Abstract: State of the art approaches of exception management are reactive, manual, and intuition-driven. Command center teams often react to exceptions. They also use BI tools which only provide statistical observation, but fail to mine domain-aware insights and actionable recommendations. The disclosure herein generally relates to analyzing process exceptions, and, more particularly, to a method and system for generating a data model to analyze and predict process exceptions. The system generates a data model by using information on rules, associated properties and exceptions, as training data. The data model is further used to process information on different rules to identify exceptions, and then to generate recommendations in response to identified exceptions.

Abstract: Described herein are a method and a system for extracting issues based on ticket mining. In one implementation, a plurality of tickets comprising descriptions of the issues in computing systems are received. The received descriptions are then cleaned by removing unwanted details. Upon cleaning, the clean descriptions are mapped with descriptions stored in service catalog data to obtain unmapped clean descriptions. In an example, the unmapped clean descriptions include one of user-generated descriptions, system-generated descriptions, and both the user-generated descriptions and the system-generated descriptions. For the user-generated descriptions; the issues are extracted by pre-processing the user-generated descriptions, determining keywords from the processed unmapped clean descriptions, constructing n-grams of keywords from the extracted keywords, and extracting the n-grams of keywords as the issues present in the computing systems.

Abstract: Described herein are a method and a system for extracting issues based on ticket mining. In one implementation, a plurality of tickets comprising descriptions of the issues in computing systems are received. The received descriptions are then cleaned by removing unwanted details. Upon cleaning, the clean descriptions are mapped with descriptions stored in service catalog data to obtain unmapped clean descriptions. In an example, the unmapped clean descriptions include one of user-generated descriptions, system-generated descriptions, and both the user-generated descriptions and the system-generated descriptions. For the user-generated descriptions; the issues are extracted by pre-processing the user-generated descriptions, determining keywords from the processed unmapped clean descriptions, constructing n-grams of keywords from the extracted keywords, and extracting the n-grams of keywords as the issues present in the computing systems.

Abstract: Systems and Methods for detection and localization of performance faults in data centers are described. In one embodiment, a method comprises identifying a performance fault in a data center upon detection of the performance fault at any of a plurality of monitors in the data center, wherein the plurality of monitors are placed at monitor nodes, amongst a plurality of nodes, in the data center. Further, the method comprises evaluating a fault vector for the data center upon identification of the performance fault, wherein the fault vector is evaluated based on a fault indicator corresponding to each of the plurality of monitors. Based on the comparison of the fault vector with signatures of each of the plurality of nodes, one or more faulty nodes, amongst the plurality of nodes in the data center, are determined as likely root cause of the performance fault.

Abstract: Systems and Methods for detection and localization of performance faults in data centers are described. In one embodiment, a method comprises identifying a performance fault in a data center upon detection of the performance fault at any of a plurality of monitors in the data center, wherein the plurality of monitors are placed at monitor nodes, amongst a plurality of nodes, in the data center. Further, the method comprises evaluating a fault vector for the data center upon identification of the performance fault, wherein the fault vector is evaluated based on a fault indicator corresponding to each of the plurality of monitors. Based on the comparison of the fault vector with signatures of each of the plurality of nodes, one or ore faulty nodes, amongst the plurality of nodes in the data center, are determined as likely root cause of the performance fault.