Described herein are systems, methods, and computer programs that may be utilized to identify nonconsecutive executions of a Business Process (BP) utilizing a crowd-based model of the BP. In one embodiment, a BP model trainer module generates the crowd-based model of the BP based on sequences of steps corresponding to nonconsecutive executions of the BP, which are associated with at least first and second organizations. In one embodiment, the crowd-based model is utilized to identify nonconsecutive executions of the BP. A sequence parser module receives one or more streams of steps performed during interactions with an instance of the software system, which belongs to another organization, and to select, from among the one or more streams, candidate sequences of steps. Additionally, a BP-identifier module utilizes the crowd-based model to identify, from among the candidate sequences, a sequence of steps that corresponds to a nonconsecutive execution of the BP.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system configured to generate a model useful for identifying nonconsecutive executions of a business process (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 positive example collector module configured to select, from among streams of steps performed during interactions with instances of one or more software systems, a positive set comprising sequences comprising steps involved in executions of the BP; wherein at least some of the sequences correspond to nonconsecutive executions of the BP; wherein a sequence corresponding to a nonconsecutive execution of the BP comprises at least first and second steps from a stream that also comprises a third step, which is not involved in an execution of the BP, and which is performed after the first step is performed and before the second step is performed; and wherein the sequences comprise first and second sequences corresponding to executions of the BP associated with first and second organizations, respectively; a negative example collector module configured to select a negative set comprising additional sequences of steps that do not correspond to executions of the BP; and a BP model trainer module configured to generate the model of the BP based on the positive set and the negative set, and to provide the model for use by a system that identifies executions of the BP. 2. The system of claim 1, further comprising a plurality of monitoring agents configured to generate the streams of steps; wherein each monitoring agent generates a stream comprising steps performed as part of an interaction with an instance of a software system from among the one or more software systems. 3. The system of claim 1, wherein the first and second steps comprised in the sequence corresponding to the nonconsecutive execution of the BP are both associated with a certain value of an execution-dependent attribute (EDA) and the third step from the stream that comprises the first and second steps is associated with a value for the EDA which is different from the certain value; and wherein the EDA corresponds to one or more of the following types of values: a mailing address, a Universal Resource Locator (URL) address, an Internet Protocol (IP) address, a phone number, an email address, a social security number, a driving license number, an address on a certain blockchain, an identifier of a digital wallet, an identifier of a client, an identifier of an employee, an identifier of a patient, an identifier of an account, and an order number. 4. The system of claim 1, wherein the positive set comprises at least first and second sequences of steps, and the first sequence comprises more steps than the second sequence. 5. The system of claim 1, wherein the positive example collector module is further configured to receive indications identifying the sequences of steps in the streams that correspond to executions of the BP and to utilize the indications to select at least some of the sequences in the positive set. 6. The system of claim 1, wherein the model of the BP comprises a pattern describing a sequence of steps involved in the execution of the BP. 7. The system of claim 6, wherein at least some of the sequences in the positive set correspond to consecutive executions of the BP; whereby in a sequence of steps corresponding to a consecutive execution of the BP, there are no first and second steps from a stream, which are performed sequentially, and the stream also comprises a third step that is not involved in the execution of the BP, and which is performed after the first step and before the second step; and wherein the pattern is obtained utilizing an alignment of the at least some of the sequences that correspond to nonconsecutive executions of the BP with the at least some of the sequences that correspond to consecutive executions of the BP. 8. The system of claim 6, wherein each step belonging to the sequence described by the pattern is included in at least 50% of the sequences in the positive set. 9. The system of claim 1, wherein the model of the BP describes an automaton configured to recognize an execution of the BP based on a sequence of steps that comprises steps involved in an execution of the BP. 10. The system of claim 1, wherein the model of the BP comprises parameters used by a machine learning-based predictor configured to receive feature values determined based on a sequence of steps and to calculate a value indicative of a probability that the sequence of steps represents an execution of the BP. 11. A method for generating a model useful for identifying nonconsecutive executions of a business process (BP), comprising: receiving, by a system comprising a processor and memory, streams of steps performed during interactions with instances of one or more software systems; selecting, from among the streams, a positive set comprising sequences of steps involved in executions of the BP; wherein each sequence of steps comprises steps that appear in one or more of the streams; wherein at least some of the sequences correspond to nonconsecutive executions of the BP; wherein a sequence corresponding to a nonconsecutive execution comprises at least first and second steps from a stream that also comprises a third step, which is not involved in the execution of the BP, and which is performed after the first step is performed and before the second step is performed; and wherein the sequences comprise first and second sequences corresponding to executions of the BP associated with first and second organizations, respectively; selecting a negative set comprising additional sequences of steps; generating the model of the BP based on the positive set and the negative set; and providing the model to be utilized for identifying executions of the BP. 12. The method of claim 11, further comprising monitoring the interactions with the instances of the one or more software systems; wherein the monitoring involves at least one of the following types of monitoring: internal monitoring, and interface monitoring. 13. The method of claim 11, further comprising utilizing, as part of the negative set, sequences corresponding to executions of BPs that are different from the BP. 14. The method of claim 11, further comprising aligning the sequences belonging to the positive set in order to obtain a consensus pattern of steps that appear in most of the sequences. 15. The method of claim 11, further comprising generating an automaton based on the positive and negative sets; wherein the automaton recognizes most of the sequences belonging to the positive set and does not recognize most of the sequences belonging to the negative set. 16. The method of claim 11, wherein generating the model comprises generating 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. 17. 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 operations comprising: receiving streams of steps performed during interactions with instances of one or more software systems; selecting, from among the streams, a positive set comprising sequences of steps involved in executions of a Business Process (BP); wherein each sequence of steps comprises steps that appear in one or more of the streams; wherein at least some of the sequences correspond to nonconsecutive executions of the BP; wherein a sequence corresponding to a nonconsecutive execution comprises at least first and second steps from a stream that also comprises a third step, which is not involved in the execution of the BP, and which is performed after the first step is performed and before the second step is performed; and wherein the sequences comprise first and second sequences corresponding to executions of the BP associated with first and second organizations, respectively; selecting a negative set comprising additional sequences of steps; generating a model of the BP based on the positive set and the negative set; and providing the model to be utilized for identifying executions of the BP. 18. The non-transitory computer-readable medium of claim 17, further comprising instructions defining a step of monitoring the interactions with the instances of the one or more software systems; wherein the monitoring involves at least one of the following types of monitoring: internal monitoring, and interface monitoring. 19. The non-transitory computer-readable medium of claim 17, further comprising instructions defining a step of utilizing, as part of the negative set, sequences corresponding to executions of BPs that are different from the BP. 20. The non-transitory computer-readable medium of claim 17, further comprising instructions defining a step of aligning the sequences belonging to the positive set in order to obtain a consensus pattern of steps that appear in most of the sequences.