Abstract: A self-learning system for analytical attribute and clustering segmentation may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy value of the attribute identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy value of the attribute identifiers representing the metafields. A combination classifier may form a weighted classification set and select an attribute identifier as being representative of the datafield based on the weighted classification set. The combination classifier may further evaluate an attribute importance value of each attribute identifier, and select an attribute identifier having a top attribute importance value.

Abstract: A self-learning system for categorizing log entries may be provided. The system may display a first log entry and receive a categorical identifier for the first log entry. The system may parse the first log entry for predetermined text information and predetermined image information. The predetermined text information may be included in a datafield classifier and the predetermined image information included in a metadata classifier. The system may identify the predetermined text information in the log entry and adjust a first prioritization of respective categorical identifiers included in the datafield classifier. The system may identify the predetermined image information in the first log entry and adjust a second prioritization of the respective categorical identifiers included in the metadata classifier. The system may map a second log entry to the categorical identifier based on adjustment of the first prioritization or adjustment of the second prioritization.

Abstract: A self-learning system for analytical attribute and clustering segmentation may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy value of the attribute identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy value of the attribute identifiers representing the metafields. A combination classifier may form a weighted classification set and select an attribute identifier as being representative of the datafield based on the weighted classification set. The combination classifier may further evaluate an attribute importance value of each attribute identifier, and select an attribute identifier having a top attribute importance value.

Abstract: A self-learning system for categorizing log entries may be provided. The system may display a first log entry and receive a categorical identifier for the first log entry. The system may parse the first log entry for predetermined text information and predetermined image information. The predetermined text information may be included in a datafield classifier and the predetermined image information included in a metadata classifier. The system may identify the predetermined text information in the log entry and adjust a first prioritization of respective categorical identifiers included in the datafield classifier. The system may identify the predetermined image information in the first log entry and adjust a second prioritization of the respective categorical identifiers included in the metadata classifier. The system may map a second log entry to the categorical identifier based on adjustment of the first prioritization or adjustment of the second prioritization.

Abstract: A self-learning system for categorizing log entries may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy of the categorical identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy categorical identifiers representing the metafields. A combination classifier may form a weighted classification set and select a categorical identifier as being representative of the datafield based on the weighted classification set. A categorical controller may identify new categories based on an analysis of the context metrics of the log entry.

Abstract: A self-learning system for categorizing log entries may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy of the categorical identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy categorical identifiers representing the metafields. A combination classifier may form a weighted classification set and select a categorical identifier as being representative of the datafield based on the weighted classification set. A categorical controller may identify new categories based on an analysis of the context metrics of the log entry.