Method for managing a plurality of events

1. A method for managing a plurality of events, wherein each event comprises physical attributes and logical attributes, the method comprising the steps of:

creating tuples, wherein each tuple corresponds to events having all the same logical attributes;

providing a set of hierarchized relations between tuples, wherein combinations of two tuples form a pair of tuples, by means of an unsupervised machine learning pipeline algorithm, wherein parent-child relations are provided between tuples, by:

creating a plurality of co-occurrence matrices, each co-occurrence matrix reflecting different time intervals, wherein each column corresponds with a tuple and each row corresponds with a time window, so each matrix entry represents the number of times that an event corresponding to each tuple appears in each time window;

successively applying a heuristic function to each matrix entry of said plurality of co-occurrence matrices to obtain a co-occurrence probabilistic score for each pair of tuples, wherein the probabilistic score indicates the probability that one tuple of the pair, referred to as child tuple, co-occurs with the other tuple of the pair, referred to as parent tuple; and

using the probabilistic score of each pair of tuples to quantify the strength of the parent-child relations;

classifying the tuples in families, each family contains all the tuples related according to the parent-child relation provided by the unsupervised machine learning pipeline algorithm;

identifying the parent tuple of each family, defined as the tuple which has at least one child and has no parent; and

presenting the parent tuple instances of each family, together with the physical attributes of the events associated to each parent tuple instance of each family.

2. The method according to claim 1 , wherein the unsupervised machine learning algorithm further comprises:

dividing the events into two samples;

wherein the step of creating the plurality co-occurrence matrices, comprises apply the for each of the two samples wherein each column corresponds with a tuple and each row corresponds with a time window, so each matrix entry represents the number of times that an event of said each sample corresponding to each tuple appears in each time window;

repeat applying the heuristic function to obtain a co-occurrence probabilistic score for each pair of tuples, wherein the probabilistic score indicates the probability that one tuple of the pair, referred to as child tuple, co-occurs with the other tuple of the pair, referred to as parent tuple;

identifying common parent-child relations in the two samples; and

use the identified common parent-child relations to provide a final set of parent-child relations between tuples.

3. The method according to claim 2 , wherein, prior to the step of dividing the events into two samples, the method comprises the step of cleaning the tuples, by deleting those tuples which do not fulfill a plurality of minimum requirements.

4. The method according to claim 2 , further comprises:

creating a graph of parent-child relations based on the results of the heuristic function

calculating a probability for each parent-child relation and mark those parent-child relations which has a probability higher than a predetermined threshold as strong.

5. The method according to claim 3 , further comprising the step of creating a first attempt of parent-child relations by:

creating a graph of parent-child relations based on the results of the heuristic function; and

calculating a probability for each parent-child relation and mark those parent-child relations which has a probability higher than a predetermined threshold as strong.

6. The method according to claim 2 , further comprising the step of choosing a co-occurrence matrix based on having better probabilistic scores for parent-child relations vis-à-vis other co-occurrence matrices and use the parent-child relations generated by the optimal co-occurrence matrix to provide the final set of parent-child relations between tuple.

7. The method according to claim 3 , further comprising the step of choosing a co-occurrence matrix based on having better probabilistic scores for parent-child relations vis-à-vis other co-occurrence matrices and use the parent-child relations generated by the optimal co-occurrence matrix to provide the final set of parent-child relations between tuples.

8. The method according to claim 1 , wherein the step of presenting the parent tuples comprises presenting instances associated to each parent tuple.

9. The method according to claim 1 , wherein the step of presenting the parent tuples comprises presenting instances associated to each parent tuple.

10. The method according to claim 2 , wherein the step of presenting the parent tuples comprises presenting instances associated to each parent tuple.

11. The method according to claim 3 , wherein the step of presenting the parent tuples comprises presenting the instances associated to each parent tuple.

12. The method according to claim 1 , wherein the step of presenting the parent tuples comprises conferring a severity index to each parent tuple of each family, so that the final list of parent tuples is hierarchized.

13. The method according to claim 1 , wherein the step of presenting the parent tuple instances comprises conferring a severity index to each parent tuple instance of each family, so that the final list of parent tuple instances is hierarchized.

14. The method according to claim 2 , wherein the step of presenting the parent tuples comprises conferring a severity index to each parent tuple of each family, so that the final list of parent tuples is hierarchized.