KR20240117743A - Method and system for DATA UTILIZATION MODIELING USING ARTIFICIAL INTELLIGENCE IN ENTERPRISE RESOURCE PLANNING ENVIRONMENT - Google Patents

Abstract

A data utilization modeling method and system using artificial intelligence in an enterprise resource management environment is disclosed. The data utilization modeling method includes the following steps: acquiring a database schema of a company's ERP (Enterprise Resource Planning) system, analyzing business objects of the ERP system, analyzing characteristics for each operating period of the ERP system, and It may include detecting a section in which set key characteristics appear, selecting learning data for the detected section, and generating a data utilization model for the company based on the selected learning data.

Description

Data utilization modeling method and system using artificial intelligence in an enterprise resource management environment {Method and system for DATA UTILIZATION MODIELING USING ARTIFICIAL INTELLIGENCE IN ENTERPRISE RESOURCE PLANNING ENVIRONMENT}

The explanation below is about the data utilization modeling method and system using artificial intelligence in the enterprise resource management environment. This study was supported by the Industry-Academic-Research Collaborative R&D Project of the Ministry of SMEs and Startups [S3245278].

Many companies are aware of the need to transition their corporate systems to the cloud, but are facing many concerns due to operational types and technical barriers. By mirroring the cloud-based innovations of our competitors, we are considering establishing a migration strategy that is reasonable, efficient, and cost-saving for our company.

However, despite switching to the cloud to save money and invest in business innovation, monthly expenses are increasing more than on-premise systems. As such, individual companies have the problem of not being able to accurately predict cloud usage due to insufficient cost-optimized design due to issues such as lack of technical capabilities.

Automation technology for essential data transfer and cost optimization of cloud conversion can replace important parts of existing cloud transfer projects, thereby resolving the lack of customer technology capabilities, reducing project costs (CapEx), and reducing cloud operating expenses (OpEx). You can. Cloud provisioning risks are increasing due to increased project execution period due to existing manual work and inaccurate infrastructure and data analysis. Therefore, the demand for customized solutions that can precisely extract data to eliminate data risks and reduce operating costs through accurate predictions based on artificial intelligence is increasing.

[Prior art document number]

Korean Patent Publication No. 10-2022-0083046

Provides a data utilization modeling method and system using artificial intelligence in an enterprise resource management environment.

A data utilization modeling method of a computer device including at least one processor, comprising: acquiring, by the at least one processor, a database schema of an enterprise resource planning (ERP) system; analyzing business objects of the ERP system by the at least one processor; analyzing characteristics of the ERP system for each operating period by the at least one processor; Detecting, by the at least one processor, a section in which preset main characteristics appear and selecting learning data for the detected section; and generating, by the at least one processor, a data utilization model of the company based on the selected learning data.

According to one aspect, the step of acquiring the database schema may include, if the ERP system is a general-purpose system or an open source system, acquiring existing data modeling information as the database schema or through the terminal of the data manager of the company. It may be characterized by receiving the database schema.

According to another aspect, the step of analyzing the business object includes analyzing key table and key value-based relationship information using the database schema, and analyzing the business object by at least one condition of the company's organization, data type, and object. It may be characterized by analyzing an object.

According to another aspect, the step of analyzing the characteristics for each operating period is characterized by analyzing the characteristics of the business object for each preset period according to at least one of short term, medium term, long term, and business transition of the company and assigning weight. can do.

According to another aspect, the step of selecting the learning data may be characterized by selecting learning data for a section in which preset main characteristics appear based on the characteristics of each operation period.

According to another aspect, the step of generating the company's data utilization model may be characterized by generating the data utilization model by learning a machine learning model through the selected training data.

According to another aspect, the data utilization modeling method further includes the step of performing a detailed analysis of a key business object by the at least one processor, and the step of performing the detailed analysis is performed by using an operating system or a backup mirror. The system may be used to extract data within a range permitted by the company for the main business object, and the data utilization model may be updated using the extracted data.

A computer program stored on a computer-readable recording medium is provided in conjunction with a computer device to execute the method on the computer device.

Provided is a computer-readable recording medium on which a program for executing the above method on a computer device is recorded.

It includes at least one processor implemented to execute computer-readable instructions, and obtains a database schema of a company's Enterprise Resource Planning (ERP) system using the at least one processor, and performs business operations of the ERP system. Analyze objects, analyze the characteristics of each operation period of the ERP system, detect sections in which preset key characteristics appear, select learning data for the detected sections, and determine the company's performance based on the selected learning data. A computer device is provided, characterized in that it generates a data utilization model.

According to embodiments of the present invention, a data utilization modeling method and system using artificial intelligence in an enterprise resource management environment can be provided.

1 is a diagram illustrating an example of a network environment according to an embodiment of the present invention.
Figure 2 is a block diagram showing an example of a computer device according to an embodiment of the present invention.
Figure 3 is a diagram showing an example of a general view of a data utilization modeling system according to an embodiment of the present invention.
Figure 4 is a diagram showing an example of selecting learning data to analyze business objects of an RDB-based enterprise information system in one embodiment of the present invention.
Figure 5 is a flowchart showing an example of a data utilization modeling method according to an embodiment of the present invention.
Figure 6 is a diagram showing an example of de-identifying and compressing learning data in one embodiment of the present invention.

Since the present invention can make various changes and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all changes, equivalents, and substitutes included in the spirit and technical scope of the present invention. While describing each drawing, similar reference numerals are used for similar components.

Terms such as first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, a first component may be named a second component, and similarly, the second component may also be named a first component without departing from the scope of the present invention. The term and/or includes any of a plurality of related stated items or a combination of a plurality of related stated items.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

The terms used in this application are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

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 technical field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted in an ideal or excessively formal sense unless explicitly defined in the present application. No.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

The data utilization modeling system according to embodiments of the present invention may be implemented by at least one computer device, and the data utilization modeling method according to embodiments of the present invention may be implemented by at least one computer device included in the data utilization modeling system. It can be performed through . The computer program according to an embodiment of the present invention may be installed and driven in the computer device, and the computer device may perform the data utilization modeling method according to the embodiment of the present invention under the control of the driven computer program. The above-described computer program can be combined with a computer device and stored in a computer-readable recording medium to execute the data utilization modeling method on the computer device.

1 is a diagram illustrating an example of a network environment according to an embodiment of the present invention. The network environment in FIG. 1 shows an example including a plurality of electronic devices 110, 120, 130, and 140, a plurality of servers 150 and 160, and a network 170. Figure 1 is an example for explaining the invention, and the number of electronic devices or servers is not limited as in Figure 1. In addition, the network environment in FIG. 1 only explains one example of environments applicable to the present embodiments, and the environment applicable to the present embodiments is not limited to the network environment in FIG. 1.

The plurality of electronic devices 110, 120, 130, and 140 may be fixed terminals or mobile terminals implemented as computer devices. Examples of the plurality of electronic devices 110, 120, 130, and 140 include smart phones, mobile phones, navigation devices, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), and PMPs (Portable Multimedia Players). ), tablet PC, etc. For example, in FIG. 1, the shape of a smartphone is shown as an example of the electronic device 110. However, in embodiments of the present invention, the electronic device 110 actually communicates with other devices through the network 170 using a wireless or wired communication method. It may refer to one of various physical computer devices capable of communicating with electronic devices 120, 130, 140 and/or servers 150, 160.

The communication method is not limited, and may include not only a communication method utilizing a communication network that the network 170 may include (for example, a mobile communication network, wired Internet, wireless Internet, and a broadcast network), but also short-range wireless communication between devices. For example, the network 170 may include a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), and a broadband network (BBN). , may include one or more arbitrary networks such as the Internet. Additionally, the network 170 may include any one or more of network topologies including a bus network, star network, ring network, mesh network, star-bus network, tree or hierarchical network, etc. Not limited.

Each of the servers 150 and 160 is a computer device or a plurality of computers that communicate with a plurality of electronic devices 110, 120, 130, 140 and a network 170 to provide commands, codes, files, content, services, etc. It can be implemented with devices. For example, the server 150 may be a system that provides at least one service to a plurality of electronic devices 110, 120, 130, and 140 connected through the network 170.

Figure 2 is a block diagram showing an example of a computer device according to an embodiment of the present invention. Each of the plurality of electronic devices 110, 120, 130, and 140 described above or each of the servers 150 and 160 may be implemented by the computer device 200 shown in FIG. 2.

As shown in FIG. 2, this computer device 200 may include a memory 210, a processor 220, a communication interface 230, and an input/output interface 240. The memory 210 is a computer-readable recording medium and may include a non-permanent mass storage device such as random access memory (RAM), read only memory (ROM), and a disk drive. Here, non-perishable large-capacity recording devices such as ROM and disk drives may be included in the computer device 200 as a separate permanent storage device that is distinct from the memory 210. Additionally, an operating system and at least one program code may be stored in the memory 210. These software components may be loaded into the memory 210 from a computer-readable recording medium separate from the memory 210. Such separate computer-readable recording media may include computer-readable recording media such as floppy drives, disks, tapes, DVD/CD-ROM drives, and memory cards. In another embodiment, software components may be loaded into the memory 210 through the communication interface 230 rather than a computer-readable recording medium. For example, software components may be loaded into memory 210 of computer device 200 based on computer programs installed by files received over network 170.

The processor 220 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Commands may be provided to the processor 220 by the memory 210 or the communication interface 230. For example, processor 220 may be configured to execute received instructions according to program code stored in a recording device such as memory 210.

The communication interface 230 may provide a function for the computer device 200 to communicate with other devices (eg, the storage devices described above) through the network 170. For example, a request, command, data, file, etc. generated by the processor 220 of the computer device 200 according to a program code stored in a recording device such as memory 210 is transmitted to the network ( 170) and can be transmitted to other devices. Conversely, signals, commands, data, files, etc. from other devices may be received by the computer device 200 through the communication interface 230 of the computer device 200 via the network 170. Signals, commands, data, etc. received through the communication interface 230 may be transmitted to the processor 220 or memory 210, and files, etc. may be stored in a storage medium (as described above) that the computer device 200 may further include. It can be stored as a permanent storage device).

The input/output interface 240 may be a means for interfacing with the input/output device 250. For example, input devices may include devices such as a microphone, keyboard, or mouse, and output devices may include devices such as displays and speakers. As another example, the input/output interface 240 may be a means for interfacing with a device that integrates input and output functions into one, such as a touch screen. The input/output device 250 may be configured as a single device with the computer device 200.

Additionally, in other embodiments, computer device 200 may include fewer or more components than those of FIG. 2 . However, there is no need to clearly show most prior art components. For example, the computer device 200 may be implemented to include at least some of the input/output devices 250 described above, or may further include other components such as a transceiver, a database, etc.

Figure 3 is a diagram showing an example of a general view of a data utilization modeling system according to an embodiment of the present invention. The data utilization modeling method and system according to one embodiment understands the characteristics of a system composed of RDB (Relational Database) in the enterprise service area and uses artificial intelligence to predict the increase in data, thereby predicting the volume of the ERP (Enterprise Resource Planning) system. We can provide a cloud-optimized provisioning solution to establish a multi-cloud distributed linkage strategy in response to the increase. For example, the embodiment of FIG. 3 shows an enterprise information system 310, a data utilization modeling system 320, and a plurality of cloud systems 330.

The data utilization modeling system 320 can automatically extract essential information for data transfer of the enterprise information system 310, and predicts multi-cloud (for example, machine learning engine 321) based on artificial intelligence (e.g., machine learning engine 321). For example, a strategy for distributing and linking data to a plurality of cloud systems 330 can be created, and the generated strategy can be presented to the visualization unit 322.

At this time, the data utilization modeling system 320 analyzes the data utilization model according to the characteristics of the RDB based on the business object analysis data set information of the ERP system (for example, the enterprise information system 310) and performs machine learning-based learning on it. Through this, it is possible to develop a sophisticated future prediction model for a company's activities.

Due to the nature of a company's operation, data in the related RDB of the ERP system increases as the organization operates. As the data is expanded and utilized depending on the relationship, related business is likely to increase in the future. For example, if specific data is created at a specific point in time, the data volume may increase through related business activities at a later point in time, and the data management subject may change through organizational reorganization or reorganization in response to the increase in specific volume. You can.

Figure 4 is a diagram showing an example of selecting learning data to analyze business objects of an RDB-based enterprise information system in one embodiment of the present invention. Figure 4 shows that learning data can be selected and utilized by organization, period, object, and type. However, in reality, there are limitations to a company's use of data in the selection process of learning data due to the presence of sensitive data (cost, customer information, etc.) and personal information (HR (Human Resource) and personnel-related data). It is possible to predict data utilization using only the data volume or log information on the RDB, or to utilize data by using anonymization and/or de-identification technology, but companies generally do not allow this. Therefore, a technique that predicts the data utilization model of the main table is required by using the data utilization form of the tables connected based on relationships rather than using the table information containing the main information.

Figure 5 is a flowchart showing an example of a data utilization modeling method according to an embodiment of the present invention. The data utilization modeling method according to this embodiment can be performed by the computer device 200 previously described with reference to FIG. 2. At this time, the processor 220 of the computer device 200 may be implemented to execute control instructions according to the code of an operating system included in the memory 210 or the code of at least one computer program. Here, the processor 220 causes the computer device 200 to perform steps 510 to 560 included in the method of FIG. 5 according to control instructions provided by code stored in the computer device 200. can be controlled.

In step 510, the computer device 200 may acquire a database schema of the enterprise ERP system. For example, the computer device 200 may receive the database schema of the ERP system currently being operated by the company through the terminal of the company's data manager. As another example, the computer device 200 may obtain and utilize existing data modeling information as a database schema in the case of general-purpose systems and open source systems such as SAP and Oracle. However, when receiving data from a company, information about the modules in use may be requested to reduce the amount of analysis. In other words, in the case of a general-purpose or open source system, the amount of analysis can be reduced by utilizing existing data modeling information for the system, but also using information about currently used modules provided by the company.

In step 520, the computer device 200 may analyze business objects of the ERP system. At this time, the computer device 200 may analyze relationship information based on key tables and key values using a database schema, and analyze business objects according to at least one condition among company organization, data type, and object. For example, the computer device 200 may analyze a business object based on the database schema of the ERP system of the company to be analyzed. At this time, key table and key value-based relationship information is used to analyze the business object using the database schema information. can be analyzed. In this case, the computer device 200 may analyze business objects by corporate organization, data type, and/or object. For organization-specific analysis, the computer device 200 can analyze business objects based on data access range and utilization according to organizational structure and member information so as to model data utilization patterns according to the company's organizational structure. For analysis by data type, the computer device 200 may analyze business objects based on data provided as code defined and used in an existing ERP system or as separate structured data. For object-specific analysis, the computer device 200 may analyze business objects based on business object units (modules) classified in the ERP system. In the case of these analyses, similarity can be determined using a previously learned model based on the industry classification, industry, or industry type of existing companies. In these cases, when prediction is difficult due to insufficient learning data, such as for new companies or companies that have undergone physical division, some of the characteristic information of the data model can be utilized.

In step 530, the computer device 200 may analyze the characteristics of the ERP system for each operating period. When analysis is performed for each characteristic of a business object, the computer device 200 may reflect the period-specific characteristics of the corresponding characteristics. The characteristics of business objects may appear differently for each preset period depending on the short-term, medium-term, long-term, and the company's business transition, and may have a high relationship with the company's management strategy, so the relevant information is used to analyze the characteristics of each period, Weights can be assigned. In other words, the computer device 200 may assign weights by analyzing the characteristics of business objects for each preset period according to at least one of the short term, medium term, long term, and business transition of the company.

In step 540, the computer device 200 may detect key characteristic sections and select learning data. When a section with preset key characteristics (one or more operating periods with key characteristics) appears, the computer device 200 may select data for the section as learning data. Because corporate data may include sensitive data for business and privacy data such as personal information, there is a limitation in that all data cannot be used for learning. Therefore, a strategy for constructing learning data through sampling of characteristic sections and areas is needed. In particular, in the case of security-sensitive data, only the volume of the data table can be extracted and used for learning.

The final learning data may be selected through user review of the selected learning data. The user review process may be conducted to confirm whether the selected learning data matches the characteristics of the data to be actually analyzed.

In step 550, the computer device 200 may perform a detailed analysis of major business objects. The computer device 200 can analyze data using an operating system or a mirror system for backup, and extract and utilize data within the permitted range. In addition, through job registration in batch form, analysis can be performed periodically during the company's non-business hours, allowing the model to be continuously updated in the future. In other words, the computer device 200 can further extract data within the range permitted by the company as learning data through detailed analysis of key business objects. Thereafter, the data utilization model created based on the learning data in step 560 may be updated as it is re-trained using the training data extracted in step 550.

In step 560, the computer device 200 may create a data utilization model for the company based on the learning data. For example, the computer device 200 may learn a machine learning model through training data to create a data utilization model. Afterwards, the generated data utilization model is used to identify and understand the company's data utilization pattern, and based on this, establish a data utilization strategy (backup and archive, distribution strategy, etc.) and/or reduce TCO (Total Cost of Ownership). It can be used in strategy.

Figure 6 is a diagram showing an example of de-identifying and compressing learning data in one embodiment of the present invention. When analyzing business objects in an enterprise information system, learning data compression is required to improve performance and increase the speed of learning data collection. Additionally, in addition to improving the learning data collection speed, compression suitable for artificial intelligence learning is required. Because relationship information exists in existing schema information, schema information can be utilized, and row-based data compression centered on the relationship key of the data time series data table can be utilized. In the case of ERP data when learning artificial intelligence, the columns are fixed, and there is a high probability that data from a certain period of time will be used as parameters for learning data. Figure 6 shows an example in which data included in the database of an enterprise information system is compressed into relationship-based raw data units and extracted as relationship business objects.

Accordingly, the computer device 200 may first select a data table for learning according to the selection of a major business object to be used for learning. At this time, the computer device 200 may filter the data table based on corporate policy according to the sensitivity and confidentiality of the data. Additionally, the computer device 200 may select a relationship-based data table based on the data table filtering results. Thereafter, the computer device 200 may process de-identification of data by analyzing the characteristics of the data table. De-identification may include anonymization.

As such, according to embodiments of the present invention, a data utilization modeling method and system using artificial intelligence in an enterprise resource management environment can be provided.

The device described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices and components described in the embodiments include a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), and a programmable logic unit (PLU). It may be implemented using one or more general-purpose or special-purpose computers, such as a logic unit, microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. The software and/or data may be embodied in any type of machine, component, physical device, computer storage medium or device for the purpose of being interpreted by or providing instructions or data to the processing device. there is. Software may be distributed over networked computer systems and stored or executed in a distributed manner. 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 instructions that can be executed through various computer means and recorded on a computer-readable medium. At this time, the medium may continuously store a computer-executable program, or temporarily store it for execution or download. In addition, the medium may be a variety of recording or storage means in the form of a single or several pieces of hardware combined. It is not limited to a medium directly connected to a computer system and may be distributed over a network. Examples of media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, And there may be something configured to store program instructions, including ROM, RAM, flash memory, etc. Additionally, examples of other media include recording or storage media managed by app stores that distribute applications, sites or servers that supply or distribute various other software, etc.

As described above, although the embodiments have been described with limited examples and drawings, various modifications and variations can be made by those skilled in the art from the above description. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

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

Claims (10)

In a data utilization modeling method of a computer device including at least one processor,
Obtaining, by the at least one processor, a database schema of an enterprise resource planning (ERP) system;
analyzing business objects of the ERP system by the at least one processor;
analyzing characteristics of the ERP system for each operating period by the at least one processor;
Detecting, by the at least one processor, a section in which preset main characteristics appear and selecting learning data for the detected section; and
Generating, by the at least one processor, a data utilization model of the company based on the selected learning data.
Data utilization modeling method including. According to paragraph 1,
The step of obtaining the database schema is,
If the ERP system is a general-purpose system or an open source system, a data utilization modeling method comprising acquiring existing data modeling information as the database schema or receiving the database schema through a terminal of a data manager of the company. . According to paragraph 1,
The step of analyzing the business object is,
A data utilization modeling method comprising analyzing relationship information based on key tables and key values using the database schema, and analyzing the business object according to at least one condition among the company's organization, data type, and object. According to paragraph 1,
The step of analyzing the characteristics of each operating period is,
A data utilization modeling method characterized by analyzing the characteristics of the business object and assigning weights for each preset period according to at least one of the short term, medium term, long term, and business transition of the company. According to paragraph 1,
The step of selecting the learning data is,
A data utilization modeling method characterized by selecting learning data for a section in which preset main characteristics appear based on the characteristics of each operation period. According to paragraph 1,
The step of creating the company’s data utilization model is,
A data utilization modeling method characterized by generating the data utilization model by learning a machine learning model through the selected learning data. According to paragraph 1,
Performing detailed analysis on key business objects by the at least one processor
It further includes,
The steps for conducting the detailed analysis are:
Extracting data within the scope permitted by the company for the major business objects using an operating system or backup mirror system, and updating the data utilization model using the extracted data.
A data utilization modeling method characterized by A computer program stored in a computer-readable recording medium in combination with a computer device to cause the computer device to execute the method of any one of claims 1 to 7. A computer-readable recording medium recording a computer program for executing the method of any one of claims 1 to 7 on a computer device. At least one processor implemented to execute computer readable instructions
Including,
By the at least one processor,
Obtain the database schema of the company's ERP (Enterprise Resource Planning) system,
Analyzing business objects of the ERP system,
Analyzing the characteristics of the ERP system by operating period,
Detect sections where preset key characteristics appear, select learning data for the detected sections, and
Creating a data utilization model for the company based on the selected learning data
A computer device characterized by a.

Images