Abstract: An incident management system for enterprise operations is disclosed. The system 100 includes an operational details collection module 110, a data processing module 120, an operational details analysis module 130, an anomaly detection module 140 and an incident recognition module 150 including an incident cause analysis sub-module 155 and an incident cause description sub-module 160. The system 100 collects enterprise operational details from an operational database, analyzes huge volumes of logs, KPIs, traces, and IT asset relationships using proprietary machine learning techniques to identify one or more abnormal patterns, one or more hidden issues, one or more cross-domain performance issues, and one or more unusual system behaviors. Also, the system correlates, in real-time, with a huge volume of logs, KPIs, and IT system topologies to understand the relationship between different symptoms and problems at the machine's speed to arrive at a root cause and impacts.

Abstract: Embodiments of the present invention provide a method of providing network information, comprising determining one or more pseudowires between nodes in a network; grouping the pseudowires, according to information associated with each pseudowire and one or more predetermined criteria, into one or more groups each representing a virtual private network (VPN); providing a graphical user interface (GUI) displaying information associated with at least one pseudowire participating in the virtual private network.

Abstract: Embodiments of the present invention provide a method of providing network information, comprising determining one or more pseudowires between nodes in a network; grouping the pseudowires, according to information associated with each pseudowire and one or more predetermined criteria, into one or more groups each representing a virtual private network (VPN); providing a graphical user interface (GUI) displaying information associated with at least one pseudowire participating in the virtual private network.