C3.ai, Inc.

Umashankar Sandilya

Henrik Ohlsson

Mehdi Maasoumy Haghighi

Jeremy Zico Kolter

Giuseppe Barbaro

Provided herein are methods and systems for determining a historical state of a dynamic network. The methods may comprise continuously obtaining data associated with a system from a plurality of different data sources; constructing a full history dynamic network (FHDN) of the system using the data; and providing a state of the system for a historical time instance in response to a query of the FHDN for the historical time instance.

Systems and methods for full history dynamic network analysis

The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

Growing community of inventors

Palo Alto, CA, United States of America

Umashankar Sandilya