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WO2025058122A1 - Système et procédé de génération de journal d'événements multi-perspective - Google Patents

Système et procédé de génération de journal d'événements multi-perspective Download PDF

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Publication number
WO2025058122A1
WO2025058122A1 PCT/KR2023/016843 KR2023016843W WO2025058122A1 WO 2025058122 A1 WO2025058122 A1 WO 2025058122A1 KR 2023016843 W KR2023016843 W KR 2023016843W WO 2025058122 A1 WO2025058122 A1 WO 2025058122A1
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event
data
concept
quality
sample
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Korean (ko)
Inventor
이상화
원석래
아스리아나 수트리스노와티리스카
레비세이 풀샤시아이큐
프라타마프란스
김운재
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Iochord Inc
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Iochord Inc
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models

Definitions

  • the following embodiments relate to systems and methods for generating logs with multi-view events.
  • Process mining analysis uses as input an event log, which consists of a series of events that can be grouped in a specific way to represent different aspects of a business process.
  • event logs are not always readily available in most real-world situations. In most cases, event logs must be extracted from a data source (e.g., an information system or database) and converted into a specific structure and format that process mining can interpret.
  • a data source e.g., an information system or database
  • Event logs are usually bound to specific process analysis objectives, i.e., business perspectives and perspectives themselves can be correlated with entities in the data.
  • the present invention aims to provide a system and method for generating a multi-view event log.
  • a method for generating a multi-view event log may include the steps of: receiving a data source; extracting metadata from the data source; generating an event map and a sample event log using the metadata; calculating a quality of the sample event log; and, if a quality value of the sample event log is equal to or higher than a reference value, collecting data from the data source based on the event map to generate a multi-view event log.
  • the step of generating the event map and the sample event log using the metadata may include the steps of: converting the extracted metadata into a standardized entity relationship data model; generating a data catalog by connecting the entity relationship data models; inferring an activity concept and a time stamp concept from the data catalog; inferring a case concept from the data catalog; generating the event map by associating the activity concept, the time stamp concept, and the case concept; and extracting sample data from the data source and extracting data from the sample data based on the event map to generate the sample event log.
  • the step of creating a data catalog by connecting the entity relationship data models may include the step of confirming the similarity of field names included in the entity relationship data models; the step of connecting the entity relationship data models so that the data catalog becomes acyclic by using the confirmed similarity; and the step of removing optional fields that are not essential from each of the entity relationship data models included in the data catalog.
  • the step of inferring the activity concept and the time stamp concept from the data catalog comprises: a step of analyzing the data type of each field of the entity relationship data models included in the data catalog; a step of analyzing each field name of the entity relationship data models using dictionary-based pattern matching; a step of inferring the activity concept and the time stamp concept through date or time analysis among the fields included in the entity relationship data models; a step of removing the remaining time stamp fields except for the representative time stamp field from each of the entity relationship data models; a step of removing duplicate column names in the entity relationship data models included in the data catalog; a step of calculating the centrality of each of the entity relationship data models based on the number of entity relationship data models that depend on each of the entity relationship data models included in the data catalog; a step of removing an entity relationship data model that has no time stamp concept candidate and no entity relationship data model that depends on the entity relationship data model among the entity relationship data models included in the data catalog;
  • the method may further include: removing fields that are not primary keys or foreign keys and fields
  • the step of inferring the case concept from the data catalog can generate the case concept by inferring the number of times each field of the entity relationship data models included in the data catalog is cited and the ranking of the number of times cited.
  • the method for generating a multi-view event log may further include a step of generating an event map based on data quality if at least one of the activity concept, the timestamp concept and the case concept is not found; and a step of generating a multi-view event log from the data source based on the event map generated based on the data quality.
  • the step of calculating the quality of the sample event log can be calculated by considering at least one of the case identifier ratio, the trace mutation ratio, the average ratio of unique activities per case, the endpoint activity ratio, and the start-end activity ratio of the sample event log.
  • the method for generating a multi-viewpoint event log may further include a step of generating an event map based on data quality if the quality value of the sample event log is less than a reference value; and a step of generating a multi-viewpoint event log from the data source based on the event map generated based on the data quality.
  • the step of generating an event map based on the data quality may include: a step of calculating a data quality index from the data source; a step of calculating a data quality dimension and an average value from the data source; a step of inferring an event concept candidate using a classification algorithm; a step of generating a plurality of candidate event maps based on the event concept candidates; a step of extracting sample data from the data source and generating a sample candidate event log based on each of the plurality of candidate event maps; a step of calculating a quality for each of the sample candidate event logs; and a step of determining the event map from among the plurality of candidate event maps based on a quality of the sample candidate event log.
  • the step of inferring the event concept candidate using the classification algorithm may include the step of predicting the event concept for each field of the entity relationship data models extracted from the data source as the event concept candidate using the classification model generated by using the previously learned event log as learning data as input to the classification algorithm; the step of removing fields that are not primary keys or foreign keys and are not event concept fields from the entity relationship data models; the step of setting the average value of the data quality indicators for all the event concept fields included in the entity relationship data models as a threshold value and removing the event concept field whose average value of the data quality indicator of the event concept field is lower than the threshold value; the step of modifying fields with the same field name included in the entity relationship data models but whose event concept candidates of the event concept field are different to have the same event concept candidate; and the step of removing fields with the same field name included in the entity relationship data models except for the field with the highest average value of the data quality indicator.
  • the step of determining the event map from among the plurality of candidate event maps based on the quality of the sample candidate event log may determine the event map as the candidate event map corresponding to the sample candidate event log having the highest quality.
  • the step of determining the event map from among the plurality of candidate event maps based on the quality of the sample candidate event logs may include providing the user with quality information of the sample candidate event logs corresponding to the plurality of candidate event maps, and determining the event map based on the candidate event map selected by the user.
  • a system for generating a multi-view event log comprises: a memory for storing a data source; and a processor, wherein the processor extracts metadata from the data source, generates an event map and a sample event log using the metadata, calculates a quality of the sample event log, and if the quality value of the sample event log is equal to or higher than a reference value, collects data from the data source based on the event map to generate a multi-view event log.
  • the processor may convert the extracted metadata into a standardized entity relationship data model when generating the event map and the sample event log using the metadata, generate a data catalog by connecting the entity relationship data models, infer an activity concept and a time stamp concept from the data catalog, infer a case concept from the data catalog, generate the event map by associating the activity concept, the time stamp concept, and the case concept, extract sample data from the data source, and extract data from the sample data based on the event map to generate the sample event log.
  • the processor can generate an event map based on data quality if at least one of the activity concept, the timestamp concept and the case concept is not found, and can generate a multi-view event log from the data source based on the event map generated based on the data quality.
  • the processor may calculate the quality of the sample event log by considering at least one of the case identifier ratio, the trace mutation ratio, the average ratio of unique activities per case, the endpoint activity ratio, and the start-end activity ratio of the sample event log.
  • the processor can generate an event map based on data quality if the quality value of the sample event log is less than a reference value, and generate a multi-viewpoint event log from the data source based on the event map generated based on the data quality.
  • the present invention relates to a system and method for generating a multi-perspective event log, which receives a data source, extracts metadata from the data source, generates an event map and a sample event log using the metadata, calculates the quality of the sample event log, and generates a multi-perspective event log by collecting data from the data source based on the event map if the quality value of the sample event log is higher than a reference value. Since various case concepts are automatically inferred, the multi-perspective event log can be extracted without forcing the user to specify or infer case concepts.
  • FIG. 1 is a diagram schematically illustrating a configuration of a system for generating a multi-view event log according to an embodiment of the present invention.
  • FIG. 2 is a flowchart schematically illustrating a process for generating a multi-view event log in a system according to one embodiment of the present invention.
  • FIG. 3 is a flowchart illustrating a process of generating an event map and a sample event log using metadata in a system according to an embodiment of the present invention.
  • FIG. 4 is a flowchart illustrating a process of creating a data catalog in a system according to one embodiment of the present invention.
  • FIG. 5 is a flowchart illustrating a process of inferring an activity concept and a time stamp concept from a data catalog in a system according to one embodiment of the present invention.
  • FIG. 6 is a flowchart illustrating a process of generating an event map based on data quality in a system according to an embodiment of the present invention.
  • FIG. 7 is a flowchart illustrating a process of inferring event opening candidates using a classification algorithm in a system according to one embodiment of the present invention.
  • FIG. 1 is a diagram schematically illustrating a configuration of a system for generating a multi-view event log according to an embodiment of the present invention.
  • a system (100) for generating a multi-view event log may be configured to include a processor (110), a communication unit (120), and a memory (130).
  • the communication unit (120) is a communication interface device including a receiver and a transmitter, and can transmit and receive data wired or wirelessly.
  • the communication unit (120) can receive a data source by connecting to an external database server, etc.
  • the memory (130) stores an operating system, application programs and storage data for controlling the overall operation of the system (100), and can also store data sources, metadata, entity relationship data models, data catalogs, event maps, sample event logs and multi-view event logs according to the present invention.
  • the processor (110) may be configured to include a metadata extraction unit (111), a first event map generation unit (112), a quality calculation unit (113), a second event map generation unit (114), and a multi-viewpoint event log generation unit (115).
  • the metadata extraction unit (111) can receive a data source from the memory (130) and extract metadata from the data source.
  • the first event map generation unit (112) can generate an event map and a sample event log using metadata.
  • the first event map generation unit (112) can 1) convert extracted metadata into a standardized entity relationship data model, 2) generate a data catalog by connecting entity relationship data models, 3) infer an activity notion and a timestamp notion from the data catalog, infer a case notion from the data catalog, 4) generate an event map by associating the activity concept, the timestamp concept, and the case concept, and 5) extract sample data from a data source and extract data from the sample data based on the event map to generate a sample event log.
  • the first event map generation unit (112) can request the second event map generation unit (114) to generate an event map based on data quality.
  • the first event map generation unit (112) can generate a data catalog by connecting entity relationship data models through the following process.
  • the first event map generation unit (112) can generate a data catalog by 1) checking the similarity of field names included in entity relationship data models, 2) using the checked similarity to connect entity relationship data models so that the data catalog is acyclic, and 3) removing optional fields that are not essential from each of the entity relationship data models included in the data catalog.
  • the first event map generation unit (112) can infer the activity concept and the timestamp concept from the data catalog through the following process.
  • the first event map generation unit (112) 1) analyzes the data type of each field for the entity relationship data models included in the data catalog, 2) analyzes each field name of the entity relationship data models using dictionary-based pattern matching, 3) infers the activity concept and the timestamp concept through date or time analysis among the fields included in the entity relationship data models, 4) removes the remaining timestamp fields except for the representative timestamp field from each of the entity relationship data models, 5) removes duplicate column names in the entity relationship data models included in the data catalog, 6) calculates the centrality of each entity relationship data model based on the number of entity relationship data models that depend on each entity relationship data model included in the data catalog, 7) removes entity relationship data models that do not have a timestamp concept candidate and do not have an entity relationship data model that depends on the entity relationship data model included in the data catalog, and 8) generates a centrality map for each entity relationship data model included in
  • the timestamp concept and activity life cycle can be inferred through text similarity analysis of the timestamp field, and 10) the date and time pattern can be removed from the timestamp concept and the activity concept can be inferred by linking the fields that are not primary or foreign keys and timestamps so that there is at most one category field and one numeric field.
  • the first event map generation unit (112) can generate a case concept by inferring the number of times each field of entity relationship data models included in the data catalog is cited and the ranking of the number of times cited.
  • the quality calculation unit (113) can calculate the quality of a sample event log.
  • the quality calculation unit (113) can calculate the quality of the sample event log by considering at least one of the case identifier ratio, the trace mutation ratio, the average ratio of unique activities per case, the endpoint activity ratio, and the start-end activity ratio of the sample event log.
  • the second event map generation unit (114) can generate an event map based on data quality if the quality value of the sample event log is lower than a reference value. More specifically, the second event map generation unit (114) can 1) calculate a data quality index from a data source, 2) calculate a data quality dimension and an average value from the data source, 3) infer event concept candidates using a classification algorithm, 4) generate a plurality of candidate event maps based on the event concept candidates, 5) extract sample data from the data source and generate sample candidate event logs based on each of the plurality of candidate event maps, 6) calculate the quality for each of the sample candidate event logs, and 7) determine an event map from among the plurality of candidate event maps based on the quality of the sample candidate event logs.
  • the second event map generation unit (114) determines an event map from among a plurality of candidate event maps based on the quality of the sample candidate event logs, if set to fully automatic, it can determine the candidate event map corresponding to the sample candidate event log with the highest quality as the event map.
  • the second event map generation unit (114) determines an event map from among a plurality of candidate event maps based on the quality of the sample candidate event logs. If manually set, the second event map generation unit (114) provides the user with quality information of the sample candidate event logs corresponding to the plurality of candidate event maps, and determines the event map based on the candidate event map selected by the user.
  • the second event map generation unit (114) can infer event concept candidates by the following process when inferring them using a classification algorithm.
  • the second event map generation unit (114) 1) predicts event concepts for each field of entity relationship data models extracted from a data source as event concept candidates by using a classification model generated by using pre-learned event logs as learning data as input to a classification algorithm, 2) removes fields that are not primary keys or foreign keys and are not event concept fields from the entity relationship data models, 3) sets the average value of data quality indicators for all event concept fields included in the entity relationship data models as a threshold value, and removes event concept fields whose average value of data quality indicators of event concept fields is lower than the threshold value, 4) modifies fields with the same field names included in the entity relationship data models but whose event concept candidates are different to have the same event concept candidates, and 5) removes fields with the same field names included in the entity relationship data models except for the field with the highest average value of data quality indicators, thereby inferring event concept candidates.
  • the second event map generation unit (114) is requested to generate an event map because at least one of the activity concept, the time stamp concept, and the case concept is not found by the first event map generation unit (112), 1) an event map can be generated based on data quality, and 2) a multi-viewpoint event log can be generated from a data source based on the event map generated based on data quality.
  • the multi-viewpoint event log generation unit (115) can generate a multi-viewpoint event log by collecting data from a data source based on an event map if the quality value of the sample event log calculated by the quality calculation unit (113) is higher than a reference value.
  • the multi-viewpoint event log generation unit (115) can generate a multi-viewpoint event log from a data source based on the event map generated based on data quality by generating an event map generated based on data quality in the second event map generation unit (114).
  • the metadata extraction unit (111), the first event map generation unit (112), the quality calculation unit (113), the second event map generation unit (114), and the multi-viewpoint event log generation unit (115) are configured to be included in the processor (110), but are not limited thereto and may be configured as separate devices. In addition, they may be implemented in the form of program commands that can be executed through various computer means.
  • FIG. 2 is a flowchart schematically illustrating a process for generating a multi-view event log in a system according to one embodiment of the present invention.
  • the system (100) can receive a data source (210).
  • system (100) can extract metadata from a data source (220).
  • Metadata can be composed of key information indicating whether it is a primary key or a foreign key, a field name indicating what the field is, type information distinguishing whether the field is a case type or a timestamp type, and required information indicating whether the field is required or optional.
  • the system (100) can generate an event map and a sample event log using metadata (230).
  • a detailed description of step 230 will be provided later with reference to FIG. 3.
  • system (100) can calculate the quality of the sample event log (240).
  • the system (100) can calculate by considering at least one of a case identifier ratio of the sample event log, a trace mutation ratio, an average ratio of unique activities per case, an endpoint activity ratio, and a start-to-finish activity ratio.
  • the system (100) can calculate the quality of the event log by referring to ⁇ Mathematical Formula 1> and ⁇ Mathematical Formula 2> below.
  • event log ( ) represents the total number of unique start and end activities.
  • event log ( ) represents the total number of common elements of start and end activities.
  • the relationship between the event map and the generated event log perspective is that exactly one event log is generated for each event map perspective. can have a relationship.
  • the system (100) can check whether the quality value of the sample event log is higher than the reference value (250).
  • the system (100) can collect data from the data source based on the event map to generate a multi-view event log (270).
  • step 260 If the quality value of the sample event log as a result of the verification in step 250 is less than the reference value, the system (100) can generate an event map based on the data quality (260). A detailed description of step 260 will be provided later with reference to FIG. 6.
  • system (100) can generate a multi-view event log from a data source based on an event map generated based on data quality in step 270.
  • FIG. 3 is a flowchart illustrating a process of generating an event map and a sample event log using metadata in a system according to an embodiment of the present invention.
  • the system (100) can convert extracted metadata into a standardized entity relationship data model (310).
  • system (100) can create a data catalog by connecting entity relationship data models (320). A specific description of step 320 will be explained later through FIG. 4.
  • the system (100) can infer the activity concept and the time stamp concept from the data catalog (330). A specific description of step 330 will be explained later through FIG. 5.
  • system (100) can infer case concepts from the data catalog (340).
  • the system (100) can generate a case concept by inferring the number of times each field of the entity relationship data models included in the data catalog is cited and the rank of the number of times cited.
  • the case concept can include entity relationship data model information, a field name, information on the number of times the field is cited, and information on the rank of the number of times cited.
  • the system (100) can check whether it has discovered all of the activity concept, the time stamp concept, and the case concept (350).
  • step 350 If, as a result of the verification in step 350, none of the activity concept, the time stamp concept, and the case concept are found, that is, if there is at least one concept that is not found among the activity concept, the time stamp concept, and the case concept, the system (100) can proceed to step 260 of FIG. 2 and perform step 260.
  • the system (100) can generate an event map by associating the activity concepts, timestamp concepts, and case concepts (360). That is, in step 360, the system (100) can generate an event map to include all of the activity concepts, timestamp concepts, and case concepts.
  • the system (100) can extract sample data from a data source and extract data from the sample data based on an event map to generate a sample event log (370).
  • the system (100) can generate an event map based on data quality if at least one of the activity concept, the time stamp concept, and the case concept is not found (380).
  • system (100) can generate a multi-view event log from a data source based on an event map generated based on data quality (390).
  • FIG. 4 is a flowchart illustrating a process of creating a data catalog in a system according to an embodiment of the present invention.
  • the system (100) can check the similarity of field names included in entity relationship data models (410).
  • system (100) can connect entity relationship data models to make the data catalog acyclic by utilizing the verified similarity (420).
  • system (100) can remove optional fields that are not essential from each of the entity relationship data models included in the data catalog (430).
  • FIG. 5 is a flowchart illustrating a process of inferring an activity concept and a time stamp concept from a data catalog in a system according to one embodiment of the present invention.
  • the system (100) can analyze the data type of each field for entity relationship data models included in the data catalog (510).
  • system (100) can analyze each field name of entity relationship data models using dictionary-based pattern matching (512).
  • the system (100) can infer the activity concept and the time stamp concept through date or time analysis among the fields included in the entity relationship data models (514).
  • the time stamp concept can include entity relationship data model information, field name information, and information about the life cycle of the corresponding field.
  • system (100) can remove all timestamp fields except the representative timestamp field from each of the entity relationship data models (516).
  • system (100) can remove duplicate column names in entity relational data models included in the data catalog (518).
  • system (100) can calculate the centrality of each entity relationship data model included in the data catalog based on the number of entity relationship data models that depend on each entity relationship data model (520).
  • the system (100) can remove an entity relationship data model that does not have a time stamp concept candidate among the entity relationship data models included in the data catalog and does not have an entity relationship data model that depends on the entity relationship data model (522).
  • system (100) can infer the time stamp concept and activity life cycle through text similarity analysis of the time stamp field for each entity relationship data model included in the data catalog (526).
  • system (100) can remove the date and time pattern from the timestamp concept and infer the activity concept by connecting fields that are not primary keys or foreign keys and fields that do not correspond to timestamps (528).
  • FIG. 6 is a flowchart illustrating a process of generating an event map based on data quality in a system according to an embodiment of the present invention.
  • the system (100) can calculate a data quality index from a data source (610).
  • system (100) can calculate data quality dimensions and average values from the data source (620).
  • the system (100) can calculate steps 610 and 620 by referring to Andrews, Robert, et al. "Quality-informed semi-automated event log generation for process mining.” Decision Support Systems 132 (2020): 113265.
  • the system (100) can infer event concept candidates using a classification algorithm (630).
  • a specific description of step 630 will be described later through FIG. 7.
  • system (100) can generate a plurality of candidate event maps based on the event concept candidates (640).
  • system (100) can extract sample data from a data source and generate sample candidate event logs based on each of a plurality of candidate event maps (650).
  • system (100) can calculate the quality for each sample candidate event log (660).
  • the system (100) can determine an event map among a plurality of candidate event maps based on the quality of the sample candidate event log (670).
  • step 670 the system (100) determines an event map corresponding to a sample candidate event log having the highest quality, or provides the user with quality information of sample candidate event logs corresponding to a plurality of candidate event maps, and determines an event map corresponding to a candidate event map selected by the user.
  • FIG. 7 is a flowchart illustrating a process of inferring event opening candidates using a classification algorithm in a system according to one embodiment of the present invention.
  • the system (100) can predict event concepts for each field of entity relationship data models extracted from a data source as event concept candidates by using a classification model generated by using pre-learned event logs as learning data and as input to a classification algorithm (710).
  • system (100) can remove fields that are not primary or foreign keys and are not event concept fields from entity relational data models (720).
  • the system (100) can set the average value of the data quality indicators for all event concept fields included in the entity relationship data models as a threshold value, and remove event concept fields whose average value of the data quality indicators of the event concept fields is lower than the threshold value (730).
  • system (100) can modify fields that have the same field name but different event concept candidates in the event concept fields included in entity relationship data models to have the same event concept candidates (740).
  • system (100) can remove fields with the same field name included in entity relationship data models except for the field with the highest average value of data quality indicator (750).
  • the devices described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components.
  • the devices and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them.
  • the processing device may execute an operating system (OS) and one or more software applications running on the operating system.
  • the processing device may access, store, manipulate, process, and generate data in response to the execution of the software.
  • OS operating system
  • the processing device may access, store, manipulate, process, and generate data in response to the execution of the software.
  • the processing device is sometimes described as being used alone, but those skilled in the art will appreciate that the processing device may include multiple processing elements and/or multiple types of processing elements.
  • the processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.
  • the software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may, independently or collectively, command the processing device.
  • the software and/or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal waves, for interpretation by the processing device or for providing instructions or data to the processing device.
  • the software may also be distributed over network-connected computer systems, and stored or executed in a distributed manner.
  • the software and data may be stored on one or more computer-readable recording media.
  • the method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium.
  • the computer-readable medium may include program commands, data files, data structures, etc., alone or in combination.
  • the program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known to and available to those skilled in the art of computer software.
  • Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands such as ROMs, RAMs, flash memories, etc.
  • Examples of the program commands include not only machine language codes generated by a compiler but also high-level language codes that can be executed by a computer using an interpreter, etc.
  • the hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

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  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Quality & Reliability (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente invention concerne un système et un procédé de génération d'un journal d'événements multi-perspective et peut consister à : recevoir une source de données; extraire des métadonnées de la source de données ; générer une carte d'événements et un journal d'événements d'échantillon à l'aide des métadonnées; calculer la qualité du journal d'événements d'échantillon ; et, si la valeur de qualité du journal d'événements d'échantillon est supérieure ou égale à une valeur de référence, générer, sur la base de la carte d'événements, un journal d'événements multi-perspective par collecte de données à partir de la source de données.
PCT/KR2023/016843 2023-09-14 2023-10-27 Système et procédé de génération de journal d'événements multi-perspective Pending WO2025058122A1 (fr)

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KR1020230122543A KR102655198B1 (ko) 2023-09-14 2023-09-14 다중 관점 이벤트 로그를 생성하는 시스템 및 방법
KR10-2023-0122543 2023-09-14

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WO2025058122A1 true WO2025058122A1 (fr) 2025-03-20

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Citations (3)

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KR20140085866A (ko) * 2012-12-28 2014-07-08 국립대학법인 울산과학기술대학교 산학협력단 다양한 성과측정 분석이 가능한 프로세스 마이닝 방법 및 그 장치
KR20150098400A (ko) * 2014-02-20 2015-08-28 부산대학교 산학협력단 다차원 시간차 분석 방법 및 장치
KR20190071571A (ko) * 2017-12-14 2019-06-24 주식회사 퍼즐데이터 표준 프로세스를 기반으로 한 사용자 프로세스 분석 장치 및 방법

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KR20140085866A (ko) * 2012-12-28 2014-07-08 국립대학법인 울산과학기술대학교 산학협력단 다양한 성과측정 분석이 가능한 프로세스 마이닝 방법 및 그 장치
KR20150098400A (ko) * 2014-02-20 2015-08-28 부산대학교 산학협력단 다차원 시간차 분석 방법 및 장치
KR20190071571A (ko) * 2017-12-14 2019-06-24 주식회사 퍼즐데이터 표준 프로세스를 기반으로 한 사용자 프로세스 분석 장치 및 방법

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