Process mining method, device, electronic device and medium based on graph database

Abstract

The embodiment of the present application discloses a process mining method, device, electronic device and medium based on a graph database. The method includes: obtaining an event log in a business process system, extracting a case identifier and case information for any event log, and storing them in a log layer corresponding to the case identifier in a first graph database; for a log layer corresponding to any case identifier, storing the activities in the case information stored in the log layer in chronological order to the activity layer corresponding to the case identifier in the first graph database; mining the process edge information contained in the log layer based on the case information in the log layer, and storing the process edge information in the edge layer corresponding to the case identifier in the first graph database. This mining and storage method is compatible with process edge information, the storage method of the graph database, and has lower expression complexity and higher visibility.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of process data mining, and in particular to a process mining method, device, electronic device and medium based on a graph database. Background Art

[0002] With the development of office digitization and networking, the process interactions involved in modern office processes are usually completed on the company's self-developed network platform. As the company continues to grow, the types and complexity of the business processes involved in the company will usually increase exponentially.

[0003] At present, traditional databases are usually used to store data related to business processes in the form of tables. Since business processes usually involve complex causal relationships and multiple executors, it is difficult to simply express these causal relationships and multiple executors in the form of table storage. The expression complexity is high, which will result in more computing power being consumed in analysis when querying a business process, and the query complexity is high. In addition, the table storage method has low visibility of the relationship between nodes in the business process. Summary of the invention

[0004] The embodiments of the present application provide a process mining method, device, electronic device and medium based on a graph database to reduce the expression complexity and query complexity of process storage and improve the visibility of process storage.

[0005] In a first aspect, an embodiment of the present application provides a process mining method based on a graph database, the method comprising:

[0006] Obtain an event log in the business process system, extract a case identifier and case information for any of the event logs, and store them in a log layer corresponding to the case identifier in the first graph database;

[0007] For the log layer corresponding to any of the case identifiers, storing the activities in the case information stored in the log layer in chronological order to the activity layer corresponding to the case identifier in the first graph database;

[0008] Based on the case information in the log layer, the process edge information contained in the log layer is mined, and the process edge information is stored in the edge layer corresponding to the case identifier in the first graph database.

[0009] In a second aspect, an embodiment of the present application provides a process mining device based on a graph database, the device comprising:

[0010] A first storage module is used to obtain event logs in the business process system, extract case identification and case information for any of the event logs, and store them in a log layer corresponding to the case identification in the first graph database;

[0011] A second storage module is used to store, for any log layer corresponding to the case identifier, the activities in the case information stored in the log layer in chronological order to the activity layer corresponding to the case identifier in the first graph database;

[0012] A storage mining module is used to mine the process edge information contained in the log layer based on the case information in the log layer, and store the process edge information in the edge layer corresponding to the case identifier in the first graph database.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0014] one or more processors;

[0015] a storage device for storing one or more programs,

[0016] When one or more programs are executed by one or more processors, the one or more processors implement a process mining method based on a graph database as provided in any embodiment of the present application.

[0017] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, a process mining method based on a graph database as provided in any embodiment of the present application is implemented.

[0018] The technical solution of the embodiment of the present application obtains the event log in the business process system, and for any of the event logs, extracts the case identification and case information, and stores them in the log layer corresponding to the case identification in the first graph database; for any of the log layers corresponding to the case identification, stores the activities in the case information stored in the log layer in chronological order to the activity layer corresponding to the case identification in the first graph database; mines the process edge information contained in the log layer based on the case information in the log layer, and stores the process edge information in the edge layer corresponding to the case identification in the first graph database. Based on this, by storing case information with the same case identification in the log layer, and using the activity layer and the edge layer to mine and store the active links in the case, the mining and storage of the process is achieved. This mining and storage method is compatible with the process edge information, and the storage method of the graph database has lower expression complexity and higher visibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1A schematic diagram of a process mining method based on a graph database provided in Example 1 of the present application;

[0020] Figure 2 A schematic diagram of the structure of a process mining device based on a graph database provided in Example 2 of the present application;

[0021] Figure 3 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present application;

[0022] Figure 4 A schematic diagram of case classification in the first graph database provided in Example 1 of the present application. DETAILED DESCRIPTION

[0023] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0024] Embodiment 1

[0025] Figure 1 A flowchart of a process mining method based on a graph database provided in Example 1 of the present application is shown in FIG. Figure 1 As shown, the process mining method based on the graph database provided in this embodiment can be applied to a process mining platform based on the graph database mounted on a device with data processing capability such as a computer, and can be used in conjunction with some work platform software to achieve a better experience. Specifically, the following steps may be included:

[0026] Step 101: Obtain the event log in the business process system. For any event log, extract the case identifier and case information, and store them in the log layer corresponding to the case identifier in the first graph database.

[0027] In this step, the business process system refers to any system involved in process interaction. In the business process system, each process node will be executed as an event, and a corresponding event log will be generated during the execution process.

[0028] For process nodes belonging to the same process, the same case identifier is usually stored in the event log generated each time it is executed. The case identifier is usually generated when the first process node of a process is executed. When subsequent process nodes are executed, the aforementioned case identifier is stored in the corresponding event log.

[0029] Based on the above execution basis, in this embodiment, the event logs in the business system can be periodically collected and acquired, and then case identification and case information can be extracted. In a specific example, the case information can include multiple key fields, such as resources, activities, start time, end time, organization, etc.

[0030] After the extraction is completed, it is queried whether there is a log layer corresponding to the case identifier in the first graph database. If so, the step of storing the log layer corresponding to the case identifier in the first graph database is performed; if not, a case classification corresponding to the case identifier is created, and a log layer, an activity layer, and an edge layer corresponding to the case identifier are created under the case classification. In addition, an activity computing layer can also be created under the activity layer.

[0031] Then the case identification and case information extracted above are stored in the log layer corresponding to the case identification in the first graph database. Figure 4 , Figure 4 A schematic diagram of case classification in the first graph database provided in Example 1 of the present application. Figure 4 On the left is a list view of the log layer, activity layer, edge layer, and case calculation layer established above, which has the function of a tab. By selecting each tab, the corresponding content of the tab is displayed on the right.

[0032] like Figure 4 As shown, it is the content of the log layer with the case identification (ie, case id) of 1234, which stores 4 pieces of case information. Among them, the resource column stores the executor of the activity, and the organization can be the department group to which the executor belongs.

[0033] The first graph database is used to store information of each case.

[0034] Step 102: For a log layer corresponding to any case identifier, the activities in the case information stored in the log layer are stored in chronological order in the activity layer corresponding to the case identifier in the first graph database.

[0035] In this step, the start time is stored for the activities in the aforementioned log layer. Therefore, this step can sort the activities according to their start time and then store them in the activity layer corresponding to the case identifier in order.

[0036] In addition, an activity calculation layer can be created under the activity layer. For any activity, the activity duration is determined based on the start time and end time stored in the corresponding case information; and the activity duration corresponding to each activity is stored in the activity calculation layer.

[0037] It should be noted that the time difference between the start time and the end time is the activity duration of the activity.

[0038] Step 103: mine the process edge information contained in the log layer based on the case information in the log layer, and store the process edge information in the edge layer corresponding to the case identifier in the first graph database.

[0039] In this step, process edge information refers to the flow relationship between activities. During specific mining, we can first determine the flow order between activities in the log layer based on the start time and end time in the case information; then determine the flow order between activities as the process edge information contained in the log layer.

[0040] With the aforementioned Figure 4 For example, the closest time to the end time of submitting the reimbursement work order is the start time of the review. Therefore, it can be determined that there is a flow relationship between submitting the reimbursement work order and the review. In chronological order, the activity of submitting the reimbursement work order flows to the activity of review.

[0041] By analogy, there is a flow relationship between review and processing, with review flowing to processing; there is a flow relationship between processing and reimbursement, with processing flowing to reimbursement.

[0042] The above flow order is stored in the edge layer corresponding to the case identifier in the first graph database. Based on the above flow relationship and flow direction, it can be determined that the active link in the process involved in the case is: submitting a reimbursement work order-review-processing-reimbursement.

[0043] In addition, after this step, an edge computing layer can be created in the edge layer. For any two activities with a flow relationship, the flow time between the activities is determined based on the start time and end time stored in the corresponding case information; the flow time between the activities is stored in the edge computing layer.

[0044] It should be noted that the flow time refers to the time spent between two activities with a flow relationship. In a specific example, taking the flow from A to B as an example, the time difference between the end time of A and the start time of B can be determined as the flow time between A and B.

[0045] In addition, when creating a case classification corresponding to a case identifier, a case calculation layer corresponding to the case identifier can be created under the case classification. Then, based on all case information stored in the activity layer corresponding to the case identifier, the case indicator data corresponding to the case identifier is determined; and the case indicator data is stored in the case calculation layer corresponding to the case identifier.

[0046] It should be noted that case indicator data may include the following indicators: number of activities, number of users, number of logs, total time consumed, number of organizations, and case identification.

[0047] Each of the above indicators has a corresponding calculation algorithm, which automatically calculates the results and stores them in the case calculation layer.

[0048] In this embodiment, a second graph database for storing processes may also be provided, and the second graph database may store a plurality of process categories.

[0049] Specifically, this embodiment can link the activity links with chronological sequence stored in the activity layer to the target vector library to generate a vector identifier corresponding to the activity link; then determine whether there is a process classification corresponding to the vector identifier in the second graph database; if so, map the process classification corresponding to the vector identifier with the case classification corresponding to the activity links with chronological sequence in the first graph database; if not, create a process classification corresponding to the vector identifier in the second graph database, and store the activity link in the process classification.

[0050] The target vector library is provided with a vector generator, and the corresponding vector identifier can be obtained by inputting the active link into the vector generator. It should be noted that for the same active link, the vector identifier generated by the vector generator is the same, because it has an unchanged generation rule, and when the input is the same, the output is also the same.

[0051] Since the aforementioned case information is stored in the first graph database, the processes involved may be the same, but the related resources are different. Therefore, the process classification may have been stored in the second graph database. At this time, you can generate a vector identifier, find the process classification corresponding to the vector identifier, and then map the process classification with the case identifier, so that users can query the corresponding case in the first graph database through the process in the second graph database.

[0052] In this embodiment, the event log in the business process system is obtained, and for any event log, the case identifier and case information are extracted and stored in the log layer corresponding to the case identifier in the first graph database; for the log layer corresponding to any case identifier, the activities in the case information stored in the log layer are stored in chronological order in the activity layer corresponding to the case identifier in the first graph database; based on the case information in the log layer, the process edge information contained in the log layer is mined, and the process edge information is stored in the edge layer corresponding to the case identifier in the first graph database. Based on this, by storing case information with the same case identifier in the log layer, and using the activity layer and the edge layer to mine and store the active links in the case, the mining and storage of the process is achieved. This mining and storage method is compatible with the process edge information, and the storage method of the graph database has lower expression complexity and higher visibility.

[0053] Embodiment 2

[0054] Figure 2This is a schematic diagram of the structure of a graph database-based process mining device provided in the second embodiment of the present application. The graph database-based process mining device provided in the embodiment of the present application can execute the graph database-based process mining method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. The device can be implemented in software and / or hardware, such as Figure 2 As shown, the process mining device based on the graph database specifically includes: a first storage module 201, a second storage module 202, and a storage mining module 203.

[0055] The first storage module is used to obtain event logs in the business process system, extract case identification and case information for any event log, and store them in the log layer corresponding to the case identification in the first graph database;

[0056] A second storage module is used to store, for a log layer corresponding to any case identifier, activities in the case information stored in the log layer in chronological order to an activity layer corresponding to the case identifier in the first graph database;

[0057] The storage mining module is used to mine the process edge information contained in the log layer based on the case information in the log layer, and store the process edge information in the edge layer corresponding to the case identifier in the first graph database.

[0058] Furthermore, the device is specifically used for:

[0059] Query whether there is a log layer corresponding to the case identifier in the first graph database, and if so, execute the step of storing in the log layer corresponding to the case identifier in the first graph database;

[0060] If it does not exist, create a case category corresponding to the case ID, and create a log layer, activity layer, and edge layer corresponding to the case ID under the case category.

[0061] Furthermore, the storage mining module is specifically used for:

[0062] Determine the flow order between activities in the log layer based on the start and end times in the case information;

[0063] The flow order between activities is determined as the process edge information contained in the log layer.

[0064] Furthermore, the device is specifically used for:

[0065] Create an activity calculation layer under the activity layer. For any activity, determine the activity duration based on the start time and end time stored in the corresponding case information.

[0066] The activity duration corresponding to each activity is stored in the activity calculation layer.

[0067] Furthermore, the device is specifically used for:

[0068] Create an edge computing layer at the edge layer. For any two activities with a flow relationship, determine the flow time between the activities based on the start time and end time stored in the corresponding case information.

[0069] The time consumption of the flow between activities is stored in the edge computing layer.

[0070] Furthermore, the device is specifically used for:

[0071] If it does not exist, create a case calculation layer corresponding to the case identifier under the case classification;

[0072] Determine case indicator data corresponding to the case identifier based on all case information stored in the activity layer corresponding to the case identifier;

[0073] The case indicator data is stored in the case calculation layer corresponding to the case identifier.

[0074] Furthermore, the device is specifically used for:

[0075] Link the time-ordered activity links stored in the activity layer to the target vector library to generate vector identifiers corresponding to the activity links;

[0076] Determine whether there is a process classification corresponding to the vector identifier in the second graph database;

[0077] If so, mapping the process classification corresponding to the vector identifier with the case classification corresponding to the activity link with the time sequence in the first graph database;

[0078] If it does not exist, create a process classification corresponding to the vector identifier in the second graph database, and store the active link in the process classification.

[0079] Embodiment 3

[0080] Figure 3 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present application is shown in FIG. Figure 3 As shown, the electronic device includes a processor 310, a memory 320, an input device 330 and an output device 340; the number of the processor 310 in the electronic device can be one or more. Figure 3 A processor 310 is taken as an example; the processor 310, the memory 320, the input device 330 and the output device 340 in the electronic device can be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0081] The memory 320, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the process mining method based on the graph database in the embodiment of the present invention. The processor 310 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 320, that is, to implement the above-mentioned process mining method based on the graph database:

[0082] Obtaining event logs in the business process system, extracting case identifiers and case information for any event log, and storing them in the log layer corresponding to the case identifier in the first graph database;

[0083] For a log layer corresponding to any case identifier, the activities in the case information stored in the log layer are stored in chronological order in the activity layer corresponding to the case identifier in the first graph database;

[0084] Based on the case information in the log layer, the process edge information contained in the log layer is mined, and the process edge information is stored in the edge layer corresponding to the case identifier in the first graph database.

[0085] Furthermore, after extracting the case identification and case information, the method further includes:

[0086] Query whether there is a log layer corresponding to the case identifier in the first graph database, and if so, execute the step of storing in the log layer corresponding to the case identifier in the first graph database;

[0087] If it does not exist, create a case category corresponding to the case ID, and create a log layer, activity layer, and edge layer corresponding to the case ID under the case category.

[0088] Furthermore, based on the case information in the log layer, the process edge information contained in the log layer is mined, including:

[0089] Determine the flow order between activities in the log layer based on the start and end times in the case information;

[0090] The flow order between activities is determined as the process edge information contained in the log layer.

[0091] Furthermore, after storing the activities in the case information stored in the log layer in chronological order in the activity layer corresponding to the case identifier in the first graph database, the method further includes:

[0092] Create an activity calculation layer under the activity layer. For any activity, determine the activity duration based on the start time and end time stored in the corresponding case information.

[0093] The activity duration corresponding to each activity is stored in the activity calculation layer.

[0094] Furthermore, after storing the process edge information in the edge layer corresponding to the case identifier in the first graph database, the method further includes:

[0095] Create an edge computing layer at the edge layer. For any two activities with a flow relationship, determine the flow time between the activities based on the start time and end time stored in the corresponding case information.

[0096] The time consumption of the flow between activities is stored in the edge computing layer.

[0097] Furthermore, the method also includes:

[0098] If it does not exist, create a case calculation layer corresponding to the case identifier under the case classification;

[0099] Determine case indicator data corresponding to the case identifier based on all case information stored in the activity layer corresponding to the case identifier;

[0100] The case indicator data is stored in the case calculation layer corresponding to the case identifier.

[0101] Furthermore, the method also includes:

[0102] Link the time-ordered activity links stored in the activity layer to the target vector library to generate vector identifiers corresponding to the activity links;

[0103] Determine whether there is a process classification corresponding to the vector identifier in the second graph database;

[0104] If so, mapping the process classification corresponding to the vector identifier with the case classification corresponding to the activity link with the time sequence in the first graph database;

[0105] If it does not exist, create a process classification corresponding to the vector identifier in the second graph database, and store the active link in the process classification.

[0106] The memory 320 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 320 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 320 may further include a memory remotely arranged relative to the processor 310, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0107] Embodiment 4

[0108] Embodiment 4 of the present application further provides a storage medium containing computer executable instructions, and when the computer executable instructions are executed by a computer processor, they are used to execute a process mining method based on a graph database, and the method includes:

[0109] Obtaining event logs in the business process system, extracting case identifiers and case information for any event log, and storing them in the log layer corresponding to the case identifier in the first graph database;

[0110] For a log layer corresponding to any case identifier, the activities in the case information stored in the log layer are stored in chronological order in the activity layer corresponding to the case identifier in the first graph database;

[0111] Based on the case information in the log layer, the process edge information contained in the log layer is mined, and the process edge information is stored in the edge layer corresponding to the case identifier in the first graph database.

[0112] Furthermore, after extracting the case identification and case information, the method further includes:

[0113] Query whether there is a log layer corresponding to the case identifier in the first graph database, and if so, execute the step of storing in the log layer corresponding to the case identifier in the first graph database;

[0114] If it does not exist, create a case category corresponding to the case ID, and create a log layer, activity layer, and edge layer corresponding to the case ID under the case category.

[0115] Furthermore, based on the case information in the log layer, the process edge information contained in the log layer is mined, including:

[0116] Determine the flow order between activities in the log layer based on the start and end times in the case information;

[0117] The flow order between activities is determined as the process edge information contained in the log layer.

[0118] Furthermore, after storing the activities in the case information stored in the log layer in chronological order in the activity layer corresponding to the case identifier in the first graph database, the method further includes:

[0119] Create an activity calculation layer under the activity layer. For any activity, determine the activity duration based on the start time and end time stored in the corresponding case information.

[0120] The activity duration corresponding to each activity is stored in the activity calculation layer.

[0121] Furthermore, after storing the process edge information in the edge layer corresponding to the case identifier in the first graph database, the method further includes:

[0122] Create an edge computing layer at the edge layer. For any two activities with a flow relationship, determine the flow time between the activities based on the start time and end time stored in the corresponding case information.

[0123] The time consumption of the flow between activities is stored in the edge computing layer.

[0124] Furthermore, the method also includes:

[0125] If it does not exist, create a case calculation layer corresponding to the case identifier under the case classification;

[0126] Determine case indicator data corresponding to the case identifier based on all case information stored in the activity layer corresponding to the case identifier;

[0127] The case indicator data is stored in the case calculation layer corresponding to the case identifier.

[0128] Furthermore, the method also includes:

[0129] Link the time-ordered activity links stored in the activity layer to the target vector library to generate vector identifiers corresponding to the activity links;

[0130] Determine whether there is a process classification corresponding to the vector identifier in the second graph database;

[0131] If so, mapping the process classification corresponding to the vector identifier with the case classification corresponding to the activity link with the time sequence in the first graph database;

[0132] If it does not exist, create a process classification corresponding to the vector identifier in the second graph database, and store the active link in the process classification.

[0133] Of course, the storage medium containing computer executable instructions provided in the embodiment of the present application, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the graph database-based process mining method provided in any embodiment of the present application.

[0134] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present application can be implemented with the help of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0135] It is worth noting that in the embodiment of the above-mentioned search device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0136] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A process mining method based on graph database, characterized in that: The method comprises: Obtain an event log in the business process system, extract a case identifier and case information for any of the event logs, and store them in a log layer corresponding to the case identifier in the first graph database; For the log layer corresponding to any of the case identifiers, storing the activities in the case information stored in the log layer in chronological order to the activity layer corresponding to the case identifier in the first graph database; Mining process edge information contained in the log layer based on the case information in the log layer, and storing the process edge information in the edge layer corresponding to the case identifier in the first graph database; Wherein, mining the process edge information contained in the log layer based on the case information in the log layer includes: Determine the flow order between the activities in the log layer according to the start time and end time in the case information; The flow sequence between the activities is determined as the process edge information contained in the log layer; wherein the process edge information refers to the flow information between the activities.

2. The method according to claim 1, characterized in that After extracting the case identification and case information, the method further includes: Query whether there is a log layer corresponding to the case identifier in the first graph database, and if so, execute the step of storing in the log layer corresponding to the case identifier in the first graph database; If it does not exist, create a case classification corresponding to the case identification, and create a log layer, an activity layer, and an edge layer corresponding to the case identification under the case classification.

3. The method according to claim 1, characterized in that After storing the activities in the case information stored in the log layer in chronological order in the activity layer corresponding to the case identifier in the first graph database, the method further includes: An activity calculation layer is created under the activity layer, and for any activity, the activity duration of the activity is determined according to the start time and end time stored in the corresponding case information; The activity duration corresponding to each of the activities is stored in the activity calculation layer.

4. The method according to claim 1, characterized in that: After storing the process edge information in the edge layer corresponding to the case identifier in the first graph database, the method further includes: An edge computing layer is created at the edge layer, and for any two activities having a flow relationship, a flow consumption time of the flow between the activities is determined according to the start time and the end time stored in the corresponding case information; The flow time consumed by the flow between activities is stored in the edge computing layer.

5. The method according to claim 2, characterized in that: The method further comprises: If it does not exist, create a case calculation layer corresponding to the case identifier under the case classification; Determine the case indicator data corresponding to the case identifier according to all case information stored in the activity layer corresponding to the case identifier; wherein the case indicator data includes: the number of activities, the number of users, the number of logs, the total time consumed, the number of organizations and the case identifier; The case indicator data is stored in the case calculation layer corresponding to the case identifier.

6. The method according to claim 1, characterized in that The method further comprises: Linking the activity links in time sequence stored in the activity layer to the target vector library to generate vector identifiers corresponding to the activity links; Determine whether there is a process classification corresponding to the vector identifier in the second graph database; If so, mapping the process classification corresponding to the vector identifier with the case classification corresponding to the activity link with the time sequence in the first graph database; If not, create a process classification corresponding to the vector identifier in the second graph database, and store the active link in the process classification.

7. A process mining device based on a graph database, characterized in that: The device comprises: A first storage module is used to obtain event logs in the business process system, extract case identification and case information for any of the event logs, and store them in a log layer corresponding to the case identification in the first graph database; A second storage module is used to store, for any log layer corresponding to the case identifier, the activities in the case information stored in the log layer in chronological order to the activity layer corresponding to the case identifier in the first graph database; A storage mining module, configured to mine process edge information contained in the log layer based on the case information in the log layer, and store the process edge information in an edge layer corresponding to the case identifier in the first graph database; Wherein, the storage mining module is specifically used for: Determine the flow order between the activities in the log layer according to the start time and end time in the case information; The flow sequence between the activities is determined as the process edge information contained in the log layer; wherein the process edge information refers to the flow information between the activities.

8. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the process mining method based on the graph database as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a process mining method based on a graph database as described in any one of claims 1 to 6 is implemented.

Images