Enhancing extraction rules based on user feedback

1. A computer-implemented method comprising:

providing for display, via a graphical user interface, a plurality of extraction rules relevant to an event set and corresponding match rates, the plurality of extraction rules identified as relevant to the event set by determining a match rate for each extraction rule indicating a number of events of in the event set that match the extraction rule as compared to a total number of events in the event set, wherein each event in the event set is associated with a timestamp and includes a portion of raw machine data that reflects activity in an information technology environment and that is produced by a component of that information technology environment, and wherein each extraction rule, of the plurality of extraction rules, indicates how to extract a subportion of text from the portion of raw machine data in the event to produce a value for a field specified by the extraction rule;

receiving, via the graphical user interface, a selection of an extraction rule of the plurality of extraction rules;

providing for display, via the graphical user interface, a set of events matching the selected extraction rule;

receiving, via the graphical user interface, a modification applied to the displayed extraction rule and a modification applied to a field name associated with the set of events matching the selected extraction rule; and

providing the modifications, provided via the graphical user interface, into a machine learning model to enhance extraction rules available for performing subsequent data extraction, wherein the machine learning model uses the modifications to enhance the extraction rules by reordering extraction rules.

2. The computer-implemented method of claim 1 further comprising:

obtaining the event set; and

anonymizing at least a portion of data in the event set.

3. The computer-implemented method of claim 1 further comprising anonymizing at least a portion of data in the event set by replacing the at least the portion of the data with one or more default values.

4. The computer-implemented method of claim 1 , wherein the plurality of extraction rules identified as relevant is based on the corresponding match rates exceeding a threshold.

5. The computer-implemented method of claim 1 further comprising determining the plurality of extraction rules relevant to the event set by comparing extraction rules to the event set.

6. The computer-implemented method of claim 1 wherein the plurality of extraction rules identified as relevant correspond with high match rates.

7. The computer-implemented method of claim 1 , wherein the set of events matching the selected extraction rule are presented with values extracted via the selected extraction rule being visually emphasized within the set of events.

8. The computer-implemented method of claim 1 , further comprising receiving a modification of a field value.

9. The computer-implemented method of claim 1 , wherein the machine learning model further uses the modifications to enhance the extractions rules by replacing extraction rules or refining extraction rules.

10. The computer-implemented method of claim 1 further comprising using at least a portion of the enhanced extraction rules to perform data extraction.

11. The computer-implemented method of claim 1 further comprising:

identifying context associated with the modification applied to the displayed extraction rule; and

providing the context into the machine learning model to generate the enhanced extraction rules.

12. One or more computer storage media having instructions stored thereon, wherein the instructions, when executed by a computing device, cause the computing device to:

provide for display, via a graphical user interface, a plurality of extraction rules relevant to an event set and corresponding match rates, the plurality of extraction rules identified as relevant to the event set by determining a match rate for each extraction rule indicating a number of events of in the event set that match the extraction rule as compared to a total number of events in the event set, wherein each event in the event set is associated with a timestamp and includes a portion of raw machine data that reflects activity in an information technology environment and that is produced by a component of that information technology environment, and wherein each extraction rule, of the plurality of extraction rules, indicates how to extract a subportion of text from the portion of raw machine data in the event to produce a value for a field specified by the extraction rule;

receive, via the graphical user interface, a selection of an extraction rule of the plurality of extraction rules;

provide for display, via the graphical user interface, a set of events matching the selected extraction rule;

receive, via the graphical user interface, a modification applied to the displayed extraction rule and a modification applied to a field name associated with the set of events matching the selected extraction rule; and

provide the modifications, provided via the graphical user interface, into a machine learning model to enhance extraction rules available for performing subsequent data extraction, wherein the machine learning model uses the modifications to enhance the extraction rules by reordering extraction rules.

13. The computer storage media of claim 12 further causing the computing device to:

obtain the event set; and

anonymize at least a portion of data in the event set.

14. The computer storage media of claim 12 further causing the computing device to anonymize at least a portion of data in the event set by replacing the at least the portion of the data with one or more default values.

15. The computer storage media of claim 12 further causing the computing device to determine the plurality of extraction rules relevant to the event set by comparing extraction rules to the event set.

16. The computer storage media of claim 12 , wherein the plurality of extraction rules identified as relevant correspond with high match rates.

17. The computer storage media of claim 12 , wherein the set of events matching the selected extraction rule are presented with values extracted via the selected extraction rule being visually emphasized within the set of events.

18. A computing system comprising:

one or more processors; and

a memory coupled with the one or more processors, the memory having instructions stored thereon, wherein the instructions, when executed by the one or more processors, cause the computing system to:

provide for display, via a graphical user interface, a plurality of extraction rules relevant to an event set and corresponding match rates, the plurality of extraction rules identified as relevant to the event set by determining a match rate for each extraction rule indicating a number of events of in the event set that match the extraction rule as compared to a total number of events in the event set, wherein each event in the event set is associated with a timestamp and includes a portion of raw machine data that reflects activity in an information technology environment and that is produced by a component of that information technology environment, and wherein each extraction rule, of the plurality of extraction rules, indicates how to extract a subportion of text from the portion of raw machine data in the event to produce a value for a field specified by the extraction rule;

receive, via the graphical user interface, a selection of an extraction rule of the plurality of extraction rules;

provide for display, via the graphical user interface, a set of events matching the selected extraction rule;

receive, via the graphical user interface, a modification applied to the displayed extraction rule and a modification applied to a field name associated with the set of events matching the selected extraction rule; and

provide the modifications, provided via the graphical user interface, into a machine learning model to enhance extraction rules available for performing subsequent data extraction, wherein the machine learning model uses the modifications to enhance the extraction rules by reordering extraction rules.

19. The system of claim 18 , further comprising receiving a modification of a field value.

20. The system of claim 18 , wherein the machine learning model further uses the modifications to enhance the extractions rules by replacing extraction rules or refining extraction rules.

21. The system of claim 18 further comprising using at least a portion of the enhanced extraction rules to perform data extraction.

22. The system of claim 18 further comprising:

identifying context associated with the modification to the displayed extraction rule; and

providing the context into the machine learning model to generate the enhanced extraction rules.