Cisco Technology, Inc.

Yannick Weibel

Jean-Philippe Vasseur

Grégory Mermoud

Sofia Karygianni

Mohit Dubey

Timothy Simon Stirling

A device may obtain a query from a user interface regarding a computer network. The device may retrieve, based on the query, schema metadata from a database. The device may resolve any time ambiguity in the query using the schema metadata. The device may provide an answer to the query to the user interface, after resolving any time ambiguity in the query using the schema metadata.

Time disambiguation for natural language queries in network monitoring systems

In one embodiment, a device in a network receives data indicative of a target state for one or more distributed learning agents in the network. The device determines a difference between the target state and state information maintained by the device regarding the one or more distributed learning agents. The device calculates a synchronization penalty score for each of the one or more distributed learning agents. The device selects a particular one of the one or more distributed learning agents with which to synchronize, based on the synchronization penalty score for the selected distributed learning agent and on the determined difference between the target state and the state information regarding the selected distributed learning agent. The device initiates synchronization of the state information maintained by the device regarding the selected distributed learning agent with state information from the selected distributed learning agent.

Constraint-aware resource synchronization across hyper-distributed learning systems

A device in a network receives fingerprints of two or more network anomalies detected in the network by different anomaly detectors. Each fingerprint comprises a hash of tags that describe a detected anomaly. The device associates the fingerprints with network records captured within a timeframe in which the two or more network anomalies were detected. The device compares the fingerprints associated with the network records to determine that the two or more detected anomalies are part of a singular anomaly event. The device generates a notification regarding the singular anomaly event. The notification includes those of the fingerprints that are associated with the singular anomaly event.

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Lausanne, Switzerland

Yannick Weibel