System and method of managing events of temporal data

1. A method of managing events of temporal data, after receiving at least one current event, the method comprising:

receiving, a plurality of historical events related to temporal data;

selecting at least one predefined feature of interest from the plurality of historical events;

segregating the plurality of historical events into one or more buckets based on a time of occurrence of the plurality of historical events, using a sliding time window of a predefined size which is determined prior to receiving at least one current event;

determining at least one pattern based on the one or more buckets, using a pattern mining technique based on one or more predefined parameters, wherein the one or more predefined parameters comprises a maximum length of a rule, and wherein the maximum length is indicative of a maximum number of events involved in the rule; and

obtaining a set of predefined rules from the at least one pattern, wherein the set of predefined rules are obtained prior to receiving at least one current event;

receiving the at least one current event related to the temporal data;

identifying at least one predefined feature of interest of the at least one current event;

correlating the at least one current event with one or more clusters of events based on the at least one predefined feature of interest, in one of a real-time manner and a scheduled manner, wherein the one or more clusters are obtained by generating at least one of a similarity matrix and a distance matrix for the at least one current event by using at least one of a community detection technique and a clustering technique,

wherein correlating the at least one current event in the scheduled manner comprises:

segregating the at least one current event into one or more buckets based on a time of occurrence of the at least one current event, using the sliding time window of the predefined size which is determined prior to receiving the at least one current event;

determining whether the one or more clusters is present in each of the one or more buckets based on the set of predefined rules which are obtained prior to receiving the at least one current event; and

correlating the at least one current event based on the set of predefined rules which are obtained prior to receiving the at least one current event; and

wherein correlating the at least one current event in the real-time manner comprises:

identifying at least one rule from the set of predefined rules which are obtained prior to receiving the at least one current event, that matches with the at least one current event; and

correlating the at least current event with the one or more clusters based on the identified at least one rule.

2. The method as claimed in claim 1 , wherein the one or more predefined parameters further comprises a minimum support of the rule,

wherein the minimum support is indicative of a frequency threshold of the rule.

3. The method as claimed in claim 1 , wherein segregating the at least one current event and the plurality of historical events using the sliding time window comprising:

selecting a size (t) and a step size (Δt) of the sliding time window; and

putting events occurring within the same window into a bucket based on the selected size (t) and the step size (Δt).

4. The method as claimed in claim 1 , wherein each of the predefined rules in the set of predefined rules comprising at least one of a unique rule identification number, an antecedent, and a consequent.

5. The method as claimed in claim 4 , when correlating the at least one current event in the scheduled manner, comprises:

filtering at least one rule from the set of predefined rules based on the at least one current event, wherein the at least one current event is mentioned in one of the antecedent and the consequent of the at least one rule.

6. The method as claimed in claim 4 , comprising:

identifying a plurality of rules, from the set of predefined rules, that match with the at least one current event, where each rule has a confidence score that is indicative of a probability of a consequent happening provided that an antecedent has occurred;

comparing confidence scores of the plurality of rules; and

correlating, based on the comparison, the at least one current event with the one or more clusters of one of the plurality of the rules having the highest confidence score.

7. The method as claimed in claim 6 , comprising:

identifying a plurality of rules, from the set of predefined rules, that matches with the at least one current event, wherein the plurality of rules have the same confidence score;

selecting one of the plurality of rules having the highest lift factor, wherein a lift factor is associated with each rule and is indicative of a probability of the consequent and the antecedent occurring together; and

correlating the at least one current event with the one or more clusters of the selected rule having the highest lift factor.

8. The method as claimed in claim 1 , wherein correlating the at least one current event in the real-time manner comprising:

determining that the at least one current event does not match with any of the set of predefined rules; and

generating a new cluster for the at least one current event based on the determination.

9. The method as claimed in claim 5 , wherein identifying the at least one rule when correlating the at least one current event in one of the scheduled manner and the real-time manner comprises:

determining whether a consequent or an antecedent of the at least one rule matches with the at least one current event; and

identifying the at least one rule based on the determination.

10. The method as claimed in claim 1 , comprising predicting at least one future event in one of the real-time manner and the scheduled manner,

wherein predicting the at least one future event in the scheduled manner comprises:

segregating the at least one current event into one or more buckets based on a time of occurrence of the at least one current event, using the sliding time window of the predefined size;

identifying at least one rule, from a set of predefined rules, that matches with the at least one current event; and

predicting at least one other event from the one or more clusters that has not yet occurred in a predetermined time period as the at least one future event; and

wherein predicting the at least one future event in the real-time manner comprises:

identifying at least one rule, from the set of predefined rules, an antecedent of which matches with the at least one current event; and

predicting at least one other event that is mentioned in a consequent of the at least one rule as the at least one future event.

11. A system for managing events of temporal data after receiving at least one current event, the system comprising:

a processor;

a receiving module configured to receive a plurality of historical events related to temporal data;

a selection module configured to select at least one predefined feature of interest from the plurality of historical events;

a segregation module configured to segregate the plurality of historical events into one or more buckets based on a time of occurrence of the plurality of historical events, using a sliding time window of a predefined size which is determined prior to receiving at least one current event;

a determining module configured to determine at least one pattern based on the one or more buckets, using a pattern mining technique based on one or more predefined parameters, wherein the one or more predefined parameters comprises a maximum length of a rule, and wherein the maximum length is indicative of a maximum number of events involved in the rule; and

a rule obtaining module configured to obtain a set of predefined rules from the at least one pattern, wherein the set of predefined rules are obtained prior to receiving at least one current event; wherein the receiving module is further configured to receive the at least one current event related to the temporal data, wherein

identification module is further configured to identify at least one predefined feature of interest of the at least one current even;

correlation module for correlating the at least one current event with one or more clusters of events based on the at least one predefined feature of interest, in one of a real-time manner and a scheduled manner, wherein the one or more clusters are obtained by generating at least one of a similarity matrix and a distance matrix for the at least one current event by using at least one of a community detection technique and a clustering technique,

wherein the processor, the receiving module, the identification module, and the correlation module are communicatively coupled with each other;

wherein for correlating the at least one current event in the scheduled manner, the correlation module is configured to:

segregate the at least one current event into one or more buckets based on a time of occurrence of the at least one current event, using the sliding time window of the predefined size which is determined prior to receiving the at least one current event;

determine whether the one or more clusters is present in each of the one or more buckets based on the set of predefined rules which are obtained prior to receiving the at least one current event; and

correlate the at least one current event based on the set of predefined rules which are obtained prior to receiving the at least one current event; and

wherein for correlating the at least one current event in the real-time manner, the correlation module is configured to:

identify at least one rule from the set of predefined rules which are obtained prior to receiving the at least one current event, that matches with the at least one current event; and

correlate the at least current event with the one or more clusters based on the identified at least one rule.

12. The system as claimed in claim 11 , wherein the one or more predefined parameters further comprises a minimum support of the rule, wherein the minimum support is indicative of a frequency threshold of the rule.

13. The system as claimed in claim 11 , wherein for segregating the at least one current event and the plurality of historical events using the sliding time window, the segregation module is configured to:

select a size (t) and a step size (Δt) of the sliding time window; and

put events occurring within the same window into a bucket based on the selected size (t) and the step size (Δt).

14. The system as claimed in claim 11 , wherein each of the predefined rules in the set of predefined rules comprising at least one of a unique rule identification number, an antecedent, and a consequent.

15. The system as claimed in claim 14 , wherein the correlation module, when correlating the at least one current event in the scheduled manner, is configured to:

filter at least one rule from the set of predefined rules based on the at least one current event, wherein the at least one current event is mentioned in one of the antecedent and the consequent of the at least one rule.

16. The system as claimed in claim 14 , wherein the correlation module is configured to:

identify a plurality of rules, from the set of predefined rules, that match with the at least one current event, where each rule has a confidence score that is indicative of a probability of a consequent happening provided that an antecedent has occurred;

compare confidence scores of the plurality of rules; and

correlate, based on the comparison, the at least one current event with the one or more cluster of one of the plurality of the rules having the highest confidence score.

17. The system as claimed in claim 16 , wherein the correlation module is configured to:

identify a plurality of rules, from the step of predefined rules, that matches with the at least one current event, wherein the plurality of rules have the same confidence score;

select one of the plurality of rules having the highest lift factor, wherein a lift factor is associated with each rule and is indicative of a probability of the consequent and the antecedent occurring together; and

correlate the at least one current event with the one or more cluster of the selected rule having the highest lift factor.

18. The system as claimed in claim 11 , wherein for correlating the at least one current event in the real-time manner, the correlation module is configured to:

determine that the at least one current event does not match with any of the set of predefined rules; and

generate a new cluster for the at least one current event based on the determination.

19. The system as claimed in claim 14 , wherein for identifying the at least one rule when correlating the at least one current event in one of the scheduled manner and the real-time manner, the correlation module is configured to:

determine whether a consequent or an antecedent of the at least one rule matches with the at least one current event; and

identify the at least one rule based on the determination.

20. The system as claimed in claim 11 , comprising:

a prediction module configured to predict at least one future event in one of the real-time manner and the scheduled manner,

wherein for predicting the at least one future event in the scheduled manner, the prediction module is configured to:

segregate the at least one current event into one or more buckets based on a time of occurrence of the at least one current event, using the sliding time window of the predefined size;

identify at least one rule, from a set of predefined rules, that matches with the at least one current event; and

predict at least one other event from the one or more clusters that has not occurred in a predetermined time period as the at least one future event; and

wherein for predicting the at least one future event in the real-time manner, the prediction module is configured to:

identify at least one rule from the set of predefined rules, an antecedent of which matches with the at least one current event; and

predict at least one other event that is mentioned in a consequent of the at least one rule as the at least one future event.