected within a vicinity of an aquatic infrastructure device. A wireless capability level of the vehicle is determined. Based on the determined wireless capability level of the vehicle, at least one of a wireless power transfer between the vehicle and the aquatic infrastructure device or a wireless data transfer between the vehicle and the aquatic infrastructure device is enabled.

Methods and systems for aquatic infrastructure recharge and data transfer

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Naas, Ireland

Mark Purcell