System and method for generating multi-perspective event log

Abstract

The present invention relates to a system and a method for generating a multi-perspective event log, and may comprise: receiving a data source; extracting metadata from the data source; generating an event map and a sample event log by using the metadata; calculating the quality of the sample event log; and, if the quality value of the sample event log is greater than or equal to a reference value, generating, on the basis of the event map, a multi-perspective event log by collecting data from the data source.

Description

System and method for generating multi-view event logs

[0001] The following embodiments relate to a system and method for generating logs with multiple viewpoint events.

[0002] 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.

[0003] In practice, event logs are not always readily available in most real-world situations. In most cases, event logs must be extracted from data sources (e.g., information systems or databases) and converted into a specific structure and format that process mining can interpret.

[0004] The process of extracting and identifying (e.g., cases, activities, timestamps) events requires manual intervention and domain knowledge about the process, making event log generation a major challenge, both time-consuming and costly.

[0005] Another challenge in generating event logs is addressing process perspectives. Event logs are typically tied to specific process analysis objectives. This means that business perspectives and the perspectives themselves can be correlated with data entities.

[0006] Multiple perspectives can be extracted from a database, meaning multiple event logs and process models can be generated for the same process data.

[0007] In particular, several approaches have been proposed to automatically extract event logs from databases, such as rdb2log or OpenSLEX. However, these approaches require partial or full database scans, resulting in long computation times and limiting the source to relational databases.

[0008] The present invention aims to provide a system and method for generating a multi-view event log.

[0009] A method for generating a multi-view event log according to an embodiment of the present invention 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 the quality value of the sample event log is equal to or greater than a reference value, collecting data from the data source based on the event map to generate a multi-view event log.

[0010] At this time, 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 timestamp concept from the data catalog; inferring a case concept from the data catalog; generating the event map by associating the activity concept, the timestamp 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.

[0011] At this time, 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 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.

[0012] At this time, the step of inferring the activity concept and the timestamp concept from the data catalog includes: 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 timestamp concept through date or time analysis among the fields included in the entity relationship data models; a step of removing the remaining timestamp fields except for the representative timestamp 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 included in the data catalog based on the number of entity relationship data models that depend on each of the entity relationship data models; a step of removing an entity relationship data model that does not have a timestamp concept candidate and does not have an 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 that do not correspond to timestamps so that each of the entity relationship data models included in the data catalog has at most one category field and one numeric field; inferring a timestamp concept and an activity life cycle through text similarity analysis of a timestamp field for each of the entity relationship data models included in the data catalog; and removing a date and time pattern from the timestamp concept and inferring an activity concept by connecting fields that are not primary keys or foreign keys and fields that do not correspond to timestamps.

[0013] At this time, 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.

[0014] At this time, the method for generating a multi-view event log may further include the 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 the step of generating a multi-view event log from the data source based on the event map generated based on the data quality.

[0015] At this time, the step of calculating the quality of the sample event log may be calculated by considering at least one of the case identifier ratio, the trace variation ratio, the average ratio of unique activities per case, the endpoint activity ratio, and the start-end activity ratio of the sample event log.

[0016] At this time, the method for generating a multi-view 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-view event log from the data source based on the event map generated based on the data quality.

[0017] At this time, the step of generating an event map based on the data quality may include: calculating a data quality index from the data source; calculating a data quality dimension and an average value from the data source; inferring an event concept candidate using a classification algorithm; generating a plurality of candidate event maps based on the event concept candidate; extracting sample data from the data source and generating a sample candidate event log based on each of the plurality of candidate event maps; calculating a quality for each of the sample candidate event logs; and determining the event map from among the plurality of candidate event maps based on the quality of the sample candidate event log.

[0018] At this time, the step of inferring the event concept candidate using the classification algorithm may include the step of predicting an event concept for each field of entity relationship data models extracted from the data source as the event concept candidate using a classification model generated by using a pre-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 an average value of 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 fields whose average value of the data quality indicators of the event concept fields is lower than the threshold value; the step of modifying fields included in the entity relationship data models that have the same field name but different event concept candidates of the event concept fields 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 that has the highest average value of the data quality indicator.

[0019] At this time, 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 with the highest quality.

[0020] At this time, 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 provide the user with quality information of the sample candidate event log corresponding to the plurality of candidate event maps, and determine the event map based on the candidate event map selected by the user.

[0021] A system for generating a multi-view event log according to one embodiment of the present invention 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 the 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.

[0022] At this time, the processor may, when generating the event map and the sample event log using the metadata, convert the extracted metadata into a standardized entity relationship data model, connect the entity relationship data models to generate a data catalog, 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.

[0023] At this time, if at least one of the activity concept, the timestamp concept, and the case concept is not found, the processor can generate an event map based on data quality, and generate a multi-view event log from the data source based on the event map generated based on the data quality.

[0024] At this time, when calculating the quality of the sample event log, the processor may calculate by considering at least one of the case identifier ratio, the trace variation ratio, the average ratio of unique activities per case, the endpoint activity ratio, and the start-end activity ratio of the sample event log.

[0025] At this time, if the quality value of the sample event log is less than a reference value, the processor can generate an event map based on data quality, and generate a multi-view event log from the data source based on the event map generated based on the data quality.

[0026] 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 if the quality value of the sample event log is higher than a reference value, collects data from the data source based on the event map to generate a multi-perspective event log, and can extract a multi-perspective event log without forcing a user to specify or infer a case concept because various case concepts are automatically inferred.

[0027] FIG. 1 is a schematic diagram illustrating a configuration of a system for generating a multi-view event log according to an embodiment of the present invention.

[0028] 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.

[0029] FIG. 3 is a flowchart illustrating a process of creating an event map and a sample event log using metadata in a system according to an embodiment of the present invention.

[0030] FIG. 4 is a flowchart illustrating a process of creating a data catalog in a system according to one embodiment of the present invention.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, the embodiments may be modified in various ways, and the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, or alternatives to the embodiments are included within the scope of the patent application.

[0035] The terms used in the examples are for illustrative purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but should be understood to not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0036] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments pertain. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0037] In addition, when describing with reference to the attached drawings, identical components will be assigned the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted. When describing embodiments, if a detailed description of a related known technology is judged to unnecessarily obscure the gist of the embodiment, the detailed description will be omitted.

[0038] Additionally, terms such as first, second, A, B, (a), (b), etc. may be used to describe components of the embodiments. These terms are only intended to distinguish the components from other components, and the nature, order, or sequence of the components are not limited by the terms. When a component is described as being "connected," "coupled," or "connected" to another component, it should be understood that the component may be directly connected or connected to the other component, but another component may also be "connected," "coupled," or "connected" between each component.

[0039] Components included in one embodiment and components with common functions will be described using the same names in other embodiments. Unless otherwise stated, the descriptions given in one embodiment may also apply to other embodiments, and detailed descriptions will be omitted to the extent of overlap.

[0040] Hereinafter, a system and method for generating a multi-view event log according to an embodiment of the present invention will be described in detail with reference to the attached FIGS. 1 to 7.

[0041] FIG. 1 is a schematic diagram illustrating a configuration of a system for generating a multi-view event log according to an embodiment of the present invention.

[0042] Referring to FIG. 1, 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).

[0043] 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.

[0044] 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.

[0045] 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).

[0046] The metadata extraction unit (111) can receive a data source from the memory (130) and extract metadata from the data source.

[0047] The first event map generation unit (112) can generate an event map and a sample event log using metadata.

[0048] More specifically, the first event map generation unit (112) can 1) convert the extracted metadata into a standardized entity relationship data model, 2) create 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) create 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 create a sample event log.

[0049] At this time, if at least one of the activity concept, time stamp concept, and case concept is not found, the first event map generation unit (112) may request the second event map generation unit (114) to generate an event map based on data quality.

[0050] 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) confirming the similarity of field names included in entity relationship data models, 2) connecting the entity relationship data models so that the data catalog is acyclic using the confirmed similarity, and 3) removing optional fields that are not essential from each of the entity relationship data models included in the data catalog.

[0051] When the first event map generation unit (112) infers the activity concept and the timestamp concept from the data catalog, it can infer 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 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, 7) removes the entity relationship data models that do not have a timestamp concept candidate and that do not have an entity relationship data model that depends on the entity relationship data model among the entity relationship data models included in the data catalog, and 8) removes the entity relationship data models that do not have a timestamp concept candidate and that do not have an entity relationship data model that depends on the entity relationship data model among the entity relationship data models included in the data catalog. 9) For each entity-relationship data model included in the data catalog, 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 to at most one category field and one numeric field.

[0052] When inferring a case concept from a data catalog, 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.

[0053] The quality calculation unit (113) can calculate the quality of a sample event log.

[0054] More specifically, 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.

[0055] 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 less 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.

[0056] When 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 log, 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.

[0057] When 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, it provides the user with quality information of the sample candidate event logs corresponding to the plurality of candidate event maps, and can determine the event map based on the candidate event map selected by the user.

[0058] The second event map generation unit (114) can infer event concept candidates using a classification algorithm through the following process. 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 using a classification model generated by using pre-learned event logs as learning data and 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 included in the entity relationship data models that have the same field name but different event concept candidates of the event concept fields to have the same event concept candidate, and 5) removes fields with the same field name included in the entity relationship data models except for the field that has the highest average value of data quality indicator, thereby inferring event concept candidates.

[0059] In addition, when 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), the second event map generation unit (114) can 1) generate an event map based on data quality, and 2) generate a multi-view event log from a data source based on the event map generated based on data quality.

[0060] 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.

[0061] 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 when the second event map generation unit (114) generates an event map based on data quality.

[0062]

[0063] In FIG. 1, 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-view 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.

[0064]

[0065] Hereinafter, the method according to the present invention configured as described above will be described with reference to the drawings below.

[0066] 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.

[0067] Referring to FIG. 2, the system (100) can receive a data source (210).

[0068] And, the system (100) can extract metadata from a data source (220).

[0069] At this time, the metadata may be configured to include 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.

[0070] Additionally, 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.

[0071] And, the system (100) can calculate the quality of the sample event log (240).

[0072] In step 240, the system (100) can calculate by considering at least one of the case identifier ratio of the sample event log, the trace mutation ratio, the average ratio of unique activities per case, the endpoint activity ratio, and the start-end activity ratio.

[0073] More specifically, in step 240, the system (100) can calculate the quality of the event log by referring to <Mathematical Formula 1> and <Mathematical Formula 2> below.

[0074] [Mathematical Formula 1]

[0075]

[0076] Here,

[0077] is the event log ( ) is also interesting,

[0078] is the event log ( ) is the case identifier ratio,

[0079] is the event log ( ) is the total number of unique case identifiers,

[0080] is the event log ( ) is the total number of events,

[0081] is the event log ( ) is the tracking deformation ratio,

[0082] is the event log ( ) is the total number of tracking variations,

[0083] is the average proportion of unique activities per case,

[0084] is the event log ( ) case( ) is the total number of unique activities,

[0085] is the event log ( ) case( ) is the entire event,

[0086] is the endpoint activity rate,

[0087] is the event log ( ) is the total number of unique start and end activities,

[0088] is the start-end activity ratio,

[0089] is the event log ( ) represents the total number of unique start and end activities.

[0090] is the event log ( ) represents the total number of common elements of start and end activities.

[0091] is a weight with a value between 0 and 1 for the case identifier ratio,

[0092] is a weight with a value between 0 and 1 for the tracking deformation ratio,

[0093] is a weight with a value between 0 and 1 for the average proportion of unique activities per case,

[0094] is a weight with a value between 0 and 1 for the endpoint activity ratio,

[0095] is a weight with a value between 0 and 1 for the start-end activity ratio.

[0096]

[0097] [Equation 2]

[0098]

[0099] Here, Event map results with multi-perspective event log interest Each event log perspective generated from The average of the event log interest in the sample event log, which corresponds to the quality of the sample event log,

[0100] is the perspective of each event log in <Mathematical Formula 1> Indicates the event log interest of

[0101] is the total number of event log views.

[0102] At this time, 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.

[0103]

[0104] And, the system (100) can check whether the quality value of the sample event log is higher than the reference value (250).

[0105] If the quality value of the sample event log as a result of the verification in step 250 is higher than the reference value, the system (100) can collect data from a data source based on the event map and generate a multi-view event log (270).

[0106] If the quality value of the sample event log is below the reference value as a result of the verification in step 250, 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.

[0107] In addition, the 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.

[0108]

[0109] FIG. 3 is a flowchart illustrating a process of creating an event map and a sample event log using metadata in a system according to an embodiment of the present invention.

[0110] Referring to FIG. 3, the system (100) can convert the extracted metadata into a standardized entity relationship data model (310).

[0111] Additionally, the system (100) can create a data catalog by connecting entity relationship data models (320). A detailed description of step 320 will be provided later with reference to FIG. 4.

[0112] Additionally, the system (100) can infer activity concepts and timestamp concepts from the data catalog (330). A detailed description of step 330 will be provided later with reference to FIG. 5.

[0113] And, the system (100) can infer case concepts from the data catalog (340).

[0114] In step 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 ranking of the number of times the field is cited. The case concept can include entity-relationship data model information, field names, information on the number of times the field is cited, and information on the ranking of the number of times the field is cited.

[0115] And, the system (100) can check whether it has discovered all of the activity concept, the time stamp concept, and the case concept (350).

[0116] If, as a result of the verification in step 350, none of the activity concepts, timestamp concepts, and case concepts are found, that is, if any of the activity concepts, timestamp concepts, and case concepts are not found, the system (100) can proceed to step 260 of FIG. 2 and perform step 260.

[0117] If all of the activity concepts, timestamp concepts, and case concepts are discovered as a result of the verification in step 350, 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 that includes all of the activity concepts, timestamp concepts, and case concepts.

[0118] And, 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).

[0119] And, the system (100) 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 (380).

[0120] In addition, the system (100) can generate a multi-view event log from a data source based on an event map generated based on data quality (390).

[0121]

[0122] FIG. 4 is a flowchart illustrating a process of creating a data catalog in a system according to one embodiment of the present invention.

[0123] Referring to FIG. 4, the system (100) can check the similarity of field names included in entity relationship data models (410).

[0124] And, the system (100) can connect entity relationship data models so that the data catalog becomes acyclic by using the confirmed similarity (420).

[0125] Additionally, the system (100) can remove optional fields that are not essential from each of the entity relationship data models included in the data catalog (430).

[0126]

[0127] 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.

[0128] Referring to FIG. 5, the system (100) can analyze the data type of each field for entity relationship data models included in the data catalog (510).

[0129] And, the system (100) can analyze each field name of the entity relationship data models using dictionary-based pattern matching (512).

[0130] In addition, the system (100) can infer activity concepts and timestamp concepts through date or time analysis among fields included in entity relationship data models (514). At this time, the timestamp concept may include entity relationship data model information, field name information, and information regarding the life cycle of the corresponding field.

[0131] And, the system (100) can remove the remaining timestamp fields except the representative timestamp field from each of the entity relationship data models (516).

[0132] Additionally, the system (100) can remove duplicate column names in entity relationship data models included in the data catalog (518).

[0133] In addition, the system (100) can calculate 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 (520).

[0134] And, 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).

[0135] In addition, the system (100) can remove fields that are not primary keys or foreign keys and fields that do not correspond to timestamps so that each of the entity relationship data models included in the data catalog has at most one category field and one numeric field (524).

[0136] In addition, the 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).

[0137] And, the system (100) can infer the activity concept by removing the date and time pattern from the timestamp concept and connecting fields that are not primary keys or foreign keys and fields that do not correspond to timestamps (528).

[0138]

[0139] 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.

[0140] Referring to FIG. 6, the system (100) can calculate a data quality index from a data source (610).

[0141] And, the system (100) can calculate data quality dimensions and average values ​​from the data source (620).

[0142] At this time, 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.

[0143] Additionally, the system (100) can infer event concept candidates using a classification algorithm (630). A detailed description of step 630 will be provided later with reference to FIG. 7.

[0144] And, the system (100) can generate a plurality of candidate event maps based on event concept candidates (640).

[0145] In addition, the system (100) can extract sample data from a data source and generate a sample candidate event log based on each of a plurality of candidate event maps (650).

[0146] And, the system (100) can calculate the quality for each sample candidate event log (660).

[0147] And, 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).

[0148] In 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.

[0149]

[0150] 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.

[0151] Referring to FIG. 7, the system (100) can predict an event concept for each field of entity relationship data models extracted from a data source as an event concept candidate by using a classification model generated by using a pre-learned event log as learning data and as input to a classification algorithm (710).

[0152] Additionally, the system (100) can remove fields that are not primary keys or foreign keys and are not event concept fields from entity relationship data models (720).

[0153] In addition, 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).

[0154] In addition, the 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).

[0155] And, the 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).

[0156]

[0157] The devices described above may be implemented as hardware components, software components, and / or a combination of hardware components and software components. For example, 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 also access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, one of ordinary skill in the art will recognize that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, a processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

[0158] 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 wave, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked 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.

[0159] 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 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, and flash memories. 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.

[0160] Although the embodiments described above have been described by way of limited examples and drawings, those skilled in the art will appreciate that various modifications and variations can be made based on the above teachings. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0161] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

Claims

1. Step of receiving a data source; A step of extracting metadata from the above data source; A step of generating an event map and a sample event log using the above metadata; a step of calculating the quality of the above sample event log; and If the quality value of the above sample event log is greater than or equal to the reference value, a step of collecting data from the data source based on the event map to generate a multi-view event log How to generate a multi-view event log containing .

2. In paragraph 1, The step of generating the event map and the sample event log using the above metadata is as follows: A step of converting the extracted metadata into a standardized entity relationship data model; A step of creating a data catalog by connecting the above entity relationship data models; A step of inferring the activity concept and the timestamp concept from the above data catalog; A step of inferring case concepts from the above data catalog; A step of generating the event map by associating the above activity concept, the above time stamp concept, and the above case concept; and A step of extracting sample data from the above data source and extracting data from the sample data based on the above event map to generate the sample event log. How to generate a multi-view event log containing .

3. In paragraph 2, The steps for creating a data catalog by connecting the above entity relationship data models are: A step of verifying the similarity of field names included in the above entity relationship data models; A step of connecting the entity relationship data models so that the data catalog becomes acyclic by using the confirmed similarity; and A step of removing optional fields that are not essential from each of the entity relationship data models included in the data catalog. How to generate a multi-view event log containing .

4. In paragraph 2, The step of inferring the activity concept and the timestamp concept from the above data catalog is: 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 above entity relationship data models using dictionary-based pattern matching; A step of inferring the activity concept and the timestamp concept through date or time analysis among the fields included in the above entity relationship data models; A step of removing all timestamp fields except the representative timestamp field from each of the above 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 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; A step of removing an entity relationship data model that does not have a timestamp concept candidate among the entity relationship data models included in the data catalog and that does not have an entity relationship data model that depends on the entity relationship data model; Steps for removing fields that are not primary keys or foreign keys and fields that are not timestamps so that each of the entity relationship data models included in the data catalog has at most one category field and one numeric field: A step of inferring the time stamp concept and activity life cycle through text similarity analysis of the time stamp field for each of the entity relationship data models included in the data catalog; and A step of removing date and time patterns from the above timestamp concept and inferring the activity concept by connecting fields that are not the primary key or the foreign key and fields that do not correspond to the timestamp. How to generate a multi-view event log containing .

5. In paragraph 2, The step of inferring the case concept from the above data catalog is: Generating 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 rank of the number of times cited. How to generate a multi-perspective event log.

6. In paragraph 2, If at least one of the above activity concept, the above timestamp concept and the above case concept is not found, a step of generating an event map based on data quality; and A step of generating a multi-view event log from the data source based on an event map generated based on the above data quality. How to generate a multi-perspective event log that includes more.

7. In paragraph 1, The steps for calculating the quality of the above sample event log are: Calculated by considering at least one of the case identifier ratio, trace mutation ratio, average ratio of unique activities per case, endpoint activity ratio, and start-end activity ratio of the above sample event log. How to generate a multi-perspective event log.

8. In paragraph 1, If the quality value of the above sample event log is below the reference value, a step of generating an event map based on data quality; and A step of generating a multi-view event log from the data source based on an event map generated based on the above data quality. How to generate a multi-perspective event log that includes more.

9. In paragraph 8, The step of creating an event map based on the above data quality is: A step of calculating data quality indicators from the above data source; A step of calculating data quality dimensions and average values ​​from the above data source; A step of inferring event concept candidates using a classification algorithm; A step of generating a plurality of candidate event maps based on the above event concept candidates; A step of extracting sample data from the above data source and generating a sample candidate event log based on each of the plurality of candidate event maps; A step of calculating the quality for each of the above sample candidate event logs; and A step of determining the event map among the plurality of candidate event maps based on the quality of the sample candidate event log. How to generate a multi-view event log containing .

10. In paragraph 9, The step of inferring the event concept candidate using the above classification algorithm is: A step of predicting an event concept for each field of entity relationship data models extracted from the data source as an event concept candidate using a classification model generated by using a pre-learned event log as learning data and as input to the classification algorithm; A step of removing fields that are not primary keys or foreign keys and are not event concept fields from the above entity relationship data models; A step of setting an average value of data quality indicators for all event concept fields included in the entity relationship data models as a threshold value, and removing an event concept field whose average value of data quality indicators is lower than the threshold value; A step of modifying fields whose field names are the same but whose event concept candidates are different in the above entity relationship data models to have the same event concept candidates; and A step of removing fields with the same field name included in the above entity relationship data models, except for the field with the highest average value of the data quality indicator. How to generate a multi-view event log containing .

11. In paragraph 9, The step of determining the event map among the plurality of candidate event maps based on the quality of the sample candidate event log is as follows: Determine the event map corresponding to the candidate event log of the sample candidate with the highest quality. How to generate a multi-perspective event log.

12. In paragraph 9, The step of determining the event map among the plurality of candidate event maps based on the quality of the sample candidate event log is as follows: Providing the user with quality information of the sample candidate event log corresponding to the plurality of candidate event maps, and determining the candidate event map selected by the user as the event map. How to generate a multi-perspective event log.

13. A computer-readable recording medium characterized in that a program for executing the method of any one of claims 1 to 12 is recorded thereon.

14. Memory for storing data sources; and processor Including, The above processor, Extract metadata from the above data sources, Using the above metadata, create an event map and sample event logs, Calculate the quality of the above sample event log, If the quality value of the above sample event log is higher than the reference value, data is collected from the data source based on the above event map to generate a multi-view event log. A system that generates multi-perspective event logs.

15. In paragraph 14, The above processor, When generating the event map and the sample event log using the above metadata, Convert the above extracted metadata into a standardized entity relationship data model, Create a data catalog by connecting the above entity relationship data models, Infer the activity concept and timestamp concept from the above data catalog, Infer case concepts from the above data catalog, Generate the event map by associating the above activity concept, the above timestamp concept and the above case concept, Extracting sample data from the above data source, and extracting data from the sample data based on the above event map to generate the sample event log. A system that generates multi-perspective event logs.

16. In paragraph 15, The above processor, If at least one of the above activity concept, the above timestamp concept and the above case concept is not found, an event map is generated based on data quality, Generate multi-view event logs from the data source based on the event map generated based on the above data quality. A system that generates multi-perspective event logs.

17. In paragraph 14, The above processor, When calculating the quality of the above sample event log, Calculated by considering at least one of the case identifier ratio, trace mutation ratio, average ratio of unique activities per case, endpoint activity ratio, and start-end activity ratio of the above sample event log. A system that generates multi-perspective event logs.

18. In paragraph 14, The above processor, If the quality value of the above sample event log is below the reference value, an event map is generated based on the data quality, Generate multi-view event logs from the data source based on the event map generated based on the above data quality. A system that generates multi-perspective event logs.

Images