We claim:
1. A method for automated resolution of one or more events
in a computing environment, the method is implemented by a
processor configured to execute program instructions stored in
a memory, the method comprising:
identifying one or more problem types from the events, wherein the identified problem types are associated with one or more configuration items (CIs) at issue;
identifying one or more domains of the CIs at issue, wherein the domains represent one or more types of the CIs at issue;
checking existence of one or more diagnosis and resolution sequences, wherein the checking of the diagnosis and resolution sequences is based on the identified problem types and the domain associated with the CI types;
fetching the diagnosis and resolution sequences, wherein the diagnosis and resolution sequences fetched at a first instance are at least in part updated with one or more new diagnosis and resolution sequences; and
executing the fetched diagnosis and resolution sequences iteratively such that a next step of the executed diagnosis and resolution sequence is based on an output of a first step of the executed diagnosis and resolution sequence.
2. The method as claimed in claim 1, wherein the events
received includes at least one of: an incident, an activity, a
token, a request, an alert, and a ticket.
3. The method as claimed in claim 1, wherein the CIs at
issue includes at least one of: an application software, network
components, a hardware component, an operating system, a
firmware, central processing units, servers, and databases.
4. The method as claimed in claim 1, wherein the
identification of the problem types is carried out using at least

one of natural language understanding (NLU) techniques and entity extraction techniques.
5. The method as claimed in claim 1, wherein the problem types associated with the CI types include at least one of: a computing service not operable, a website not operable, a network issue, a web server is non-functional, an application server is non-functional, a software application is non-functional and a hardware component is non-functional.
6. The method as claimed in claim 1, wherein the CI types include at least one of an application type, a location type or a technology type of a CI at issue.
7. The method as claimed in claim 1, wherein the step of identifying the domains of the CIs at issue includes at least one of: checking CI's application and using it as domain name, checking CI's location and using it as domain name, and checking CI's technology and using it as domain name.
8. The method as claimed in claim 1, wherein the diagnosis
and resolution sequences comprises one or more sequence of steps
for diagnosis and resolution of the events associated with the
CIs of the identified domains.
9. The method as claimed in claim 1, wherein the diagnosis
and resolution sequences are executed in a sequence of steps
until the events associated with the CIs associated with the
domain is completely resolved.
10. The method as claimed in claim 1, wherein the execution of diagnosis and resolution sequences is carried out by providing the first step of the diagnosis and resolution sequences and based on the output of the executed first step the next step of diagnosis and resolution sequence is provided for execution until the events associated with the CIs is completely resolved.
11. The method as claimed in claim 1, wherein the fetching and execution of the diagnosis and resolution sequences is based on one or more defined set of rules, wherein the defined set of

rules relates to the events associated with the CIs related to the domains.
12. The method as claimed in claim 11, wherein the defined set of rules fetches and executes the diagnosis and resolution sequences iteratively such that the next step of the diagnosis and resolution sequence is provided based on the output of the first step of the executed diagnosis and resolution sequence until the events associated with the CIs associated with the domain is completely resolved.
13. The method as claimed in claim 1, wherein the executed diagnosis and resolution sequences are displayed for tracking and validation.
14. The method as claimed in claim 1, wherein the executed diagnosis and resolution sequences is capable of being paused for reviewing.
15. The method as claimed in claim 1, wherein the diagnosis and resolution sequences is fed as training data and wherein the diagnosis and resolution sequences fed as training data is capable of being paused for reviewing and modifications.
16. The method as claimed in claim 1, wherein the executed
diagnosis and resolution sequences are capable of being aborted
if the executed diagnosis and resolution sequences are found to
be incorrect.
17. A system for automated resolution of one or more
events in a computing environment, the system comprising:
a memory storing program instructions;
a processor configured to execute instructions stored in the memory; and
an intelligent sequencing engine in communication with the processor and configured to:
identify one or more problem types from one or more events, wherein the identified problem types are associated with one or more configuration items (CIs) at issue;

identify one or more domains of the CIs at issue, wherein the domains represent one or more types of the CIs at issue;
check existence of one or more diagnosis and resolution sequences, wherein the checking of the diagnosis and resolution sequence is based on the identified problem types and the domain associated with the CI types;
fetch the diagnosis and resolution sequences, wherein the diagnosis and resolution sequences fetched at a first instance are at least in part updated with one or more new diagnosis and resolution sequences; and
execute the fetched diagnosis and resolution sequences iteratively such that a next step of the executed diagnosis and resolution sequence is based on an output of a first step of the executed diagnosis and resolution sequence.
18. The system as claimed in claim 17, wherein the
intelligent sequencing engine comprises a collaboration unit in
communication with the processor, said collaboration unit is
configured to identify the problem types associated with the
events associated with the CIs utilizing at least a natural
language understanding (NLU) technique and entity extraction
techniques.
19. The system as claimed in claim 17, wherein the
intelligent sequencing engine in communication with an
automation engine is configured to execute the diagnosis and
resolution sequences by providing the first step of the diagnosis
and resolution sequences and based on the output of the executed
first step providing the next step of diagnosis and resolution
sequence for execution until the events associated with the CIs
is completely resolved.
20. The system as claimed in claim 17, wherein the
intelligent sequencing engine in communication with the
automation engine fetches and executes the diagnosis and
resolution sequences based on one or more defined set of rules,

wherein the defined set of rules relates to the events associated with the CIs related to the domains.
21. The system as claimed in claim 20, wherein the defined
set of rules fetches and executes the diagnosis and resolution
sequences iteratively such that the next step of the diagnosis
and resolution sequence is provided based on the output of the
first step of the executed diagnosis and resolution sequence
until the events associated with the CIs associated with the
domain is completely resolved.
22. The system as claimed in claim 17, wherein the intelligent sequencing engine comprises an intelligent sequencing unit in communication with the processor, said intelligent sequencing unit is capable of being trained by inputting diagnosis and resolution sequence training data.
23. The system as claimed in claim 22, wherein the intelligent sequencing unit is configured to predict and execute the diagnosis and resolution sequences based on the output of the executed first step of the diagnosis and resolution sequences.
24. The system as claimed in claim 17, wherein the diagnosis and resolution sequences are stored in a log database, wherein the log database is updated from time to time based on feeding of one or more new diagnosis and resolution sequences.