US 2017/0109668 A1 - Model For Linking Between Nonconsecutively Performed Steps In A Business Process - The Lens

Model For Linking Between Nonconsecutively Performed Steps In A Business Process

Published: Apr 20, 2017
Earliest Priority: Dec 30 2012
Family: 1
Cited Works: 0
Cited by: 10
Cites: 0
Additional Info: Full text

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.

Claims

A system configured to generate a model for linking between steps performed when executing 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 link example collector module configured to receive sequences of steps selected from among steps belonging to streams of steps performed during interactions with instances of one or more software systems; wherein each sequence corresponds to an execution of the BP;

the link example collector module is further configured to identify pairs of nonconsecutively performed steps in the sequences;

a sample generator module configured to generate samples corresponding to pairs of steps; wherein each sample corresponding to a pair comprises one or more feature values describing properties of a link from a first step to a second step performed after the first step; and

a linkage model generator module configured to generate 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 from the streams.
The system of claim 1, wherein each pair of nonconsecutively performed steps in a sequence comprises a first step that is performed before a second step and appears directly after the first step in the sequence; and wherein and at least one of the following is true: (i) there is a third step that appears in the same stream as the first and seconds steps, the third step is performed after the first step and before the second step, but the third step does not appear in the sequence, and (ii) the first step belongs to a first stream and the second step belongs to a second stream.
The system of claim 1, wherein the positive set includes first and second samples generated based pairs of steps belonging to first and second sequences of steps; wherein the first sequence corresponds to an execution of a first BP associated with a first organization, and the second sequence corresponds to an execution of a second BP associated with a second organization, which is different from the first organization.
The system of claim 1, wherein the linkage model generator module is further configured to provide the model to a sequence parser module configured to select candidate sequences; wherein each candidate sequence is selected from among steps belonging to at least one stream of steps; and wherein the candidate sequences comprise a sequence that comprises a pair of nonconsecutively performed steps.
The system of claim 1, wherein the linkage model generator module is further configured to utilize a machine learning-based training algorithm to generate parameters of the model based on the positive and negative samples; wherein the model is utilized to calculate an output indicative of whether a certain first step and a certain second step, which is performed after the certain first step, belong to a sequence of steps corresponding to an execution of a BP; and wherein the output is calculated based on an input comprising one or more feature values describing properties of a link from the certain first step to the certain second step.
The system of claim 5, wherein the model comprises one or more of the following: parameters of a neural network, parameters of a support vector machine, parameters of a regression model, parameters of a graphical model.
The system of claim 1, wherein the model describes one or more rules for generating a link from a first step to a second step, which is performed after the first step; wherein each rule involves a condition involving the one or more feature values describing properties of a link from the first step to the second step.
The system of claim 7, wherein the linkage model generator module is further configured to utilize inductive logic concept learning to generate the one or more rules.
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 one or more software systems.
The system of claim 1, wherein the one or more feature values describing properties of the link from the first step to the second step comprise a feature value indicative of at least one of the following: a transaction executed as part of the first step, a transaction executed as part of the second step, a value of an Execution-Dependent Attribute (EDA) in the first step, and a value of the EDA in the second step; 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.
A method for generating a model for linking between steps performed when executing a business process (BP), comprising:
receiving, by a system comprising a processor and memory, sequences of steps selected from among steps belonging to streams of steps performed during interactions with instances of one or more software systems; wherein each sequence corresponds to an execution of the BP;

identifying pairs of nonconsecutively performed steps in the sequences;

generating positive samples based the pairs; wherein each of the positive samples comprises one or more feature values describing properties of a link from a first step of a pair from among the pairs, to the second step of that pair;

generating negative samples based on additional pairs of steps from the streams; wherein each of the negative samples comprises one or more feature values describing properties of a link from the first step of a pair, from among the additional pairs, to the second step of that pair; and

generating the model based on the positive and negative samples.
The method of claim 11, further comprising providing the model for utilization in selection of candidate sequences from among steps belonging to at least one stream of steps; wherein the candidate sequences comprise a sequence that comprises a pair of nonconsecutively performed steps.
The method of claim 11, further comprising utilizing a machine learning-based training algorithm to generate parameters of the model based on the positive and negative samples; wherein the model is utilized to calculate an output indicative of whether a certain first step and a certain second step, which is performed after the certain first step, belong to a sequence of steps corresponding to an execution of a BP; and wherein the output is calculated based on an input comprising one or more feature values describing properties of a link from the certain first step to the certain second step.
The method of claim 11, further comprising generating, based on the positive samples and the negative samples, one or more rules for generating a link from a first step to a second step, which is performed after the first step; wherein each rule involves a condition that is evaluated based on values of one or more feature values describing properties of a link from the first step to the second step; and wherein the model describes the one or more rules.
The method of claim 11, further comprising monitoring the interactions with the instances of the one or more software systems and generating the streams based on data collected during the monitoring.
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 steps belonging to streams of steps performed during interactions with instances of one or more software systems; wherein each sequence corresponds to an execution of a Business Process (BP);

identifying pairs of nonconsecutively performed steps in the sequences;

generating positive samples based the pairs; wherein each of the positive samples comprises one or more feature values describing properties of a link from a first step of a pair from among the pairs, to the second step of that pair;

generating negative samples based on additional pairs of steps from the streams; wherein each of the negative samples comprises one or more feature values describing properties of a link from the first step of a pair, from among the additional pairs, to the second step of that pair; and

generating, based on the positive and negative samples, a model a model for linking between steps performed when executing the BP.
The non-transitory computer-readable medium of claim 16, further comprising instructions defining a step of providing the model for utilization in selection of candidate sequences from among steps belonging to at least one stream of steps; wherein the candidate sequences comprise a sequence that comprises a pair of nonconsecutively performed steps.
The non-transitory computer-readable medium of claim 16, further comprising instructions defining a step of utilizing a machine learning-based training algorithm to generate parameters of the model based on the positive and negative samples; wherein the model is utilized to calculate an output indicative of whether a certain first step and a certain second step, which is performed after the certain first step, belong to a sequence of steps corresponding to an execution of a BP; and wherein the output is calculated based on an input comprising one or more feature values describing properties of a link from the certain first step to the certain second step.
The non-transitory computer-readable medium of claim 16, further comprising instructions defining a step of generating, based on the positive samples and the negative samples, one or more rules for generating a link from a first step to a second step, which is performed after the first step; wherein each rule involves a condition that is evaluated based on values of one or more feature values describing properties of a link from the first step to the second step; and wherein the model describes the one or more rules.
The non-transitory computer-readable medium of claim 16, further comprising instructions defining a step of monitoring the interactions with the instances of the one or more software systems and generating the streams based on data collected during the monitoring.