Abstract: Customer data volume of a database and estimated transaction throughput requirements for a multi-tiered transaction system are received at a server sizing computing device. The customer data volume is compared to the estimated transaction throughput requirements. Based upon the comparison of the customer data volume to the estimated transaction throughput requirements, a customer data volume edge case for which server hardware is improperly sized by transaction-based hardware sizing is detected. Using the customer data volume, a quantity of operational memory of a server sufficient to store the customer data volume generated under the detected customer data volume edge case during real-time transactional processing by the server is calculated.

Abstract: A rideshare system including: a memory including executable instructions; a controller to execute the executable instructions; one or more mobile computing devices configured to transmit pickup location data and drop-off location data to the controller; a rideshare vehicle configured to transmit vehicle location data to the controller; and where the executable instructions enable the controller to: receive the pickup location data from the one or more mobile computing devices; receive the drop-off location data from the one or more mobile computing devices; receive vehicle location data from the rideshare vehicle; based on the pickup location data, drop-off location data, and vehicle location data, calculate rideshare service priority variable data for each of the one or more mobile computing devices; transmit, to the rideshare vehicle, the rideshare service priority variable data, pickup location data, and drop-off location data for at least one of the one or more mobile computing devices.

Abstract: This disclosure relates generally to performance appraisal management, and more particularly to standardization of goals associated with performance appraisal. In one embodiment, a method for standardization of goals includes identifying labeled and unlabeled goals associated with a role. The goals includes template and manually created goals. Each of the template goals is associated with a class label, and includes corresponding goal description and self-comments. First and second classifiers are trained using goal description and self-comments. Candidate negative goals are identified and excluded from the goals to obtain a set of unlabeled goals. The set of unlabeled goals are classified by the first and second classifier, and a confidence score associated with the classification is determined. The unlabeled goals with high confidence score are added to labeled goals to obtain an updated set of labeled goals. The first and second classifiers are iteratively co-trained using the updated set of labeled goals.