JP7699953B2 - Business process estimation device and method - Google Patents

Description

The present invention relates to a business process estimation device and method. For example, the present invention relates to a technology for analyzing the order in which various tasks are performed in various types of tasks (hereinafter, may be simply referred to as a "business process"). It also relates to a technology for acquiring the execution history of a task (for example, represented in the format of an event log described in this specification) and estimating the business process from this execution history.

DX (Digital Transformation), typified by the introduction of "RPA (Robotic Process Automation)," has been attracting attention as an effort to improve business efficiency and productivity. RPA is software that uses AI and machine learning to take over or substitute for tasks that have previously been handled only by humans, or more advanced tasks. DX is when companies use digital technology to transform their organizations and business models. In such DX efforts, business processes have been visualized, for example as shown in Patent Document 1, in order to identify inefficient tasks that need to be improved or transformed.

However, identifying these business processes requires a great deal of time and effort, as it requires experts with business analysis skills to observe the organization where the work is carried out, interview those in charge of the work, read and understand business manuals and standards, manually transcribe the contents, and then repeatedly check and revise the transcripts with various people involved in the work. In addition, depending on the level of understanding of the work of those interviewed, infrequent exceptional work may be overlooked. Furthermore, the more advanced the work, the more rapidly the way the work is carried out continues to change. For this reason, it is not enough to simply identify the business processes at a certain point in time; it is necessary to keep up with the changes.

In recent years, therefore, process mining has been attracting attention as a technology for automating the identification of business processes. This is a technology/method for estimating tasks being performed in a business and the order in which they are performed (business process) from logs (operation history = event logs) in IT systems used to proceed with the business, as shown in, for example, Patent Document 2 and Non-Patent Document 1. A task is an element that constitutes a business process. For example, in a procurement business process, there are tasks such as "create an order specification" and "check inventory," while in a design business process, there are tasks such as "design the shape of part XX," "evaluate the strength of part XX," and "create materials for design review." This process mining technology can automate or reduce the labor required for business analysis that previously required experts to perform, and by continuing to use it, changes in business processes can be captured.

JP 2003-150650 A JP 2020-052451 A

Wil van der Aalst, "Process Mining: Data Science in Action", Impress, September 2019

However, the above technology has the following issues:

In Patent Document 1, the identification of business processes must be performed by an expert. In the techniques described in Patent Document 2 and Non-Patent Document 1, the IT system must be able to generate and acquire event logs corresponding to all tasks that make up a business process.

For example, a system such as a travel and expense settlement system or an order management system for ordering based on inventory information must have the functionality to perform all of the tasks and be able to record them as an event log. This is because if the entire business process is not recorded in the event log, the information will be fragmented.

In addition, there must be a one-to-one correspondence between the functions of an IT system and the tasks that make up a business process. For example, tasks such as "checking specifications," "creating a quote," and "approving the quote" in order placement operations are implemented as functions of the order placement system, and there is a one-to-one correspondence. On the other hand, in hardware product design operations, tasks such as "designing the shape of the casing parts," "designing the shape of the motor parts," and "designing the layout of the parts placement for the entire product" use a CAD system, but there is no one-to-one correspondence between the functions of that CAD system and the tasks. Similarly, spreadsheet software has many functions, but these are used to perform tasks such as "evaluating the strength of the casing parts" and "estimating the cost of the motor parts," and in this case too, there is no one-to-one correspondence between functions and tasks.

For this reason, there is an issue that it cannot be applied to tasks that do not correspond to the tasks that make up the business process and the functions of the IT system. In routine tasks, as IT systems have been introduced and streamlined, there is often a correspondence between tasks and functions. On the other hand, in non-routine tasks such as product planning, structural design, and production preparation, the functions of the IT system do not correspond one-to-one with all tasks, so many tasks are missed from the event log, and as a result, even if process mining is performed, the estimated business process is incomplete.

The present invention was made in consideration of the above circumstances, and aims to enable more accurate estimation of business processes.

An example of a business process inference device according to the present invention includes:
A specimen database for storing a plurality of specimen materials;
a sample material-task association database that associates, with each of the sample materials, a task expression that represents a task;
a similarity evaluation unit that, for each of the intermediate products generated in relation to any of the sample materials, determines one of the sample materials that is most similar to the intermediate product as a similar sample material;
an access log acquisition unit that acquires an access log indicating the date and time of access to each of the intermediate products;
a related task acquisition unit that acquires, for each of the intermediate products, a task expression associated with the corresponding similar sample material as a related task expression;
a business process estimation unit that estimates a business process expression that indicates an order between the task expressions based on the access log and the related task expressions;
Equipped with.

An example of a business process estimation method according to the present invention includes:
A business process estimation method executed by a computer, comprising the steps of:
A step of associating a task expression representing a task with each of a plurality of sample materials; a step of determining, for each intermediate product generated in association with any of the sample materials, one of the sample materials that is most similar to the intermediate product as a similar sample material;
acquiring an access log indicating the date and time of access to each of the intermediate products;
obtaining a task expression associated with the corresponding similar sample material as an associated task expression for each of the intermediate products;
estimating a business process representation representing an order between each of the task representations based on the access log and the related task representations;
Equipped with.

The business process estimation device and method according to the present invention enable more accurate estimation of business processes.

For example, even if there is no one-to-one correspondence between the tasks that make up a business process and the functions of an IT system, event logs can be generated automatically, expanding the scope of applicability of process mining technology. This makes it possible to more accurately automate or reduce the labor required for business analysis that previously required the work of experts.

In addition, by continuing to use the business process estimation device and method according to the present invention, changes in the business process can also be captured.

FIG. 2 is a diagram illustrating an example of hardware and a functional flow of a business process estimation device. FIG. 2 is an example of a functional flow diagram of a business process estimation device. 2 is an example of a table structure and data of the specimen material database 101. 1 is an example of a table structure and data of a specimen material-task relation database 102. 1 is an example of a table structure and data of an event log. FIG. 11 is an explanatory diagram showing an example of a method of a business process estimation unit 110. 1 is an example of an intermediate product file 103. This is an example of a similar specimen material 104. 1 is an example of business process data.

Below, an example of a business process estimation device of the present invention is explained using the drawings.

FIG. 1 is a configuration diagram showing one embodiment of a business process estimation device 10 according to the present invention. FIG. 1(a) shows an example of a hardware configuration, and FIG. 1(b) shows an example of a functional flow. The business process estimation device 10 implements the business process estimation method described in this embodiment.

As shown in FIG. 1(a), the business process estimation device 10 can be configured using a computer having a known hardware configuration, and includes, for example, a calculation means 11 and a storage means 12. The calculation means 11 includes, for example, a processor, and the storage means 12 includes, for example, a storage medium such as a semiconductor memory device and a magnetic disk device. A part or all of the storage medium may be a non-transitory storage medium.

The computer may also include input/output means. The input/output means may include, for example, input devices such as a keyboard and a mouse, output devices such as a display and a printer (for example, output device 100, which will be described later in relation to FIG. 1(b)), and communication devices such as a network interface.

The storage means may store a program. The processor may execute the program, causing the computer to perform the functions described in this embodiment.

The business process estimation device 10 shown in FIG. 1(a) has the functions represented by the functional blocks shown in FIG. 1(b). For example, the business process estimation device 10 has a sample material database 101 that stores multiple sample materials used in business, and a sample material-task association database 102 that associates an expression representing a task (task expression) with each of the sample materials. A task corresponds to or is related to the work of creating an intermediate deliverable.

The business process estimation device 10 also includes a document similarity evaluation unit 105 (similarity evaluation unit) that searches for the most similar sample material to an intermediate product file 103 created as an intermediate product during business execution and registers it as a similar sample material 104, an access log acquisition unit 106 that acquires an access log for the intermediate product file 103, a related task acquisition unit 107 that acquires a task expression related to the similar sample material 104 from the sample material-task related database 102, an event log generation unit 108 that creates an event log from the intermediate product access log and the related task expression, a business process estimation unit 110 that estimates a business process from the event log and estimates business process data 109, and a business process visualization unit 111 that outputs (for example, displays) the business process data 109 to the output device 100.

In one variant, specimen materials can be generated from multiple past intermediate products and automatically added to the specimen material database 101.

Figure 2 is a functional flow diagram of a business process estimation device 20 having a sample data automatic generation function relating to such a modified example. The business process estimation device 20 includes an intermediate product similarity calculation unit 201 that calculates the mutual similarity of multiple intermediate product files 103 created in past business operations, and an intermediate product clustering unit 202 that clusters the intermediate products based on the similarity and adds a representative intermediate product for each cluster to the sample data database 101.

Below, we will explain examples of each means and database according to Example 1 or its modified example.

(1) Specimen Material Database 101 and Intermediate Product File 103
The specimen material database 101 is a database that stores specimen materials among materials used in business. Specimen materials are, for example, those that express the characteristics of the content in intermediate deliverables (based on the definition of a specimen in taxonomy, a field of biology, as "evidence that proves its existence and clarifies its characteristics"). Specimen materials are, for example, in the format of data files handled by computers. However, specimen materials are not limited to materials that fit this definition, and materials of any content and format can be specimen materials.

The intermediate product file 103 is a database that stores intermediate products generated in relation to any of the specimen materials. Examples of intermediate products from the development and design stages include reports, specifications, study documents, technical documents, drawings, etc. More specific examples include functional specifications, experiment reports, technical documents listing anticipated issues and proposed countermeasures, etc. In addition, in the case of data related to calculations of strength, heat load, cost, etc., it is a file such as a spreadsheet or simulation software created in a past project. It may also be a file containing 2D drawings or 3D CAD data showing the shape and structure, part assembly information, various attribute information (material, part type, welding instructions, etc.), etc.

Other examples of intermediate deliverables at the production stage include mold data, manufacturing process charts, assembly process charts, quality plans, inspection records, and cost estimates. Anything created during the course of a business, such as program code, meeting minutes, test data, and operation manuals, can also be considered intermediate deliverables.

In business, when creating these intermediate products, they are rarely created from scratch, but are generally created based on similar past intermediate products or pre-prepared template materials or sample materials. These base past intermediate products, template materials, and sample materials are preferably used as sample materials. A collection of such sample materials can be used as the sample materials database 101.

An example of the table structure of the specimen material database 101 is shown in Figure 3. In this example, it has four fields: ID, specimen material name, storage address of the file that is the specimen material entity, and file type.

(2) Specimen data-task related database 102
The sample material-task relation database 102 is a database in which the relation of tasks for creating intermediate deliverables based on sample materials registered in the sample material database 101 is registered. For example, a task expression "perform strength analysis" can be related to a sample material called a "strength analysis result report," and a task expression "estimate assembly cost" can be related to a sample material called an "assembly cost estimate."

This task representation is used, for example, to display in the business process representation (Figure 9, etc.) described below. There are no particular restrictions on the specific representation, and it may be any representation that allows a user viewing the business process representation to identify the task. However, it is preferable to use a task name represented by text data as shown in the figure, as this simplifies processing.

An example of the table structure and data of the specimen material-task relation database 102 is shown in Figure 4. In this example, it has three fields: ID, specimen material name, and task name (task expression).

(3) Document Similarity Evaluation Unit 105
The document similarity evaluation unit 105 searches the sample material database 101 for the most similar sample material for the intermediate product file 103 created during the execution of the business, and registers it as a similar sample material 104. That is, for each intermediate product, one sample material that is most similar to the intermediate product among the sample materials is determined as a similar sample material and associated with the intermediate product.

The index of similarity can be defined appropriately by those skilled in the art. Known similarity evaluation techniques may be used. For example, if the intermediate product and the sample material are documents, each document can be vectorized in some way, the similarity between the vectors can be calculated, and the sample material with the highest similarity can be set as the similar sample material 104. Various methods have been proposed for vectorizing documents and calculating similarity, but any method can be used. It is not important as long as it is possible to simply calculate the similarity between documents.

Note that intermediate products and specimen materials do not have to be documents, and can be, for example, image data, spreadsheet data, or 3D shape data. As with documents, various methods have been proposed for vectorizing images and 3D data and calculating the similarity between vectors, and various types of material content can be used.

An example of a method for vectorization and similarity calculation in a text document is shown below. The text document is divided into words using morphological analysis. Note that this morphological analysis may be omitted in languages such as English where words are separated by spaces. Next, the number of occurrences of each word is counted and expressed as a vector. Cosine similarity is calculated for the vectors created for the two documents (Reference: Hidetsugu Namba, "Measuring Similarity Between Texts," Information Science and Technology, 2020, Vol. 70, No. 7, pp. 373-375). In this example, similarity is calculated using the frequency of occurrence of words, but another method for evaluating the importance of words called TF-IDF may also be used (Reference: Akinobu Kageyama, Hiroshi Tsuji, "Feature Extraction Method for Research Institutions Using the TF/IDF Algorithm," Institute of Electrical Engineers of Japan Transactions on Electronics, Information and Systems, 2005, Vol. 125, No. 5, pp. 713-719).

Similarly, an example of a method for calculating similarity in 3D shape data is shown below. The target shape is represented as a voxel structure, blocks are defined by adjacent voxels, a spectrum is calculated for each block, and the spectrum is represented as a vector (Reference: Patent No. 5024767). As in the case of documents, cosine similarity is calculated for the vectors created for the two 3D shape data.

(4) Access log acquisition unit 106
The access log acquiring unit 106 acquires the access log of the intermediate product file 103. In this embodiment, the access log indicates the date and time of access to each intermediate product, for example, access for creating and access for editing (updating). The date and time of access is indicated by, for example, a time stamp.
(5) Related task acquisition unit 107
The related task acquisition unit 107 acquires task expressions related to the similar sample materials 104 from the sample material-task relation database 102. In particular, for each intermediate product, a task expression associated with the corresponding similar sample material is acquired as a related task expression for that intermediate product.

In the example sample data-task relation database 102 shown in FIG. 4, if a sample data with the document name "operating condition definition document" is registered as a similar sample data 104 for a certain intermediate product, the related task expression related to that intermediate product will be "determine operating conditions."

(6) Event log generator 108
The event log generator 108 creates an event log based on the access log of the intermediate deliverable and the related task expression. The event log has, for example, the table structure and data shown in Fig. 5. In this example, the table structure has five fields: ID, case name, task name (related task expression), timestamp, and person in charge (access executor).

In this example, the case name is automatically determined for each intermediate deliverable. For example, if the storage location (folder name, etc.) of the intermediate deliverable includes the case name according to a predetermined naming rule, the event log generation unit 108 can obtain the case name based on the storage location of the intermediate deliverable. Also, if there is data that associates a person in charge with a case name (for example, data that clearly indicates the case name for each person in charge and the time when they will be working on that case), the event log generation unit 108 can obtain the case name based on that data. The method of obtaining the case name can be designed arbitrarily. The business process estimation device 10 or 20 may have a separate functional means for accepting or automatically identifying the case name.

As a more specific example of the operation, the event log generation unit 108 acquires the case name for each intermediate product, and generates an event log based on the case name, the access log, and the related task expression. As shown in FIG. 5, the event log associates the case name, the date and time of access, and the task expression for the intermediate product.

(7) Business Process Estimation Unit 110 and Business Process Data 109
The business process estimation unit 110 estimates a business process expression (for example, FIG. 9 described later) based on the event log. The business process expression is an expression based on a model that expresses the business process in a specific expression format, and is an expression that expresses the order between task expressions. The type of expression format can be designed arbitrarily. The business process data 109 is concrete data that corresponds to the business process expression.

As described above, since the event log is generated based on the access log and the related task expression, the business process estimation unit 110 can also be said to estimate the business process expression based on the access log and the related task expression.

The method for inferring business processes from event logs can be designed arbitrarily, and publicly known methods may be used. An example of a typical method is described below.

[First step]
For example, for each item, the associated task expressions for each intermediate deliverable are arranged in the order in which the intermediate deliverables were accessed.

As a concrete example, consider the case where the following accesses were made. The alphabets are expressions of related tasks for the intermediate deliverables that were accessed. The expression (A, B, C) indicates that the accesses were made in the order of A → B → C.
Case 1: (A, B, C, D)
Case 2: (A, C, D)
Case 3: (A, E, C, D)
Case 4: (A, B, C, E, C, D)

[Second step]
Extract task transitions for all cases.

For example, looking at only Case 1, the business process representation can be expressed as a task transition diagram as shown in 601 in Figure 6. Next, by adding the transition pattern of Case 2, the order in which A appears after C is added, so it can be expressed as shown in 602 in Figure 6. Similarly, by adding the transition pattern of Case 3, the order in which A appears after E and the order in which E appears after C are added, so it can be expressed as shown in 603 in Figure 6. Furthermore, by adding the transition pattern of Case 4, the order in which C appears after E is added, so it can be expressed as shown in 604 in Figure 6.

In this way, the model expressing the task transition is registered as a business process representation in the business process data 109. The model (representation format) expressing the task transition can be designed arbitrarily, and publicly known technology may be used. For example, BPMN (Business Process Model and Notification), Petri nets, UML activity diagrams, etc. have been proposed, and it is advisable to register in an appropriate format.

(8) Intermediate product similarity calculation unit 201
The intermediate product similarity calculation unit 201 calculates the mutual similarity between a plurality of intermediate product files 103 created in past tasks. Here, the method of calculating the mutual similarity may be the same as that of the document similarity evaluation unit 105, for example.

(9) Intermediate Product Clustering Unit 202
The intermediate product clustering unit 202 clusters the intermediate products based on the similarity calculated by the intermediate product similarity calculation unit 201, and determines a representative intermediate product (representative intermediate product) for each cluster. Then, the representative intermediate product is stored in the sample material database 101 as a sample material.

There are no particular limitations on the clustering method, but it is possible to adopt, for example, the K-means method, the shortest distance method, the multidimensional scaling method, the Ward method, etc.

In this way, according to the modified example (FIG. 2) including the intermediate deliverable similarity calculation unit 201 and the intermediate deliverable clustering unit 202, the sample material is automatically added, so that the effort of adding the sample material can be eliminated. Note that the task name (task expression) corresponding to the automatically added sample material may be configured to be input by a human being to the business process estimation device 20 at the appropriate time, or may be configured to be automatically determined by the business process estimation device 20 based on the contents of the sample material.

Next, an example of the processing procedure and data flow using this device will be described with reference to Figures 7 to 9. The processing procedure proceeds, for example, according to the functional flow diagrams of Figures 1 and 2.

In this example, the data shown in FIG. 3 is registered in the specimen material database 101, the data shown in FIG. 4 is registered in the specimen material-task relation database 102, and the data shown in FIG. 7 is registered as the intermediate product file 103.

First, the document similarity evaluation unit 105 searches the specimen database 101 for the specimen material most similar to the intermediate product file 103, and registers it as the similar specimen material 104. In this example, it is assumed that the similar specimen material 104 shown in FIG. 8 has been determined for the intermediate product file 103 shown in FIG. 7.

Then, the access log acquisition unit 106 acquires the access log of the intermediate product file 103. Then, the related task acquisition unit 107 acquires the task expression related to the similar specimen material 104 from the specimen material-task relation database 102. Then, the event log generation unit 108 creates an event log from the intermediate product access log and the related task. In this example, the event log shown in FIG. 5 is created.

For this event log, the business process estimation unit 110 estimates the business process and registers it as business process data 109. As a result, the business process expression shown in FIG. 9 is estimated. The business process visualization unit 111 visualizes the business process expression (for example, by displaying it on a display device).

As an example, let's look at the task expression "select equipment configuration." In the event log in Figure 5, there is an access with an ID of 2 that is related to the project name "XX Company," and the next access related to the same project name "XX Company" is an access with an ID of 4 that is "Create a new design." Also, there is an access with an ID of 5 that is related to a different project name "△△ Company," and the next access related to the same project name "△△ Company" is an access with an ID of 8 that is "Create a new design." Furthermore, there is an access with an ID of 14 that is related to a different project name "□□ Company," and the next access related to the same project name "□□ Company" is an access with an ID of 15 that is "Perform a design review."

To summarize, after "select device configuration" comes either "create a new design" or "conduct a design review." This process is performed for all access logs, and the business process representation shown in Figure 9 is generated.

In this embodiment, in the business process representation, task representations are represented in relation to nodes. In the example of FIG. 9, task representations are written inside rectangular nodes. Also, in this embodiment, the order between task representations is represented by links connecting the nodes. In the example of FIG. 9, the order between nodes is represented by links with arrows. Using such a representation makes it easier to visualize the flow of tasks.

In the example of Figure 9, both the nodes and links are represented as images, but it is also possible to represent the nodes and links as numbers or in other data formats.

In the example of Figure 9, when there are multiple input links or multiple output links to the same node, these are integrated via diamond-shaped marks, but this integration process is not essential.

In this way, in the business process estimation device 10 or 20 according to this embodiment, when there are multiple accesses to one or more intermediate deliverables related to the same case name in the event log, the business process estimation unit 110 can estimate the order between task expressions based on the order between the multiple accesses.

In this way, in this embodiment, case names are used, so even if multiple cases are progressing simultaneously, it is possible to distinguish between the cases and process them without confusing the business processes of different cases. However, the use of case names is not essential, and in one modified example, the use of case names can be omitted.

In particular, the business process estimation unit 110 can extract pairs of two adjacent accesses to one or more intermediate deliverables related to the same case name in the event log, and estimate the order between task expressions based on the order of access in each pair.

In this way, by estimating the order for pairs of accesses consisting of only two accesses, the structure of the business process representation can be simplified and visibility can be improved. However, in one variant, it is also possible to estimate the order between task representations based on pairs that include three or more consecutive accesses.

In this embodiment, as shown in FIG. 9, the business process representation that is finally generated does not include the case name (such as "XX Company"). This allows the business process representation to be highly versatile and not limited to a specific case. However, in one modified example, the case name can also be included.

As described above, the business process estimation device 10 or 20 according to the present embodiment can estimate business processes more accurately. For example, even if the tasks constituting a business process do not correspond to the functions of an IT system, an event log can be automatically generated, expanding the scope of applicability of process mining. This makes it possible to automate or reduce the labor required for business analysis that previously required the assistance of experts, and by continuing to use the device, changes in business processes can be captured.

10, 20... Business process estimation device 100... Output device 101... Sample data database 102... Sample data-task related database 103... Intermediate product file 104... Similar sample data 105... Document similarity evaluation unit (similarity evaluation unit)
Reference Signs List 106: Access log acquisition unit 107: Related task acquisition unit 108: Event log generation unit 109: Business process data 110: Business process estimation unit 111: Business process visualization unit 201: Intermediate product similarity calculation unit 202: Intermediate product clustering unit

Claims (6)

A specimen database for storing a plurality of specimen materials;
a sample material-task association database for storing data associating a task expression representing a task with each of the sample materials;
a similarity evaluation unit that, for each of the intermediate products generated in relation to any of the sample materials, determines one of the sample materials that is most similar to the intermediate product as a similar sample material;
an access log acquisition unit that acquires an access log indicating the date and time of access to each of the intermediate products;
a related task acquisition unit that acquires, for each of the intermediate products, a task expression associated with the corresponding similar sample material as a related task expression;
a business process estimation unit that estimates a business process expression that indicates an order between the task expressions according to the access dates and times of the accesses to the intermediate deliverables described in the access log;
an event log generating unit that acquires a case name for each of the intermediate deliverables, and generates an event log that associates the case name, the date and time of the access, and the task expression based on the case name, the access log, and the related task expression;
Equipped with
the business process inference unit infers an order of the task expressions for each of the case names according to the date and time of the access for each of the case names described in the event log, thereby inferring the business process expressions;
Business process estimation device. 2. The business process estimation device according to claim 1,
In the business process representation, the task representations are represented by nodes, and an order between the task representations is represented by links connecting the nodes. 2. The business process estimation device according to claim 1 ,
the business process estimation unit, when there are multiple accesses to one or more of the intermediate deliverables related to the same case name in the event log, estimates that the tasks corresponding to the intermediate deliverables have been accessed in accordance with the order of the multiple accesses, thereby estimating an order between the task expressions. 4. The business process inference device according to claim 3 , wherein the business process inference unit extracts, from the event log, pairs of two adjacent accesses to one or more of the intermediate deliverables related to a same case name, and infers an order between the task expressions based on an order of access in each pair. 2. The business process estimation device according to claim 1 ,
The business process expression does not include the case name. A business process estimation method executed by a computer, comprising the steps of:
A step of associating a task expression representing a task with each of a plurality of sample materials; a step of determining, for each intermediate product generated in association with any of the sample materials, one of the sample materials that is most similar to the intermediate product as a similar sample material;
acquiring an access log indicating the date and time of access to each of the intermediate products;
obtaining a task expression associated with the corresponding similar sample material as an associated task expression for each of the intermediate products;
estimating a business process representation that indicates an order between the task representations according to the access dates and times of the accesses to the intermediate deliverables described in the access log;
acquiring a case name for each of the intermediate deliverables, and generating an event log that associates the case name, the date and time of the access, and the task expression based on the case name, the access log, and the related task expression;
Equipped with
In the step of estimating the business process expression, an order of the task expressions for each of the case names is estimated according to the date and time of the access for each of the case names described in the event log, thereby estimating the business process expression.
Business process estimation method.

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