Accenture Global Solutions Limited

Accenture Global Services Gmbh

Srinivas Yelisetty

Teresa Sheausan Tung

Colin Anil Puri

Carl Matthew Dukatz

Jagaran Das

Alex M Kass

Scott Kurth

Sonali Parthasarathy

Narendra Anand

Anuraag Chintalapally

Lascelles Forrester

Michael Giba

Daniel Corin

Gurdeep Singh

Sunil Shettigar

Brett Goldstein

Masoud Loghmani

Maria P Ergueta

Alex Kass

Michael Thomas Giba

A data pipeline architecture is integrated with an analytics processing stack. The data pipeline architecture may receive incoming data streams from multiple diverse endpoint systems. The data pipeline architecture may include converter interface circuitry with multiple dynamic converters configured to convert the diverse incoming data stream into one or more interchange formats for processing by the analytics processing stack. The analytics processing stack may include multiple layers with insight processing layer circuitry above analysis layer circuitry. The analysis layer circuitry may control analytics models and rule application. The insight processing layer circuitry may monitor output from the analysis layer circuitry and generate insight adjustments responsive to rule changes and analytics model parameter changes produced at the analysis layer circuitry.

Data pipeline architecture for analytics processing stack

Scalable architectures provide resiliency and redundancy and are suitable for cloud deployment. The architectures support extreme data throughput requirements. In one implementation, the architectures provide a serving layer and an extremely high speed processing lane. With these and other features, the architectures support complex analytics, visualization, rule engines, and centralized pipeline configuration.

Data pipeline architecture for cloud processing of structured and unstructured data

Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Quantum computing improvements to transportation

Event correlation may include identifying a feature set for each log file of a plurality of log files, and extracting the feature set for each event of a plurality of events in each log file of the plurality of log files. Event correlation may further include determining a plurality of trace event pairs linkage strength values for an event from a first log file of the plurality of log files and a plurality of events from a second log file of the plurality of log files. The trace event pairs linkage strength values may represent an overlap of the feature set for the event from the first log file and the feature set for each of the plurality of events from the second log file.

Event correlation

A method for provisioning a computer includes providing a graph that defines relationships between one or more hardware components of a plurality of computers and component characteristics of the one or more hardware components, and relationships between one or more applications and requirements of the one or more applications. The method further includes receiving a selection of an application and determining, via the graph, whether at least one computer with hardware components capable of meeting the requirements of the application exists. If a computer exits, the method also includes communicating the application to the computer; triggering the computer to execute the application; and communicating, from the computer, data processed by the application to an external system.

Edge computing platform

A social collaboration workflow system embeds social collaboration functionality into the workflow process. The system identifies a location of a user device, and identifying a current activity associated with the user device based on the identified location. The system displays instructions for performing the current activity to a user interface on the user device, and displays an activity feed comprising a set of previously submitted activity feed posts that are related to the current activity to the user interface. The system determines whether the current activity has changed to a new current activity. If the current activity has changed, the system displays instructions for performing the new current activity to the user interface, and displays a new set of previously submitted activity feed posts that are related to the new current activity to the user interface.

Social workflow engine

An action oriented social collaboration system monitors users' interactions with computer software applications. The system provides the interactions to a social networking system. The system publishes the interactions data to a social networking/collaboration interface viewable by members of a group, such as a project team. The system not only publishes data corresponding to a user's interactions with the computer software application, but also publishes the user's interactions with the computer software application to interfaces corresponding to other project team members as well.

Action oriented social collaboration system

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Fremont, CA, United States of America

Srinivas Yelisetty