Cisco Technology, Inc.

Aungon Nag Radon

Dayanand Pochugari

Dipock Das

Adam Jamison Oliner

Ram Sriharsha

Nikesh Padakanti

Karthikeyan Ramasamy

Matteo Merli

Roussi Roussev

In various embodiments, a natural language (NL) application receives a first incomplete natural language (NL) request, and generates one or more request completion recommendations based on at least the first incomplete NL request and a first recommendation model, where the first recommendation model is generated via a machine learning algorithm applied to a first data dependency model and a first request completion model. The NL application receives a selection of a first request completion recommendation included in the one or more request completion recommendations, generates a complete request based on the first incomplete NL request and the first request completion recommendation, and causes the complete request to be applied to the data storage system.

Providing completion recommendation variations for a partial natural language request

In various embodiments, a natural language (NL) application enables users to more effectively access various data storage systems based on NL requests. The NL application includes functionality for selecting an optimal interpretation algorithm, generating a dashboard, and/or generating an alert based on an NL request. Advantageously, the operations performed by the NL application reduce the amount of time and user effort associated with accessing data storage systems and increase the likelihood of properly addressing NL requests.

Creating dashboards for viewing data in a data storage system based on natural language requests

Various implementations of the present application set forth a computer-implemented method comprising obtaining, by a low-power hub device, a first set of data published by an edge device, where the low-power hub device subscribes to at least a subset of data published by the edge device, generating, by the low-power hub device, a second set of data from the first set of data by inputting the first set of data into a machine learning (ML) model executing on the low-power hub device, and transmitting the second set of data to a remote server computer system.

Real-time machine learning at an edge of a distributed network

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Toronto, Canada

Aungon Nag Radon