Cisco Technology, Inc.

Naveen Kumar Tyagi

Khanh Vinh Nguyen

Patrick Daniel Wildi

Aniruddh S Dikhit

Ray Mein Lau

Douglas Roger Banks

Abhilash Sreedharan Nair Mannathanil

Techniques for orchestrating a workflow for configuring a computer networking environment or other complex workflows are described. A Directed Acyclic Graph (DAG) that defines a plurality of tasks to be executed to complete the workflow and a plurality of orders between the tasks is received. Embodiments generate a State-Machine Neural Network (SNN) based on the received DAG, by generating a plurality of SNN neurons for the SNN, based on the plurality of tasks within the received DAG and generating a plurality of SNN connections for the SNN, connecting pairs of SNN neurons within the plurality of SNN neurons, based on the plurality of connections within the received DAG. The SNN is executed to orchestrate the workflow by sending and receiving signals to and from the SNN neurons.

Network automation and orchestration using state-machine neural networks

A data management system and associated data management method is disclosed herein. An exemplary method for managing data includes receiving data records timestamped with times spanned by a defined time interval; generating a data cube that includes data planes, wherein each data plane contains a set of data records timestamped with times spanned by the defined time interval; generating an index hypercube for the data cube, wherein dimensions of the index hypercube represent hash values of index keys defined for accessing the data cube; and generating an indexed data cube for storing in a database, wherein the indexed data cube includes the data cube and the index hypercube. The index hypercube includes index hypercube elements, where each index hypercube element represents a unique combination of hashed index key values that map to a data plane in the data cube.

Fast circular database

In one embodiment, a method comprises generating, by a computing device from first and second images of a projected pattern of laser dots detected by respective camera devices in a physical environment, a stereoscopic two-dimensional (2D) object pair based on determining 2D positions for each of the laser dots detected in the first and second images, creating a first mesh of geometric patterns from the 2D positions in the first image and a corresponding second mesh of the geometric patterns from the 2D positions in the second image, and creating the stereoscopic 2D object pair based on matching corresponding geometric patterns from the first and second meshes of the geometric patterns. A three-dimensional model (3D) of the physical environment is generated based on executing stereoscopic triangulation of the stereoscopic 2D object pair. The 3D model causes a controllable device to interact with the physical environment.

Three-dimensional computer vision based on projected pattern of laser dots and geometric pattern matching

A method is provided in one example and includes receiving flow data associated with a traffic flow. The flow data can be tagged with a data source identifier identifying a data source exporting the flow data, a source site identifier identifying a site associated with a source device of the traffic flow, and a destination site identifier identifying a destination site associated with a destination device of the traffic flow. The method further includes determining at least one authoritative data source for each site and metric type using at least one selection rule. The method further includes receiving a query for de-duplicated flow data, and generating de-duplicated flow data based on the data source identifier, source site identifier, and destination site identifier and particular flow data associated with the determined at least one authoritative data source.

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Fremont, CA, United States of America

Naveen Kumar Tyagi