We claim:
1. A method of identifying a task sequence from an interaction stream, the method
comprising:
receiving, by a computing system (101), an interaction stream related to one or more interactions of one or more users with the computing system, one or more events that occurred from the one or more interactions, wherein the processed interaction stream is transformed into n-grams;
identifying, by the computing system (101), a plurality of potential data candidates for each of the n-grams by interpreting corresponding start markers and end markers;
transforming for each of the n-grams, by the computing system (101), each of the identified plurality of potential data candidates into a corresponding potential data candidate vector, wherein the potential data candidate vectors are numerical representation of the plurality of potential data candidates;
determining, by the computing system (101), a similarity score for each pair of the plurality of potential data candidates based on comparison of each of the plurality of the potential data candidate vectors of the corresponding pair of the plurality of potential data candidates; and
grouping, by the computing system (101), the plurality of potential data candidates into one or more groups based on the similarity score of a corresponding plurality of potential data candidates, wherein each of the one or more groups indicates a unique task sequence of the processed interaction stream.
2. The method as claimed in claim 1, wherein identifying each of the plurality of potential
data candidates comprises:
identifying a plurality of data candidates for each of the n-grams by defining corresponding start markers and end markers;
determining a weighted score for each of the plurality of data candidates based on frequency of a type of interaction in each of the n-grams and length of each of the plurality of data candidates;
normalizing the weighted score of each of the plurality of data candidates based on a predefined function to obtain a prioritization score, wherein each of the plurality of data candidates are prioritized based on the prioritization score;

eliminating one or more data candidates from the plurality of data candidates, wherein the elimination is based on the prioritization score of each of the plurality of data candidates and at least one of:
presence and position of one or more keywords in the plurality of data candidates, and
predefined acceptable range of length when overlapping plurality of data candidates with same start markers and end markers are present, wherein the plurality of data candidates remaining post elimination are identified as the plurality of potential data candidates.
3. The method as claimed in claim 1, wherein each of the identified plurality of potential data candidates are transformed into the corresponding potential data candidate vector based on at least one of a frequency of a type of interaction and presence or absence of the interaction, by comparing each interaction of each of the plurality of the data candidate with each corresponding interaction of each of rest of the plurality of data candidates.
4. The method as claimed in claim 1, wherein determining similarity score for each pair of the plurality of potential data candidates comprises:
for each pair of the plurality of potential data candidates,
comparing each interaction of each of the plurality of the potential data candidate vectors with each corresponding interaction of each of rest of the plurality of potential data candidate vectors; assigning:
a predefined first score to each interaction of the plurality of potential data candidates, when a comparison results in an exact match of frequency of a type of interaction,
a predefined second score to each interaction of the plurality of potential data candidates when the comparison results in mismatch of frequency of the type of interaction,
a predefined third score to each interaction of the plurality of potential data
candidates when the comparison results in absence of a common type of interaction;
determining a cumulative score based on the predefined first score, the
predefined second score and the predefined third score assigned to each interaction
of the corresponding plurality of potential data candidates; and

determining the similarity score based on the corresponding cumulative score and a predefined weightage of each of the n-grams.
5. The method as claimed in claim 1, wherein the n-grams are at least one of unigram, bigram, quad gram, pentagram, hexagram, and octagram.
6. The method as claimed in claim 1, further comprises:
performing, by the computing system (101), an inter-group and intra-group comparison of each of the one or more groups to determine presence of at least one overlapping interaction in the plurality of potential data candidates;
eliminating, by the computing system (101), one or more of the plurality of potential data candidates from the one or more groups when the presence of at least one of the overlapping interactions is determined, wherein elimination is performed based on at least one of, length of the plurality of data candidates that are determined to have overlapping interactions and frequency of a type of interactions in the plurality of data candidates that are determined to have overlapping interactions.
7. A computing system (101) of identifying a task sequence from an interaction stream, the
computing system (101) comprising:
a processor (109); and
a memory (113) communicatively coupled to the processor (109), wherein the memory (113) stores the processor-executable instructions, which, on execution, causes the processor (109) to:
receive an interaction stream related to one or more interactions of one or more users with the computing system (101), one or more events that occurred from the one or more interactions, wherein the processed interaction stream is transformed into n-grams;
identify a plurality of potential data candidates for each of the n-grams by defining corresponding start markers and end markers;
transform for each of the n-grams each of the identified plurality of potential data candidates into a corresponding potential data candidate vector, wherein the potential data candidate vectors are numerical representation of the plurality of potential data candidates;
determine a similarity score for each pair of the plurality of potential data candidates based on comparison of each of the plurality of the potential data candidate vectors of the corresponding pair of the plurality of potential data candidates; and

group the plurality of potential data candidates into one or more groups based on the similarity score of a corresponding plurality of potential data candidates, wherein each of the one or more groups indicates a unique task sequence of the processed interaction stream.
8. The computing system (101) as claimed in claim 7, wherein to identify each of the plurality
of potential data candidates, the processor (109) is configured to:
identify a plurality of data candidates for each of the n-grams by defining corresponding start markers and end markers;
determine a weighted score for each of the plurality of data candidates based on frequency of a type of interaction in each of the n-grams and length of each of the plurality of data candidates;
normalize the weighted score of each of the plurality of data candidates based on a predefined function to obtain a prioritization score, wherein each of the plurality of data candidates are prioritized based on the prioritization score;
eliminate one or more data candidates from the plurality of data candidates, wherein the elimination is based on the prioritization score of each of the plurality of data candidates and at least one of:
presence and position of one or more keywords in the plurality of data candidates,
and
predefined acceptable range of length when overlapping plurality of data candidates
with same start markers and end markers are present, wherein the plurality of data
candidates remaining post elimination is identified as the plurality of potential data
candidates.
9. The computing system (101) as claimed in claim 7, wherein each of the identified plurality of potential data candidates are transformed into the corresponding potential data candidate vector based on at least one of a frequency of a type of interaction and presence or absence of the interaction, by comparing each interaction of each of the plurality of the data candidate with each corresponding interaction of each of rest of the plurality of data candidates.
10. The computing system (101) as claimed in claim 7, wherein to determine similarity score for each pair of the plurality of potential data candidates,
for each pair of the plurality of potential data candidates, the processor (109) is configured to:

compare each interaction of each of the plurality of the potential data candidate vectors with each corresponding interaction of each of rest of the plurality of potential data candidate vectors; assign:
a predefined first score to each interaction of the plurality of potential data candidates, when a comparison results in an exact match of frequency of a type of interaction,
a predefined second score to each interaction of the plurality of potential data candidates when the comparison results in mismatch of frequency of the type of interaction,
a predefined third score to each interaction of the plurality of potential data
candidates when the comparison results in absence of a common type of interaction;
determine a cumulative score based on the predefined first score, the predefined
second score and the predefined third score assigned to each interaction of the
corresponding plurality of potential data candidates; and
determine the similarity score based on the corresponding cumulative score and a predefined weighted of each of the n-grams.
11. The computing system (101) as claimed in claim 7, wherein the n-grams are at least one of unigram, bigram and quad gram, pentagram, hexagram, and octagram.
12. The computing system (101) as claimed in claim 7, wherein the processor (109) is further configured to:
perform an inter-group and intra-group comparison of each of the one or more groups to determine presence of at least one overlapping interaction in the plurality of potential data candidates;
eliminate one or more of the plurality of potential data candidates from the one or more groups when the presence of at least one of the overlapping interactions is determined, wherein elimination is performed based on at least one of, length of the plurality of data candidates that are determined to have overlapping interactions and frequency of a type of interactions in the plurality of data candidates that are determined to have overlapping interactions.