Described herein are systems, methods, and computer programs that may be utilized to identify a sequence corresponding to an execution of a Business Processes (BP) using a machine learning-based model of the BP generated based on sequences corresponding to previous executions of the BP by a plurality of organizations. In one embodiment, a sequence parser module receives one or more streams of steps performed during interactions with an instance of a software system, which belongs to a certain organization, and selects, from among the one or more streams, candidate sequences of steps. A feature generator module generate, for each sequence from among the candidate sequences, a plurality of feature values. And a predictor module utilizes the model to calculate, based on an input comprising the plurality of feature values generated for the sequence, a value indicative of whether the sequence corresponds to an execution of the BP.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system configured to utilize a machine learning-based model to identify a sequence corresponding to an execution of a business processes (BP), comprising: memory configured to store computer executable modules; and one or more processors configured to execute the computer executable modules; the computer executable modules comprising: a sequence parser module configured to receive one or more streams of steps performed during interactions with an instance of a software system, which belongs to a certain organization, and to select, from among the one or more streams, candidate sequences of steps; a feature generator module to receive a sequence from among the candidate sequences and to generate a plurality of feature values based on the sequence; and a predictor module configured to utilize a model of the BP to calculate, based on an input comprising the plurality of feature values, a value indicative of whether the sequence corresponds to an execution of the BP; wherein the model is generated based on sequences corresponding to previous executions of the BP, which comprise first and second sequences that are associated with first and second organizations, respectively. 2. The system of claim 1, further comprising a machine learning training module configured to utilize feature values generated by the feature generator module for a positive set of sequences and a negative set of sequences; wherein the positive set comprises the first and second sequences, and most of the sequences in the positive set correspond to executions of the BP; and wherein most of the sequences in the negative set do not correspond to executions of the BP. 3. The system of claim 2, further comprising an example collector module configured to collect sequences belonging to the positive set from among streams of steps performed during interactions with instances of the software system. 4. The system of claim 3, further comprising a negative example collector module is configured to collect at least some of the sequences belonging to the negative set from among the steps belonging to the streams. 5. The system of claim 1, wherein the plurality of feature values generated based on the sequence of steps comprise a feature value that is indicative of one or more of the following: a certain transaction executed in one or more of the steps, a certain order of transactions executed in the steps, a certain screen presented in one or more of the steps, a certain order of screens presented in the steps, a certain field accessed in at least one of the steps, a certain order of accessing fields in one or more of the steps, a certain value entered in a field in at least one of the steps, a certain message received from a system as part of at least one of the steps. 6. The system of claim 1, wherein the plurality of feature values generated based on the sequence of steps comprise a feature value that is indicative of one or more of the following: the number of steps in the sequence, the duration it took to perform the steps in the sequence, an identity of a user who performed a step from among the steps, an identity of a system on which one of the steps was performed, an identity of an organization to which belongs a user who performed one of the steps, and an identity of an organization to which belongs a system on which one of the steps was performed. 7. The system of claim 1, wherein the model comprises at least one of the followings sets of parameters: parameters of a neural network, parameters for a support vector machine, parameters of a naïve Bayesian model, logistic regression parameters, and parameters of a decision tree. 8. The system of claim 1, wherein the sequence comprises first, second, and third steps belonging to a certain stream from among the one or more streams; wherein the first step was performed before the second step and the second step was performed before the third step; and wherein the first and third steps are involved in a certain execution of the BP, while the second step is not involve in the certain execution of the BP. 9. The system of claim 1, wherein an execution of a BP is associated with an organization if at least one of the following statements is true: (i) at least some steps involved in the execution of the BP are performed by a user belonging to the organization, and (ii) at least some steps involved in the execution of the BP are executed on a certain instance of a software system belonging to the organization. 10. The system of claim 1, further comprising one or more monitoring agents configured to generate the one or more streams of steps; wherein each monitoring agent generates a stream comprising steps performed as part of an interaction with the instance of the software system. 11. A method for utilizing a machine learning-based model to identify a sequence corresponding to an execution of a business processes (BP), comprising: receiving, by a system comprising a processor and memory, one or more streams of steps performed during interactions with instances of a software system, which belongs to a certain organization; selecting, from among the one or more streams, candidate sequences of steps; generating, for each sequence among the candidate sequences, a plurality of feature values based on the sequence; and utilizing a model of the BP to calculate, based on an input comprising the plurality of feature values generated for each sequence among the candidate sequences, a value indicative of whether the sequence corresponds to an execution of the BP; wherein the model is generated based on sequences corresponding to previous executions of the BP, which comprise first and second sequences that are associated with first and second organizations, respectively. 12. The method of claim 11, further comprising utilizing samples to generate the model; wherein the samples comprise feature values generated for sequences belonging to a positive set of sequences, and feature values generated for sequences belonging to a negative set of sequences; wherein the positive set comprises the first and second sequences, and most of the sequences in the positive set correspond to executions of the BP; and wherein most of the sequences in the negative set do not correspond to executions of the BP. 13. The method of claim 11, further comprising generating, for each sequence among the candidate sequences, a feature value that is indicative of one or more of the following: a certain transaction executed in one or more of the steps, a certain order of transactions executed in the steps, a certain screen presented in one or more of the steps, a certain order of screens presented in the steps, a certain field accessed in at least one of the steps, a certain order of accessing fields in one or more of the steps, a certain value entered in a field in at least one of the steps, a certain message received from a system as part of at least one of the steps. 14. The method of claim 11, further comprising generating, for each sequence among the candidate sequences, a feature value that is indicative of one or more of the following: the number of steps in the sequence, the duration it took to perform the steps in the sequence, an identity of a user who performed a step from among the steps, an identity of a system on which one of the steps was performed, an identity of an organization to which belongs a user who performed one of the steps, and an identity of an organization to which belongs a system on which one of the steps was performed. 15. The method of claim 11, further comprising monitoring the interactions with the instances of the software system; wherein the monitoring involves at least one of the following types of monitoring: internal monitoring, and interface monitoring. 16. A non-transitory computer-readable medium having instructions stored thereon that, in response to execution by a system including a processor and memory, causes the system to perform steps comprising: receiving one or more streams of steps performed during interactions with instances of a software system, which belongs to a certain organization; selecting, from among the one or more streams, candidate sequences of steps; generating, for each sequence among the candidate sequences, a plurality of feature values based on the sequence; and utilizing a model of a Business Process (BP) to calculate, based on an input comprising the plurality of feature values generated for each sequence among the candidate sequences, a value indicative of whether the sequence corresponds to an execution of the BP; wherein the model is generated based on sequences corresponding to previous executions of the BP, which comprise first and second sequences that are associated with first and second organizations, respectively. 17. The non-transitory computer-readable medium of claim 16, further comprising instructions defining a step of utilizing samples to generate the model; wherein the samples comprise feature values generated for sequences belonging to a positive set of sequences, and feature values generated for sequences belonging to a negative set of sequences; wherein the positive set comprises the first and second sequences, and most of the sequences in the positive set correspond to executions of the BP; and wherein most of the sequences in the negative set do not correspond to executions of the BP. 18. The non-transitory computer-readable medium of claim 16, further comprising generating, for each sequence among the candidate sequences, a feature value that is indicative of one or more of the following: a certain transaction executed in one or more of the steps, a certain order of transactions executed in the steps, a certain screen presented in one or more of the steps, a certain order of screens presented in the steps, a certain field accessed in at least one of the steps, a certain order of accessing fields in one or more of the steps, a certain value entered in a field in at least one of the steps, a certain message received from a system as part of at least one of the steps. 19. The non-transitory computer-readable medium of claim 16, further comprising generating, for each sequence among the candidate sequences, a feature value that is indicative of one or more of the following: the number of steps in the sequence, the duration it took to perform the steps in the sequence, an identity of a user who performed a step from among the steps, an identity of a system on which one of the steps was performed, an identity of an organization to which belongs a user who performed one of the steps, and an identity of an organization to which belongs a system on which one of the steps was performed. 20. The non-transitory computer-readable medium of claim 16, further comprising instructions defining a step of monitoring the interactions with the instances of the software system; wherein the monitoring involves at least one of the following types of monitoring: internal monitoring, and interface monitoring.