Abstract: A computer processing device receives computer readable data to derive computer executable rules for mining and constructing situation categories. The received data is transformed into a predetermined standard format if the received data is not already in the predetermined standard format. The predetermined standard formatted data is parsed, and an outer, iterative loop is performed until at least one predetermined stopping criterion is met. An inner iterative loop is performed within the outer iterative loop until all desired subsets of data are processed. During the inner iterative loop, selected subsets of data are labeled with labels associated with corresponding previously labeled subsets of data. New computer executable rules are generated for mining and constructing situation categories from the labeled subsets of data. Keyword list classifiers are transformed using the stored labeled subsets of data.

Abstract: An off-line knowledge acquisition process takes IT resource messages and automatically generates a set of rules used to provide situation categories for the resource messages/events. The off-line knowledge acquisition process generates an event-to-situation mapping file for efficiently mapping situation to events in runtime. Rules are fed back into a knowledge repository and process for reuse. The off-line knowledge acquisition process provides methods to reiterate the process of mining (autonomically and/or by human interaction) to improve the rules and confidence level assigning the situation categories. A runtime categorizer component uses the event-to-situation mapping file to add situation categories to the IT resource events. The runtime categorizer uses a plurality of annotator components each capable of adding a situation annotation to incoming messages based on rules generated by the off-line knowledge acquisition process.

Abstract: A computer processing device receives computer readable data to derive computer executable rules for mining and constructing situation categories. The received data is transformed into a predetermined standard format if the received data is not already in the predetermined standard format. The predetermined standard formatted data is parsed, and an outer, iterative loop is performed until at least one predetermined stopping criterion is met. An inner iterative loop is performed within the outer iterative loop until all desired subsets of data are processed. During the inner iterative loop, selected subsets of data are labeled with labels associated with corresponding previously labeled subsets of data. New computer executable rules are generated for mining and constructing situation categories from the labeled subsets of data. Keyword list classifiers are transformed using the stored labeled subsets of data.

Abstract: An off-line knowledge acquisition process takes IT resource messages and automatically generates a set of rules used to provide situation categories for the resource messages/events. The off-line knowledge acquisition process generates an event-to-situation mapping file for efficiently mapping situation to events in runtime. Rules are fed back into a knowledge repository and process for reuse. The off-line knowledge acquisition process provides methods to reiterate the process of mining (autonomically and/or by human interaction) to improve the rules and confidence level assigning the situation categories. A runtime categorizer component uses the event-to-situation mapping file to add situation categories to the IT resource events. The runtime categorizer uses a plurality of annotator components each capable of adding a situation annotation to incoming messages based on rules generated by the off-line knowledge acquisition process.

Abstract: Classifying a message includes receiving a message to be classified, wherein the message includes a message identifier. If the message identifier uniquely maps to a corresponding classification category, the message is labeled with the identified classification category. If the message identifier does not map directly to a corresponding classification category, the message to be classified is parsed and a plurality of features from the parsed message are identified, wherein at least one classification rule is compared to the plurality of features. Each classification rule that matches to the plurality of features is rated and a classification category is identified from the rating, wherein the message is labeled with the identified classification category.