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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a non-transitory memory storing machine readable instructions for: a text classifier, a datafield classifier, a metadata classifier, and a combination classifier; and a processor communicatively coupled to the memory, the processor executing the machine-readable instructions to: identify, according to a log format rule, a log description of a log entry and a metafield of the log entry, the identifying performed using the text analyzer, determine that the log description comprises a datafield based on a comparison of the log description with datafield indicators of a word model, the determining performed using the text classifier, generate datafield metrics for the datafield, the datafield metrics corresponding to categorical identifiers, the datafield metrics indicating an accuracy of a datafield categorization, the datafield categorization including an association between the datafield and a corresponding categorical identifier, and the generating performed using the datafield classifier, generate metadata metrics for the metafield, the metadata metrics corresponding to the categorical identifiers, the metadata metrics indicating an accuracy of a metafield categorization, the metafield categorization including an association between the metafield and the corresponding categorical identifier, and the generating performed using the metadata classifier, apply weight values to the datafield metrics and the metadata metrics, the applying performed using the combination classifier generate weighted metrics for the log entry, the weighted metrics including an accuracy indication of a log categorization, the log categorization including an association between the log entry and the corresponding categorical identifier, and the generating performed using the combination classifier, and generate a mapping between a categorical identifier of the categorical identifiers and the log entry in response to at least one of the weighted metrics for the log entry exceeding a predefined threshold, the generating performed using the combination classifier. 2. The system of claim 1, wherein the non-transitory memory further stores machine readable instructions for a whole-content classifier, the processor executing machine-readable instructions to: generate whole-content metrics for the log entry, the whole-content metrics corresponding to the categorical identifiers, the categorical identifiers including an accuracy of a whole-content categorization, the whole-content categorization including an association between a whole-content of the log entry and the corresponding categorical identifier, the whole-content of the log entry including the log description and the metafield, the generating performed using the whole-content classifier; and apply weight values to the datafield metrics, the metadata metrics, and the whole-content metrics to generate weighted metrics for the log entry, the applying performed using the combination classifier. 3. The system of claim 1, wherein the non-transitory memory further stores machine readable instructions for a categorical controller, the processor executing the machine-readable instructions to: identify outlier metafields of a plurality of log entries, the metadata metrics of the outlier metafields less than a predefined influence value, the identifying performed using the categorical controller; compare the outlier metafields to identify matching text in the outlier metafields the comparing performed using the categorical controller; and display, on an interface, a suggestion indicator to suggest a new categorical identifier in response to detection of the matching text in the outlier metafields, the suggestion indicator including the matching text of the outlier metafields, the displaying performed using the categorical controller. 4. The system of claim 1, wherein the processor further executes machine-readable instructions to: identify a first group of characters of the log description and ignore a second group of characters of the log description, the identifying performed using the text classifier; and compare the first group of characters of the log description with the datafield indicators in the word model, the comparing performed using the text classifier. 5. The system of claim 1, wherein the non-transitory memory further stores machine readable instructions for a feedback controller, the processor executing machine-readable instructions to: determine that the log description comprises the datafield in response to a priority of the datafield indicators exceeding a predefined threshold, the determining performed using the feedback controller; receive, from a display interface, a validity indication comprising an indication that a text segment includes the datafield, the receiving performed using the feedback controller; and communicate the validity indication to the text classifier, the text classifier responsive to the validity indication to modify the priority of the datafield indicators of the word model in response to the validity indication, the communicating performed using the feedback controller. 6. The system of claim 1, wherein the non-transitory memory further stores machine readable instructions for a feedback controller, the processor executing machine-readable instructions to determine a validity indication of the mapping between the categorical identifier and the datafield and adjust the weight values of the weighting model based on the validity indication, the determining and adjusting performed using the feedback controller. 7. The system of claim 1, wherein the processor further executes machine-readable instructions to: search a mapping history for a previous mapping between a previously classified log entry and the categorical identifier, the mapping history further including a priority indication of the previous mapping, the mapping performed using the metadata classifier; and generate the metadata metrics of the log entry based on the priority indication of the previous mapping, the generating performed using the metadata classifier. 8. A computer-implemented method comprising: parsing, using a processor and according to a log format rule, a log entry to identify a log description of the log entry and a metafield of the log entry, the log format rule indicating a location of the log description and the metafields of the log entry; identifying, by the processor, that the log description includes a datafield based on a comparison of the log description with datafield indicators of a word model; generating, by the processor, datafield metrics for the datafield of the log entry, the datafield metrics including an accuracy identifier of a datafield categorization, the datafield categorization including an association between the datafield and a corresponding categorical identifier of a group of categorical identifiers; generating, by the processor, metadata metrics for the metafield of the log entry, the metadata metrics including an accuracy identifier of a metafield categorization, the metafield categorization including an association between the metafield and the corresponding categorical identifier of the group of categorical identifiers; generating, by the processor, whole-content metrics for a whole-content of the log entry, the whole-content metrics including an accuracy identifier of a whole-content categorization, the whole-content categorization including an association between the whole-content of the log entry and the corresponding categorical identifier of the group of categorical identifiers; weighing, by the processor, the datafield metrics, the metadata metrics, and the whole-content metrics with respective weight values to generate weighted metrics for the log entry; and generating a mapping between a categorical identifier and the log entry in response to at least one of the weighted metrics exceeding a predefined threshold. 9. The computer-implemented method of claim 8, further comprising displaying, on a report interface, a plurality of log entries mapped to corresponding categorical identifiers. 10. The computer-implemented method of claim 8, wherein the categorical identifier is a first categorical identifier, the computer-implemented method further comprising: identifying an outlier metafield of the log entry, the metadata metrics of the outlier metafield being less than a first predefined influence value; generating an updated metadata metric for the outlier metafield, the updated metadata metric including an accuracy of an association between the outlier metafield and a second categorical identifier; and communicating, to a display interface, a link between the outlier metafield and the second categorical identifier in response to the updated metadata metric exceeding a second predefined influence value. 11. The computer-implemented method of claim 8, further comprising: identifying outlier metafields of the log entry, the metadata metrics of the outlier metafields having values less than a predefined influence value; comparing the outlier metafields to determine matching text in the outlier metafields; and displaying, on an interface, a suggestion indicator for a new categorical identifier in response to a number of outlier metafields with matching text exceeding a predefined quantity. 12. The computer-implemented method of claim 11, further comprising: receiving, from an interface, an input indication corresponding to a new categorical identifier; updating the group of categorical identifiers to include the new categorical identifier; and updating metadata metrics for an updated metafield categorization, the updated metafield categorization including an association between the new categorical identifier and the metafield of the log entry. 13. The computer-implemented method of claim 8, wherein the log description comprises text segments, the computer-implemented method further comprising: selectively identifying correspondence between the text segments and the datafield indicators of the word model; and identifying the datafield included in the log description based on the selectively identifying. 14. The computer-implemented method of claim 8, further comprising: determining updated metadata metrics corresponding to the metafield; identifying a categorized log entry that includes the metafield; reweighing, by the processor, the datafield metrics, the updated metadata metrics, and the whole-content metrics with weight values to generate updated weighted metrics for the categorized log entry; and updating a mapping between the categorized log entry and a categorical identifier based on the updated weighted metrics. 15. A non-transitory computer readable storage medium comprising computer executable instructions, the computer executable instructions executable by a processor, the computer executable instructions comprising: instructions executable by the processor to parse, according to a log format rule, a log description of a log entry and a metafield of the log entry, the log format indicating a location of the log description and the metafield of the log entry; instructions executable by the processor to identify that the log description includes a datafield based on a comparison of a text segment of the log description with datafield indicators of a word model; instructions executable by the processor to generate datafield metrics for the datafield of the log entry, the datafield metrics including an accuracy of a datafield categorization, the datafield categorization including an association between the datafield and a corresponding categorical identifier of a group of categorical identifiers; instructions executable by the processor to generate metadata metrics for the metafield of the log entry, the metadata metrics including an accuracy of a metafield categorization, the metafield categorization including an association between the metafield and the corresponding categorical identifier of the group of categorical identifiers; instructions executable by the processor to combine the datafield metrics and the metadata metrics to generate weighted metrics by application of a first weight to the datafield metrics and a second weight to the metadata metrics; and instructions executable by the processor to generate a mapping between a categorical identifier and the log entry in response a weighted metric of the weighted metrics exceeding a predefined threshold. 16. The non-transitory computer readable storage medium of claim 15, further comprising: instructions executable by the processor to generate whole-content metrics for a whole-content of the log entry, the whole-content metrics including an accuracy of a whole-content categorization, the whole-content categorization including an association between the whole-content of the log entry and the corresponding categorical identifier of the group of categorical identifiers, and instructions executable by the processor to apply a third weight to the whole-content metrics to form the weighted metrics; wherein the whole-content of the log entry includes the datafield and the metafield. 17. The non-transitory computer readable storage medium of claim 15, further comprising: instructions executable by the processor to identify outlier metafields of the log entry, the metadata metrics of the outlier metafields less than a predefined threshold; instructions executable by the processor to compare the outlier metafields and determine corresponding text in the outlier metafields; and instructions executable by the processor to display the corresponding text and a quantity of matching metafields. 18. The non-transitory computer readable storage medium of claim 15, further comprising: instructions executable by the processor to compare the text segment with non-datafield indicators of the word model, the non-datafield indicators comprising groups of characters, the groups of characters indicating non-datafield text; and instructions executable by the processor to determine that the text segment is not a datafield based on the non-datafield indicators. 19. The non-transitory computer readable storage medium of claim 15, further comprising: instructions executable by the processor to compare groups of characters of the datafield with groups of characters previously determined to be the datafield based on the word model. 20. The non-transitory computer readable storage medium of claim 15, further comprising instructions executable by the processor to display, on a report interface, metafields of a log entry with metafield metrics greater than a predefined threshold.