DESC:PROVIDING AUTOMATABLE UNITS FOR INFRASTRUCTURE SUPPORT ,CLAIMS:I/We claim:
1. A computing system (102) for providing automatable units to users in information technology environment, the computing system (102) comprising:
a processor (202);
a filtering module (212) coupled to the processor (202) to filter an operation log having information pertaining to actions performed to resolve a ticket, wherein the operation log is filtered based on filtering attributes for selection of content relevant for identifying an automatable unit;
an analysis module (216) coupled to the processor (202) to generate the automatable unit, based on an analysis of the content using one of a sequence mining technique and a graph mining technique, wherein the automatable unit is one of a standard operator unit and a control flow unit (300), and wherein the standard operator unit comprises a set of instructions that are to be performed to resolve the ticket and the control flow unit (300) comprises a set of possible actions to be performed for each of a plurality states of an infrastructure element to resolve the ticket associated with the infrastructure element; and
an automation module (218) to provide the automatable unit in a support service knowledgebase (112) accessible by the users for dynamically resolving tickets similar to the ticket.
2. The computing system (102) as claimed in claim 1, wherein the computing system (102) further comprises a cleaning module (214) coupled to the processor (202) to remove noise from the content obtained upon filtering, based on cleaning configuration data, and wherein the cleaning configuration data includes information pertaining to operations that are classified as noise, and wherein cleaned content is analyzed for determining automatable units.
3. The computing system (102) as claimed in claim 1, wherein the analysis module (216):
determines a frequency of a pattern in the content, wherein the frequency is determined based on a tolerance threshold and a minimum pattern length, the tolerance threshold defining an extent of inexactness permissible between two patterns and a pattern length being a number of commands in the pattern;
compares the frequency of the pattern with a threshold frequency; and
identifies the pattern as a repeated pattern, based on the comparison.
4. The computing system (102) as claimed in claim 3, wherein the analysis module (216) selects, from an iterative group of repeated patterns, the repeated pattern with a maximum pattern length as a candidate for automation.
5. The computing system (102) as claimed in claim 3, wherein the analysis module (216) maintains a pre-hash and a post-hash table to determine the frequency of the pattern, wherein a hash value of the pre-hash table and the post hash-table is a set of occurrences of the pattern in the content, and a hash key is the pattern in the content.
6. The computing system (102) as claimed in claim 3, wherein the analysis module (216) implements one of a suffix tree data structure and a directed acyclic word graph data structure to determine the frequency of the pattern.
7. The computing system (102) as claimed in claim 1, wherein the analysis module (216):
obtains a directed acyclic graph corresponding to each sequence in a set of homogenous sequences, wherein each vertex of the directed acyclic graph represents a command from a corresponding sequence; and
merges a plurality directed acyclic graphs to generate a largest common graph representing the control flow unit (300), wherein each node of the largest common graph indicates a state of the infrastructure element and the possible actions to resolve the ticket, when the infrastructure element is in the state.
8. The computing system (102) as claimed in claim 7, wherein the analysis module (216) to merge the plurality of directed acyclic graphs:
computes an intersection of input directed acyclic graphs as the largest common graph without loss of generality; and
adds uncommon vertices from each graph to the largest common graph.
9. The computing system (102) as claimed in claim 1, wherein the automation module (218) associates validation rules with a support service corresponding to the standard operator unit, wherein the validation rules provide a check to determine whether a user can perform the support service on the infrastructure element (108).
10. A method for providing of an automatable unit comprising:
filtering an operation log having information pertaining to actions performed to resolve a ticket, wherein the operation log is filtered based on filtering attributes for selection of content relevant for identifying an automatable unit;
generating the automatable unit, based on an analysis of the content using one of a sequence mining technique and a graph mining technique, wherein the automatable unit is one of a standard operator unit and a control flow unit (300), and wherein the standard operator unit comprises a set of instructions that are to be performed to resolve the ticket and the control flow unit (300) comprises a set of possible actions to be performed for each of a plurality states of an infrastructure element to resolve the ticket associated with the infrastructure element; and
providing the automatable unit in a support service knowledgebase (112) accessible by the users for dynamically resolving tickets similar to the ticket.
11. The method as claimed in claim 10, wherein the method further comprises removing noise from the content obtained upon filtering, based on cleaning configuration data, and wherein the cleaning configuration data includes information pertaining to operations that are classified as noise, and wherein cleaned content is analyzed for determining automatable units.
12. The method as claimed in claim 11, wherein the removing further comprising removing at least one of consecutive redundant commands and empty commands.
13. The method as claimed in claim 11, wherein the removing further comprises generalizing commands in the filtered content to provide for identification of multiple instances of same commands during analyzing.
14. The method as claimed in claim 10, wherein the method further comprises:
determining a frequency of a pattern in the content, wherein the frequency is determined based on a tolerance threshold and a minimum pattern length, the tolerance threshold defining an extent of inexactness permissible between two patterns and a pattern length being a number of commands in the pattern;
comparing the frequency of the pattern with a threshold frequency; and
determining the pattern as a repeated pattern, based on the comparison.
15. The method as claimed in claim 14, wherein the method further comprises selecting, from an iterative group of repeated patterns, the repeated pattern with a maximum pattern length as a candidate for automation.
16. The method as claimed in claim 15, wherein the method further comprises:
obtaining a directed acyclic graph corresponding to each sequence in a set of homogenous sequences, wherein each vertex of the directed acyclic graph represents a command from a corresponding sequence; and
merging a plurality directed acyclic graphs to generate a largest common graph representing the control flow unit (300), wherein each node of the largest common graph indicates a state of the infrastructure element and possible actions to resolve the ticket, when the infrastructure element is in the state.
17. The method as claimed in claim 16, wherein the merging comprises:
computing an intersection of input directed acyclic graphs as the largest common graph without loss of generality; and
adding uncommon vertices from each graph to the largest common graph.
18. A non-transitory computer readable medium having a set of computer readable instructions that, when executed, perform a method for providing automatable units comprising:
filtering an operation log having information pertaining to actions performed to resolve a ticket, wherein the operation log is filtered based on filtering attributes for selection of content relevant for identifying an automatable unit;
generating the automatable unit, based on an analysis of the content using one of a sequence mining technique and a graph mining technique, wherein the automatable unit is one of a standard operator unit and a control flow unit (300), and wherein the standard operator unit comprises a set of instructions that are to be performed to resolve the ticket and the control flow unit (300) comprises a set of possible actions to be performed for each of a plurality states of an infrastructure element to resolve the ticket associated with the infrastructure element; and
providing the automatable unit in a support service knowledgebase (112) accessible by the users for dynamically resolving tickets similar to the ticket.