Accenture Global Solutions Limited

Jaeyoung Christopher Kang

Teresa Sheausan Tung

Desmond R Duggan

Qian Zhu

Wenjia Sun

Tiffany Cecilia Dharma

William Richard Gatehouse

Jan Andre Nicholls

Jürgen Albert Weichenberger

Anuraag Chintalapally

Milad Bourhani

Fabio Bucci

Louis Gerald Farfan

Stephen Zoegall

Scott C Kelly

Jennifer Mohrig

A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a petrochemical refinery to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.

Feedback loop driven end-to-end state control of complex data-analytic systems

A distributed computing design system facilitates the creation and deployment of complex data and mathematical models. In one implementation, the system generates a graphical user interface of a visual distributed computing design workspace. The visual distributed computing design workspace includes a node palette comprising individually selectable nodes, each with a graphical representation and corresponding to a distributed computing function available on a pre-determined target distributed computing cluster, a linking tool for establishing connection links between the individually selectable nodes, and a digital canvas. The system, with modeling circuitry, responds to interactions with the graphical user interface to facilitate visually building a data model by accepting node selections of specific nodes from the node palette, placing the specific nodes on the digital canvas, accepting linking selections of dataflow connections between the specific nodes, and linking the specific nodes as specified by the linking selections.

System architecture with visual modeling tool for designing and deploying complex models to distributed computing clusters

A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a monitored system to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.

Increases in global population have led to unprecedented challenges in meeting every day needs. Vehicle parking is one such challenge given the limited parking spaces available at any destination, whether at work, at a shopping center, or any other location. A parking resource analysis system includes a stepwise simulator system and a role based access system. The stepwise simulator system tests, simulates, and tunes specific models for issuing parking control actions with respect to a specific lot. The role based access system may be physically located at the parking lot. The role based access system implements dynamic loading or dynamic access of role specific models for issuing parking control actions tailored to the role of the particular individual searching for a parking space.

Datamodel tuning for scarce resource evaluation

A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The machine facilitates building, deploying, and/or training analytical models by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The machine can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The machine also provides a dynamically reconfigurable user interface for controlling the system.

Machine for development and deployment of analytical models

A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The system facilitates building, deploying, and/or training analytical models, by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The system can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The system also provides a dynamically reconfigurable user interface for controlling the system.

A machine provides a system and interface to deploy and manage pre-defined analytical models across various compute engines or run time environments, e.g., by exposing analytical model deployment and management parameters to a user while abstracting model deployment activities. The machine also determines proper run time environments for the pre-defined analytical model and verifies the pre-defined analytical model. The machine also provides a dynamically reconfigurable user interface for controlling the machine.

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Berkeley, CA, United States of America

Jaeyoung Christopher Kang