Patent Application
20230393567
The present invention generally relates to process mining of computer processes, and more specifically, to a constraint operator for the analysis of exclusive-or (XOR) nodes in process models of computer processes.
Processes are sequences of activities executed by one or more computers to provide various services. The execution of a process may be represented as a process model, where each activity is represented as a node and execution between activities is represented as an edge linking nodes. In process mining, process models are evaluated in order to identify improvements to the execution of processes. Often times, process models may include exclusive-or (XOR) nodes. However, such process models do not provide any information on the execution of the paths from the XOR nodes, such as, e.g., information relating to when and why certain paths from the XOR nodes are executed. Conventionally, information on the execution of paths from XOR nodes in a process model is determined using a separate software tool. However, this conventional approach results in reduced efficiency and increased costs. Accordingly, an improved and/or alternative approach may be beneficial.
Certain embodiments of the present invention may provide alternatives or solutions to the problems and needs in the art that have not yet been fully identified, appreciated, or solved by current process mining technologies. For example, some embodiments of the present invention pertain to a constraint operator for the analysis of exclusive-or (XOR) nodes in process models of computer processes.
In accordance with one embodiment, a process model representing execution of a process is received. Exclusive-or blocks in the process model are identified. Attribute data relating to an exclusive outgoing path from an exclusive-or node in each of the identified exclusive-or blocks are identified. At least one of the exclusive-or node or the exclusive outgoing paths are annotated based on the attribute data. The annotated at least one of the exclusive-or node or the exclusive outgoing paths are output.
In one embodiment, the attribute data comprises numerical statistics comprising one or more a mean, a median, a number of quantiles, or a probability density function for an attribute. In another embodiment, the attribute data comprises categorical statistics comprising one or more of a mode or a probability mass function.
In one embodiment, a decision tree for one or more of the attributes is generated. The decision tree denotes a threshold value to split paths. User input selecting the exclusive-or node or the exclusive outgoing paths may be received. In response to receiving the user input, the attribute data may be displayed.
In one embodiment, the process is executed using one or more computing devices. In one embodiment, the process is a robotic process automation (RPA) process executed by one or more RPA robots.
In order that the advantages of certain embodiments of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. While it should be understood that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:
Unless otherwise indicated, similar reference characters denote corresponding features consistently throughout the attached drawings.
A computer process may be executed by one or more computing devices to provide services for a number of different applications, such as, e.g., administrative applications (e.g., onboarding a new employee), procure-to-pay applications (e.g., purchasing, invoice management, and facilitating payment), and information technology applications (e.g., ticketing systems). In one embodiment, the process may be an RPA (robotic process automation) process automatically executed by one or more RPA robots.
In process mining, a process model representing execution of the process is evaluated in order to identify improvements to the execution of the process. In accordance with embodiments described herein, a constraint operator is provided for the analysis of exclusive-or (XOR) nodes in process models of computer processes. The constraint operator annotates exclusive-or nodes and/or exclusive paths with information relating to the XOR nodes in the process models, such as, e.g., information relating to when and why certain paths from the XOR nodes are executed. Advantageously, such annotation facilitates user evaluation of the process model, thereby increasing efficiency and decreasing costs as compared to conventional approaches.
It should be understood that while embodiments described herein are described for annotating exclusive-or nodes and/or exclusive paths outgoing from the exclusive-or nodes, embodiments described herein are not so limited. Embodiments described herein may be applied to annotate nodes and/or outgoing paths from such nodes defining any relationship operator, such as, e.g., parallel, looping, sequential, etc. and are not limited to exclusive-or relationships.
At step 102 of
Process model 200 may comprise various gateway nodes, which are shown as diamond-shaped nodes in ”). Other types of relationships are also contemplated, such as, e.g., sequential relationships.
In one embodiment, process model 200 is generated using a probabilistic inductive miner, for example, as described in U.S. Patent Application Publication No. 2022/0075705, the disclosure of which is incorporated herein by reference in its entirety. However, process model 200 may be generated using any other suitable approach. Process model 200 is generated from an event log recording execution of the process. An illustrative event log is shown in
At step 104 of
At step 106 of
The attribute data may comprise any data relating to an exclusive outgoing path from an exclusive-or node in an exclusive-or block. In one embodiment, the attribute data may comprise numerical statistics, for example, represented as a list of numbers associated with one or more attributes and statistical measures of the list of numbers for each of the attributes, such as, e.g., mean, median, number of quantiles, probability density function mapping a quantile to a percentage of entries that are part of the quantile out of all entries. In another embodiment, the attribute data may comprise categorical statistics, for example, represented as a list of categorical data entries associated with one or more attributes and statistical measures of the list of categorical data for each of the attributes, such as, e.g., mode, probability mass function mapping each possible entry to the percentage of entries with that value out of all the entries.
In one embodiment, prior to determining the attribute data, each exclusive-or block is evaluated to identify skip locations in the exclusive outgoing path. A skip is an exclusive-or block, where one of the paths from the beginning exclusive-or node to the ending exclusive-or node does not contain any nodes, and hence nothing is done if that path is chosen. In effect, all other paths are “skipped”. In addition, given the skip locations, activities of other exclusive outgoing paths of the exclusive-or block are identified. In one embodiment, the skip locations are identified using a helper function FindAllSkips, which receives as input the process model and the exclusive-or nodes and generates as output a list of node IDs, and the activities of the other exclusive outgoing paths are identified using a helper function ExtractEventsToBeExcluded, which receives as input the skip locations and generates as output a list of event names to be excluded. The skip locations and the activities of the other exclusive outgoing paths are not considered at step 106, for example, by updating the mapping between exclusive paths and activities to be considered. If a skip needs to be handled, then the data attributes for the skip are related to events from the process model that are not part of traces that include the events from the other exclusive outgoing paths. To correlate attribute data to that skip, the event attribute data of traces that do not contain the events of any of the other exclusive outgoing paths in the block are used.
At step 108 of
At step 110 of
In one embodiment, decision trees are generated denoting, for each respective attribute, 1) the best numerical value on which to split and 2) the paths most often traversed if looking only at the values of the respective attribute. The decision trees may be generated based on the aggregated numerical attribute values corresponding to the paths of the exclusive-or node. Based on the distribution of the values of the numerical attribute data, split points are identified, creating value ranges. These value ranges are then correlated with the exclusive-or node paths whose attribute values most closely adhere to the split ranges. In one embodiment, the best numerical threshold value is determined using a helper function ApplyAllFilters, which receives as input the list of exclusive-or blocks and generates as output two dictionaries indexed by the attribute name, one for the categorical attributes and one for the numerical attributes.
In one embodiment, the decision trees can be extended to have a depth larger than 1, which results in increasing the visual complexity. This can lead to obtaining decisive and robust constraints that are based on multiple dimensions at the same time.
In one embodiment, the number of data structures used may be decreased from two (one for numerical statistics and one or categorical statistics) to one by introducing a generic data type.
In one embodiment, the constraint operator can represent the intervals on which exclusive paths overlap and present a probabilistic representation of the number of occurrences of each path that are part of the overlapping interview.
In one embodiment, the constraint operator can represent the start point of modelling decision enforcement based on attribute values. This can be achieved in, in an exclusive-or block, there are clear differences between the attributes associated with every exclusive path. This capability answers the questions “how is this decision influenced by this attribute?”
Advantageously, embodiments described herein generated annotated exclusive outgoing paths of process models with a big-O complexity of polynomial. This may be achieved using helper functions that limit the entries, in accordance with the need of the exclusive-or block, by looking at the cases individually.
Computing system 1000 further includes a memory 1015 for storing information and instructions to be executed by processor(s) 1010. Memory 1015 can be comprised of any combination of random access memory (RAM), read-only memory (ROM), flash memory, cache, static storage such as a magnetic or optical disk, or any other types of non-transitory computer-readable media or combinations thereof. Non-transitory computer-readable media may be any available media that can be accessed by processor(s) 1010 and may include volatile media, non-volatile media, or both. The media may also be removable, non-removable, or both. Computing system 1000 includes a communication device 1020, such as a transceiver, to provide access to a communications network via a wireless and/or wired connection. In some embodiments, communication device 1020 may include one or more antennas that are singular, arrayed, phased, switched, beamforming, beamsteering, a combination thereof, and or any other antenna configuration without deviating from the scope of the invention.
Processor(s) 1010 are further coupled via bus 1005 to a display 1025. Any suitable display device and haptic I/O may be used without deviating from the scope of the invention.
A keyboard 1030 and a cursor control device 1035, such as a computer mouse, a touchpad, etc., are further coupled to bus 1005 to enable a user to interface with computing system 1000. However, in certain embodiments, a physical keyboard and mouse may not be present, and the user may interact with the device solely through display 1025 and/or a touchpad (not shown). Any type and combination of input devices may be used as a matter of design choice. In certain embodiments, no physical input device and/or display is present. For instance, the user may interact with computing system 1000 remotely via another computing system in communication therewith, or computing system 1000 may operate autonomously.
Memory 1015 stores software modules that provide functionality when executed by processor(s) 1010. The modules include an operating system 1040 for computing system 1000. The modules further include a constraint operator module 1045 that is configured to perform all or part of the processes described herein or derivatives thereof. Computing system 1000 may include one or more additional functional modules 1050 that include additional functionality.
One skilled in the art will appreciate that a “computing system” could be embodied as a server, an embedded computing system, a personal computer, a console, a personal digital assistant (PDA), a cell phone, a tablet computing device, a quantum computing system, or any other suitable computing device, or combination of devices without deviating from the scope of the invention. Presenting the above-described functions as being performed by a “system” is not intended to limit the scope of the present invention in any way, but is intended to provide one example of the many embodiments of the present invention. Indeed, methods, systems, and apparatuses disclosed herein may be implemented in localized and distributed forms consistent with computing technology, including cloud computing systems. The computing system could be part of or otherwise accessible by a local area network (LAN), a mobile communications network, a satellite communications network, the Internet, a public or private cloud, a hybrid cloud, a server farm, any combination thereof, etc. Any localized or distributed architecture may be used without deviating from the scope of the invention.
It should be noted that some of the system features described in this specification have been presented as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom very large scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, graphics processing units, or the like.
A module may also be at least partially implemented in software for execution by various types of processors. An identified unit of executable code may, for instance, include one or more physical or logical blocks of computer instructions that may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may include disparate instructions stored in different locations that, when joined logically together, comprise the module and achieve the stated purpose for the module. Further, modules may be stored on a computer-readable medium, which may be, for instance, a hard disk drive, flash device, RAM, tape, and/or any other such non-transitory computer-readable medium used to store data without deviating from the scope of the invention.
Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.
The process steps disclosed herein, such as, e.g., the process steps performed in
The computer program can be implemented in hardware, software, or a hybrid implementation. The computer program can be composed of modules that are in operative communication with one another, and which are designed to pass information or instructions to display. The computer program can be configured to operate on a general purpose computer, an ASIC, or any other suitable device.
It will be readily understood that the components of various embodiments of the present invention, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present invention, as represented in the attached figures, is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention.
The features, structures, or characteristics of the invention described throughout this specification may be combined in any suitable manner in one or more embodiments. For example, reference throughout this specification to “certain embodiments,” “some embodiments,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in certain embodiments,” “in some embodiment,” “in other embodiments,” or similar language throughout this specification do not necessarily all refer to the same group of embodiments and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
It should be noted that reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present invention should be or are in any single embodiment of the invention. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, discussion of the features and advantages, and similar language, throughout this specification may, but do not necessarily, refer to the same embodiment.
Furthermore, the described features, advantages, and characteristics of the invention may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize that the invention can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the invention.
One having ordinary skill in the art will readily understand that the invention as discussed above may be practiced with steps in a different order, and/or with hardware elements in configurations which are different than those which are disclosed. Therefore, although the invention has been described based upon these preferred embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent, while remaining within the spirit and scope of the invention. In order to determine the metes and bounds of the invention, therefore, reference should be made to the appended claims.
