Described herein are systems, methods, and computer programs that may be utilized to identify 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 selected from among streams of steps performed during interactions with instances of a software system. Optionally, the sequences correspond to executions of the BP that are associated with a plurality of organizations. A sequence parser module is configured to receive 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. A BP-identifier module utilizes the crowd-based model to identify, from among the candidate sequences, one or more sequences of steps that correspond to executions of the BP.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system configured to identify executions of a Business Process (BP) utilizing a crowd-based model of the 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 BP model trainer module configured to receive sequences of steps selected from among streams of steps performed during interactions with instances of a software system; wherein each sequence corresponds to an execution of the BP; and wherein the sequences comprise first and second sequences corresponding to executions of the BP that are associated with first and second organizations, respectively; the BP model trainer module is further configured to generate the crowd-based model of the BP based on the sequences; a sequence parser module configured to receive one or more streams of steps performed during interactions with an instance of the software system, which belongs to a third organization, and to select, from among the one or more streams, candidate sequences of steps; and a BP-identifier module configured to utilize the crowd-based model to identify, from among the candidate sequences, one or more sequences of steps that correspond to executions of the BP. 2. 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. 3. 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 an instance of the software system; and wherein each monitoring agent that generates a stream is implemented, at least in part, via a program that is executed by an additional processor; wherein the additional processor belongs to at least one of the following machines: a client that provides a user with a user interface via which a user interacts with the instance, and a server upon which the instance runs. 4. The system of claim 1, wherein the crowd-based model of the BP comprises a pattern describing a sequence of steps involved in the execution of the BP. 5. The system of claim 1, wherein the BP model trainer module is further configured to receive additional sequences of steps, which do not correspond to executions of the BP, and to generate the crowd-based model of the BP based on the additional sequences. 6. The system of claim 5, wherein the BP model training is further configured to generate, based on the sequences and the additional sequences, an automaton configured to recognize an execution of the BP based on a sequence of steps; and wherein the crowd-based model comprises parameters of the automaton. 7. The system of claim 5, wherein the BP model training is further configured to utilize a machine learning training algorithm to generate the crowd-based model of the BP based on the sequences and the additional sequences; and wherein the crowd-based 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. 8. The system of claim 1, wherein a step belonging to a stream comprising steps performed as part of an interaction with an instance of the software system describes one or more of the following: a certain transaction that is executed, a certain screen that is displayed during the interaction, a certain form that is accessed during the interaction, a certain field that is accessed during the interaction, a certain value entered in a field belonging to a form, a certain operation performed from within a form, and a certain message returned by the software system during the interaction or following the interaction. 9. The system of claim 1, wherein the sequence parser module is further configured to identify a value of an execution-dependent attribute (EDA), and wherein at least some of the steps comprised in each candidate sequence are associated with the same value of the EDA; 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. 10. A method for identifying executions of a Business Process (BP) utilizing a crowd-based model of the BP, comprising: receiving, by a system comprising a processor and memory, sequences of steps selected from among streams of steps performed during interactions with instances of a software system; wherein each sequence corresponds to an execution of the BP; and wherein the sequences comprise first and second sequences corresponding to executions of the BP which are associated with first and second organizations, respectively; generating the crowd-based model of the BP based on the sequences; receiving one or more streams of steps performed during interactions with an instance of the software system, which belongs to a third organization; selecting, from among the one or more streams, candidate sequences of steps; and utilizing the crowd-based model to identify, from among the candidate sequences, one or more sequences of steps that correspond to executions of the BP. 11. The method of claim 10, further comprising monitoring the interactions with the instance of the software system and generating the one or more streams based on data collected during the monitoring. 12. The method of claim 11, wherein monitoring the interactions involves monitoring information exchanged between a client and the instance of the software system; and wherein the monitoring does not alter the information in a way that affects the execution of the BP. 13. The method of claim 11, wherein monitoring the interactions involves performing at least one of the following operations: (i) initiating an execution, on the instance of the software system, of a function of a packaged application, (ii) retrieving, via a query sent to the instance of the software system, a record from a database, and (iii) accessing a log file created by the instance of the software system. 14. The method of claim 10, wherein generating the crowd-based model comprises generating a pattern describing a sequence of steps involved in the execution of the BP. 15. The method of claim 10, further comprising receiving additional sequences of steps, which do not correspond to executions of the BP, and generating the crowd-based model of the BP based on the additional sequences. 16. The method of claim 15, further comprising generate, based on the sequences and the additional sequences, an automaton configured to recognize an execution of the BP based on a sequence of steps; wherein the crowd-based model comprises parameters of the automaton. 17. The method of claim 15, further comprising utilizing a machine learning training algorithm to generate the crowd-based model of the BP based on the sequences and the additional sequences; wherein the crowd-based 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. 18. 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 sequences of steps selected from among streams of steps performed during interactions with instances of a software system; wherein each sequence corresponds to an execution of a Business Process (BP); and wherein the sequences comprise first and second sequences corresponding to executions of the BP which are associated with first and second organizations, respectively; generating a crowd-based model of the BP based on the sequences; receiving one or more streams of steps performed during interactions with an instance of the software system, which belongs to a third organization; selecting, from among the one or more streams, candidate sequences of steps; and utilizing the crowd-based model to identify, from among the candidate sequences, one or more sequences of steps that correspond to executions of the BP. 19. The non-transitory computer-readable medium of claim 18, further comprising instructions defining a step of monitoring the interactions with the instance of the software system and generating the one or more streams based on data collected during the monitoring. 20. The non-transitory computer-readable medium of claim 18, further comprising instructions defining the following steps: receiving additional sequences of steps, which do not correspond to executions of the BP, and generating the crowd-based model of the BP based on the additional sequences.