International Business Machines Corporation

Carlos A Alzate Perez

Mathieu Sinn

Michael Wurst

Francesco Fusco

Pascal Pompey

Radu Marinescu

John Erik Wittern

Alice-Maria Marascu

Charles Arthur Jochim

Bei Chen

Yassine Lassoued

Ulrike Fischer

An application automatically composed using natural language processing. A natural language input comprising one or more application requirements is received via an interface. The natural language input is parsed to extract one or more chunks, each chunk representing one of the application requirements, and at least one of the chunks representing at least one of one or more main functionalities described by the application requirements. A coarse architecture logically arranging the main functionalities to satisfy the application requirements is inferred according to the chunks. Existing assets corresponding to the chunks are identified, each asset associated with at least one of the main functionalities. The identified assets are assembled according to the coarse architecture. The assembled assets are deployed as an application.

Automated application composer with natural language processing

Embodiments for recommending predictive modeling methods and features by a processor. One or more extracted methods and features of one or more predictive models are received according to selected criteria from both a structured database and from one or more data sources from a remote database. One or more extracted predictive model methods and features may be recommended according to the selected criteria.

Domain-oriented predictive model feature recommendation system

In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data based, at least in part, on one or more similar consumption patterns of meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Adaptive sampling of smart meter data

In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

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Dublin, Ireland

Carlos A Alzate Perez