Abstract: A computer-implemented method for vehicle event management. The method receives one or more sets of vehicle sensor data from a first vehicle, wherein the one or more sets of vehicle sensor data correspond to one or more sensor systems of the first vehicle. The method further maps the received one or more sets of vehicle sensor data with one or more candidate vehicle events, based on one or more sets of archived vehicle sensor data. The method further identifies one or more relationships between the received one or more sets of vehicle sensor data and the one or more candidate vehicle events, and provides an alert, to the first vehicle, with the identified one or more relationships between the received one or more sets of vehicle sensor data and the one or more candidate vehicle events.

Abstract: An approach is provided in which a system analyzes a first subject in a first image taken at a first point in time against a second subject in a second image taken at a second point in time. The first image and the second image are captured at a venue. The system determines, based on a time duration between the first point in time and the second point, a probability that the first subject is at the venue at the second point in time. Next, the system computes, based on the probability, a relevance score that quantifies a relationship between the first subject in the first image and the second subject in the second image. Finally, the system assigns the second image to a first image file corresponding to the first image based on the relevance score reaching a relevance score threshold.

Abstract: A computer-implemented method for vehicle event management. The method receives one or more sets of vehicle sensor data from a first vehicle, wherein the one or more sets of vehicle sensor data correspond to one or more sensor systems of the first vehicle. The method further maps the received one or more sets of vehicle sensor data with one or more candidate vehicle events, based on one or more sets of archived vehicle sensor data. The method further identifies one or more relationships between the received one or more sets of vehicle sensor data and the one or more candidate vehicle events, and provides an alert, to the first vehicle, with the identified one or more relationships between the received one or more sets of vehicle sensor data and the one or more candidate vehicle events.

Abstract: Disclosed aspects relate to dynamic irregularity management in a multiple-individual evaluation-activity environment. A set of activity data pertaining to the multiple-individual evaluation-activity environment may be collected. The set of activity data may include a first set of individual information for a first individual related to an evaluation-activity and a second set of individual information for a second individual related to the evaluation-activity. An irregular relationship may be identified with respect to the set of activity data. An irregularity event response action may be provided based on the irregular relationship.

Abstract: Disclosed aspects relate to dynamic irregularity management in a multiple-individual evaluation-activity environment. A set of activity data pertaining to the multiple-individual evaluation-activity environment may be collected. The set of activity data may include a first set of individual information for a first individual related to an evaluation-activity and a second set of individual information for a second individual related to the evaluation-activity. An irregular relationship may be identified with respect to the set of activity data. An irregularity event response action may be provided based on the irregular relationship.

Abstract: The embodiments relate to classifying and grouping images. Primary images and secondary images are created and stored. A preliminary relationship between the primary and secondary images is established. The primary image is compared with the secondary image to produce a relevance score. The relevance score is converted to a secondary image assignment, which includes selectively assigning the secondary image to a first group based on the relevance score.

Abstract: An approach is provided in which a system analyzes a first subject in a first image taken at a first point in time against a second subject in a second image taken at a second point in time. The first image and the second image are captured at a venue. The system determines, based on a time duration between the first point in time and the second point, a probability that the first subject is at the venue at the second point in time. Next, the system computes, based on the probability, a relevance score that quantifies a relationship between the first subject in the first image and the second subject in the second image. Finally, the system assigns the second image to a first image file corresponding to the first image based on the relevance score reaching a relevance score threshold.

Abstract: The embodiments relate to classifying and grouping images. Primary images and secondary images are created and stored. A preliminary relationship between the primary and secondary images is established. The primary image is compared with the secondary image to produce a relevance score. The relevance score is converted to a secondary image assignment, which includes selectively assigning the secondary image to a first group based on the relevance score.