Abstract: An event-notification system provides for monitoring resource-status parameters so as to repeatedly generate resource-status values for each of N resource-status parameters applicable to the resources. Each datapoint specifies a resource, a time of collection, and the values of parameters for the resource at the time. The datapoints are represented in an N-dimensional space, including N dimensions corresponding to the resource-status parameters and one dimension devoted to time. The datapoints are clustered on a proximity basis. The clusters are manually classified as Normal or Ack-Abnormal. A machine-learning engine develops a model that allows the classifications to be automated. Alerts are sent for respective Abnormal clusters rather than for each abnormal datapoint, thus reducing the amount of data an administrator must deal with to address potential problems.

Abstract: An event-notification system provides for monitoring resource-status parameters so as to repeatedly generate resource-status values for each of N resource-status parameters applicable to the resources. Each datapoint specifies a resource, a time of collection, and the values of parameters for the resource at the time. The datapoints are represented in an N-dimensional space, including N dimensions corresponding to the resource-status parameters and one dimension devoted to time. The datapoints are clustered on a proximity basis. The clusters are manually classified as Normal or Ack-Abnormal. A machine-learning engine develops a model that allows the classifications to be automated. Alerts are sent for respective Abnormal clusters rather than for each abnormal datapoint, thus reducing the amount of data an administrator must deal with to address potential problems.