Abstract: A method and system for cloud based service provider performance evaluation are disclosed. The method may include extracting service record data and ticket status change record data from the service ticket data and aggregating the service record data and the ticket status change record data. The method may include calculating ticket level performance metric data based on the aggregated record data and generating ticket level performance scores based on the ticket level performance metric data. The method may further include generating service level performance scores based on the service level performance metric data and generating service feedback performance scores based on the service feedback metric data. The method may further include merging the ticket level performance scores, the service level performance scores, and the service feedback performance scores to generate performance vectors and evaluating overall performances of the service providers based on the set of performance vectors.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a machine-learning model for predicting event tags. The system obtains event data that specifies, for each of a plurality of events, a respective set of text fields characterizing the respective event. The system generates, from the event data, encoded language features for the plurality of events. The system also obtains knowledge data that specifies information of the event data. The system generates, from the event data and the knowledge data, tag data specifying a respective tag for each of the plurality of events. The system generates, from the tag data and the encoded language features, a respective encoded feature vector for each of the plurality of events. The system combines the tag data with the encoded feature vectors to generate a plurality of training examples.

Abstract: A method and system for cloud based service provider performance evaluation are disclosed. The method may include extracting service record data and ticket status change record data from the service ticket data and aggregating the service record data and the ticket status change record data. The method may include calculating ticket level performance metric data based on the aggregated record data and generating ticket level performance scores based on the ticket level performance metric data. The method may further include generating service level performance scores based on the service level performance metric data and generating service feedback performance scores based on the service feedback metric data. The method may further include merging the ticket level performance scores, the service level performance scores, and the service feedback performance scores to generate performance vectors and evaluating overall performances of the service providers based on the set of performance vectors.

Abstract: Embodiments of this disclosure include a method and system for machine learning based evaluation of user experience on information technology (IT) support service. The method may include obtaining a field data of an IT support service ticket and obtaining a multi-score prediction engine. The method may further include predicting metric scores of a plurality of IT support service metrics for the support service ticket based on the field data by executing the multi-score prediction engine. The method may further include obtaining system-defined weights and user-defined weights for the plurality of service metrics and calculating a support service score for the support service ticket based on the metric scores, the system-defined weights, and the user-defined weights. The method may further include evaluating user experience based on the support service score.

Abstract: A client system issues a request for a resource over the Internet from a resource server. In constructing the response, the resource server includes: the data requested by the client, additional instructions for the client system to perform upon arrival of the response, and a natural language identifier which describes the resource requested by the client called the taxonomy string. Upon arrival of the response, the additional instructions inserted by the server system cause the client system to send a subsequent request over the Internet to an analytics system. The analytics request may contain a natural language description of the requested resource and a unique identifier to uniquely identify the client system. The analytics system performs analysis on the natural language identifier and stores it in a taxonomy database. The analytics system also performs calculations using the data provided in the analytics request to determine resource utilization patterns.

Abstract: A client system issues a request for a resource over the Internet from a resource server. In constructing the response, the resource server includes: the data requested by the client, additional instructions for the client system to perform upon arrival of the response, and a natural language identifier which describes the resource requested by the client called the taxonomy string. Upon arrival of the response, the additional instructions inserted by the server system cause the client system to send a subsequent request over the Internet to an analytics system. The analytics request may contain a natural language description of the requested resource and a unique identifier to uniquely identify the client system. The analytics system performs analysis on the natural language identifier and stores it in a taxonomy database. The analytics system also performs calculations using the data provided in the analytics request to determine resource utilization patterns.