Abstract: A system described herein may use automated techniques, such as machine learning techniques, to identify network events (e.g., based on user-submitted reports and/or system-generated alerts). The system may identify a type and/or attributes of the network event, and may identify past network events that share the same or similar attributes. An estimated time to remediate the network event, and/or one or more remedial measures, may be determined and implemented based on remediation measures taken to remediate the past network events. The determined remedial measures may include one or more temporary remedial measures effected until a permanent solution is put into place. Users who are affected, or who are likely to be affected, may be contacted to indicate the estimated remediation time. Feedback may be used to refine the estimation and/or remediation for future similar network events.

Abstract: A system described herein may use automated techniques, such as machine learning techniques, to identify network events (e.g., based on user-submitted reports and/or system-generated alerts). The system may identify a type and/or attributes of the network event, and may identify past network events that share the same or similar attributes. An estimated time to remediate the network event, and/or one or more remedial measures, may be determined and implemented based on remediation measures taken to remediate the past network events. The determined remedial measures may include one or more temporary remedial measures effected until a permanent solution is put into place. Users who are affected, or who are likely to be affected, may be contacted to indicate the estimated remediation time. Feedback may be used to refine the estimation and/or remediation for future similar network events.