Abstract: Described herein are systems, methods, and computer programs that may be utilized to warn about performance of steps that lead to an unsuccessful execution of a Business Process (BP). In one embodiment, a monitoring agent monitors interactions with an instance of a software system belonging to a certain organization and generates a stream comprising steps performed as part of the interaction. A warning module utilizes a model generated based on training data comprising prefixes of sequences corresponding to unsuccessful executions of one or more BPs, and determines whether the stream comprises a certain sequence of steps that corresponds to a prefix of an unsuccessful execution of a BP. Optionally, the training data comprises various sequences corresponding to executions of the BP associated with different organizations. The warning module also issues a warning responsive to determining that the stream comprises the certain sequence.

Abstract: 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.

Abstract: 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 an automaton. In one embodiment, a monitoring agent generates a stream of steps that were performed during interactions with an instance of a software system belonging to a certain organization, and a simulation module simulates a running of an automaton on an input comprising a sequence of steps. Optionally, parameters that define a behavior of the automaton are generated based on sequences of steps corresponding to executions of the BP associated with other organizations. Responsive to running the automaton on the stream, the automaton arrives at an accepting state at least once following a certain subsequence of steps that corresponds to a nonconsecutive execution of the BP, and the simulation module generates an indication indicative of a detection of an execution of the BP.

Abstract: Described herein are systems, methods, and computer programs for extracting a seed comprising steps common in executions of a Business Process (BP) and utilizing the seed to identify other executions of the BP. In one embodiment, the seed is extracted from sequences of steps from streams of steps performed during interactions with instances of a software system. The sequences comprise first and second sequences corresponding to executions of the BP, which are associated with first and second organizations, respectively. The seed comprises two or more consecutively performed steps that appear in each of the sequences. Occurrences of the seed are identified in one or more streams of steps performed during interactions with an instance of the software system belonging to a third organization. The occurrences are extended in order to generate candidate sequences that are evaluated to determine whether they correspond to executions of the BP.

Abstract: Described herein are systems, methods, and computer programs that may be utilized perform ensemble-based identification of sequences of steps corresponding to executions of a Business Processes (BP). 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 BP-scorer module utilizes models of the BP to calculate, for each sequence from among the candidate sequences, values indicative of whether the sequences correspond to executions of the BP. The models are generated based on sequences corresponding to executions of the BP that are associated with a plurality of organizations. An ensemble aggregator module utilizes the values calculated for each of the candidate sequences to identify among them one or more sequences that correspond to executions of the BP.

Abstract: 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.

Abstract: Described herein are systems, methods, and computer programs that may be utilized to identify executions of Business Processes (BPs) utilizing crowd-based patterns of the BPs. In one embodiment, each pattern of a BP is generated based on sequences corresponding to executions of the BP, which are associated with first and second organizations, respectively. A sequence parser module is configured to receive one or more streams of steps performed during interactions with an instance of a software system, which belongs to another organization, and to select, from among the one or more streams, candidate sequences of steps. A distance calculator module calculates distances between the candidate sequences and the patterns based on alignments of the candidate sequences and the patterns. An assignment module utilizes the distances to assign at least some of the candidate sequences with identifiers of the BPs.

Abstract: Systems, methods, and computer programs for generating a model for linking between steps performed when executing a Business Process (BP). In one embodiment, a link example collector receives sequences of steps, each corresponding to an execution of the BP, and identifies pairs of nonconsecutively performed steps in the sequences. A sample generator module generates samples, each corresponding to a pair, which comprises one or more feature values describing properties of a link from a first step to a second step performed after the first step. A linkage model generator module generates the model based on training samples comprising: (i) positive samples generated by the sample generator module based on pairs, identified by the link example collector module, of first and second steps which were nonconsecutively performed, and (ii) negative samples generated by the sample generator module based on pairs of steps that are not nonconsecutively performed steps from the sequences.

Abstract: Described herein are systems, methods, and computer programs for generating candidate sequences of steps utilizing links between steps that are nonconsecutively performed. In one embodiment, a link generator module generates links between pairs of steps that are among steps belonging to streams of steps performed during interactions with instances of software systems; at least some of the links are from a first step to a second step, and the first and second steps are not consecutively performed steps in the same stream. A candidate generation module utilizes the links to generate candidate sequences from steps belonging to the streams; the candidate generation module provides the candidate sequences to a system that identifies which of the candidate sequences correspond to executions of a Business Process (BP).

Abstract: 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.

Abstract: Described herein are systems, methods, and computer programs for generating a model for linking between steps performed when executing Business Processes (BPs). In one embodiment, a link example collector receives sequences of steps, each corresponding to an execution of a BP from among the BPs, and identifies pairs of nonconsecutively performed steps in the sequences. A linkage model generator module generates the model based on training samples comprising: (i) positive samples generated by the based on pairs, identified by the link example collector module, of first and second steps which were nonconsecutively performed, and (ii) negative samples generated by the sample generator module based on pairs of steps that are not nonconsecutively performed steps from the sequences. The positive samples comprise at least first and second samples generated based on first and second pairs in sequences corresponding to executions of respective first and second different BPs.