Data monitoring method, computer program, and information processing system

Abstract

To provide a data monitoring method to be executed by at least one processor.SOLUTION: A data monitoring method includes the steps of: receiving a first user input for selecting at least one data set as a monitoring target from among a plurality of data sets; analyzing at least one data set to output a recommended monitoring type for at least one data set selected from a plurality of monitoring types; receiving a second user input for determining a monitoring type for at least one data set selected from among a plurality of monitoring types; and outputting analysis results for data included in the at least one data set calculated based on the selected at least one data set and the determined monitoring type.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a data monitoring method and system. Specifically, it relates to a data monitoring method and system that provide analysis results based on a monitoring type for data included in a dataset selected as a monitoring target.

Background Art

[0002] Recently, the importance of data such as big data has been emphasized, and the amount of data managed by each company and the like has been increasing geometrically. In some companies, hundreds of millions to billions of pieces of data are loaded every day, and the loaded data is used for services and decision-making through secondary or tertiary processing. However, due to the rapid increase in the amount of data and the diversification of data types, it is extremely difficult to monitor data to detect abnormal phenomena.

[0003] In addition, in order to confirm the presence or absence of data anomalies, a program must be designed for each data every time, or a query must be generated, resulting in the problem of duplicate use of resources.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The present disclosure provides a data monitoring method and system for solving the above problems.

Means for Solving the Problems

[0006] This disclosure can be embodied in a variety of ways, including methods, systems (apparatus) or computer programs, and storage media for storing such computer programs.

[0007] According to one embodiment of the present disclosure, a data monitoring method performed by at least one processor included in a computing device includes the steps of: receiving a first user input that selects at least one dataset to be monitored from among a plurality of datasets; analyzing at least one dataset and outputting a recommended monitoring type from among a plurality of monitoring types for the at least one dataset selected by the first user input; receiving a second user input that determines a monitoring type from among a plurality of monitoring types for the at least one dataset selected by the first user input; and outputting an analysis result for the data included in at least one dataset calculated based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input.

[0008] According to one embodiment of the present disclosure, the multiple monitoring types include basic, numeric, and categorical types.

[0009] According to one embodiment of the present disclosure, the step of receiving a first user input includes receiving a first user input that selects at least one dataset to be monitored from among a plurality of datasets of other computing devices that can be connected to from the computing device.

[0010] According to one embodiment of the present disclosure, the step of outputting a recommended monitoring type includes the step of outputting a recommended monitoring type for at least one dataset based on metadata for at least one dataset selected in a first user input.

[0011] According to one embodiment of the present disclosure, the step of outputting a recommended monitoring type includes the step of outputting a categorical type as the recommended monitoring type if the distinct item count in the data included in at least one dataset is less than or equal to a default threshold.

[0012] According to one embodiment of the present disclosure, the further step includes outputting at least one of the total number of items or the number of null items included in at least one dataset.

[0013] According to one embodiment of the present disclosure, the step of outputting analysis results includes, if the monitoring type determined by the second user input is of the general type, the step of outputting at least one of the duplicate item count or the unique item count in at least one dataset.

[0014] According to one embodiment of the present disclosure, the step of outputting analysis results includes, if the monitoring type determined by the second user input is of a numerical type, the step of outputting at least one of the minimum, maximum, mean, standard deviation, or quartile of the items contained in at least one dataset.

[0015] According to one embodiment of the present disclosure, the step of outputting analysis results includes, if the monitoring type determined by the second user input is categorical, the step of outputting the percentage of the number of items in each category to the total number of items in at least one dataset.

[0016] According to one embodiment of the present disclosure, when an anomaly is detected in at least one dataset, an anomaly detection notification is sent to a user terminal associated with at least one dataset.

[0017] According to one embodiment of the present disclosure, an anomaly detection notification is sent to the user terminal when it is detected that a first anomaly has occurred in which the total number of items in at least one dataset increases or decreases faster than a predetermined first speed range.

[0018] According to one embodiment of the present disclosure, an anomaly detection notification is sent to the user terminal when a second anomaly is detected in which the total number of items in at least one dataset increases more slowly than a predetermined second speed range or does not increase at all within a predetermined time.

[0019] According to one embodiment of the present disclosure, if the determined monitoring type is numerical, an anomaly detection notification is sent to the user terminal when it is detected that a third anomaly has occurred in which at least one of the minimum, maximum, mean, standard deviation, or quartiles of an item included in at least one dataset increases or decreases faster than a predetermined third speed range.

[0020] According to one embodiment of the present disclosure, if the determined monitoring type is categorical, an anomaly detection notification is sent to the user terminal when it is detected that a fourth anomaly has occurred in which the number of categories included in at least one dataset increases or decreases.

[0021] According to one embodiment of the present disclosure, a second user input and at least one dataset are input to a machine learning model that has been trained to detect the occurrence of an anomaly based on input data and information related to the input data, and an anomaly detection notification is sent to the user terminal when an anomaly is detected.

[0022] According to an embodiment of the present disclosure, a data monitoring method performed by at least one processor includes receiving a first user input for selecting at least one data set as a monitoring target from a plurality of data sets, receiving a second user input for determining a monitoring type for at least one data set selected by the first user input from among a plurality of monitoring types, generating analysis logic based on at least one data set selected by the first user input and the monitoring type determined by the second user input, and applying the analysis logic to at least one data set to calculate an analysis result for the data included in at least one data set.

[0023] According to an embodiment of the present disclosure, the plurality of monitoring types include a general type, a numerical type, and a category type.

[0024] According to an embodiment of the present disclosure, a computer program including instruction codes for performing each step of the above-described monitoring method is provided.

[0025] An information processing system according to an embodiment of the present disclosure includes a communication module, a memory, and at least one processor coupled to the memory and configured to execute at least one computer-readable program included in the memory. The at least one program includes instruction codes for receiving a first user input for selecting at least one data set as a monitoring target from a plurality of data sets, receiving a second user input for determining a monitoring type for at least one data set selected by the first user input from among a plurality of monitoring types, generating analysis logic based on at least one data set selected by the first user input and the monitoring type determined by the second user input, and substituting the analysis logic into at least one data set to calculate an analysis result for the data included in at least one data set.

Advantages of the Invention

[0026] According to some embodiments of the present disclosure, by generating analysis logic based on information regarding a data set to be monitored and information regarding a monitoring type, monitoring of the data set can be performed without the user directly designing a program or generating a query.

[0027] According to some embodiments of the present disclosure, the integrity of data can be ensured by detecting an abnormality in a data set and transmitting an abnormality detection notification to a user terminal associated with the data set.

[0028] According to some embodiments of the present disclosure, various user experiences can be provided by visualizing and providing analysis results of a data set in various forms.

[0029] The effects of the present disclosure are not limited to these, and other effects not mentioned, etc., will be clearly understood by those having ordinary knowledge in the technical field to which the present disclosure pertains from the description of the claims (hereinafter referred to as "those skilled in the art").

Brief Description of the Drawings

[0030] Embodiments of the present disclosure, etc., will be described based on the following attached drawings. Here, similar reference numerals indicate similar elements, but are not limited thereto. [Figure 1] It is a diagram showing a detailed configuration of a data monitoring system according to an embodiment of the present disclosure. [Figure 2] It is a schematic diagram showing a configuration in which an information processing system is connected to be communicable with a plurality of user terminals in order to provide data monitoring according to an embodiment of the present disclosure. [Figure 3] It is a block diagram showing an internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure. [Figure 4] It is a diagram showing an example of a data monitoring setting screen according to an embodiment of the present disclosure. [Figure 5]This figure shows an example of the monitoring type setting screen for each dataset in the dataset catalog according to one embodiment of the present disclosure. [Figure 6] This figure shows an example of a screen displaying the results of a comprehensive monitoring analysis according to one embodiment of the present disclosure. [Figure 7] This figure shows an example of a screen displaying general monitoring analysis results according to one embodiment of the present disclosure. [Figure 8] This figure shows an example of a screen displaying numerical monitoring analysis results according to one embodiment of the present disclosure. [Figure 9] This figure shows an example of a screen displaying categorical monitoring analysis results according to one embodiment of the present disclosure. [Figure 10] This flowchart shows a data monitoring method according to one embodiment of the present disclosure. [Figure 11] This figure shows an example of an artificial neural network model according to one embodiment of the present disclosure. [Modes for carrying out the invention]

[0031] The specific details for implementing this disclosure will be described below in detail based on the attached drawings. However, in the following explanation, specific descriptions of publicly known functions and configurations will be omitted if there is a risk of unnecessarily obscuring the gist of this disclosure.

[0032] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Furthermore, in the following descriptions of embodiments, redundant descriptions of identical or corresponding components may be omitted. However, the omission of a description of a component does not mean that such a component is not included in the embodiment.

[0033] The advantages and features of the disclosed embodiments, and the methods for achieving them, will become clear with reference to the embodiments described below, based on the accompanying drawings. However, this disclosure is not limited to the embodiments disclosed below and can be embodied in a variety of different forms. The embodiments disclosed below are provided solely to enable those skilled in the art to accurately understand the content of the inventions related to this disclosure.

[0034] This disclosure provides a brief explanation of the terminology used and a detailed description of the embodiments of the disclosure. The terminology used in this disclosure has been selected to the greatest extent possible from commonly used terms, taking into account the function of the disclosure; however, this may change due to the intent of engineers in the relevant field, case law, the emergence of new technologies, etc. In certain cases, the applicant may have arbitrarily selected terms, the meanings of which will be described in detail in the description of the invention. Therefore, the terminology used in this disclosure does not simply mean names; the meaning of these terms is defined based on the overall content of this disclosure.

[0035] In this disclosure, unless explicitly specified in the context, singular expressions may include plural expressions, and plural expressions may include singular expressions. Throughout the specification, where a part "includes" a component, this does not exclude other components, unless otherwise stated, and may further include other components.

[0036] Furthermore, the terms “module” or “part” as used in this specification refer to software or hardware components, and a “module” or “part” performs a certain role. However, the meaning of “module” or “part” is not limited to software or hardware. A “module” or “part” may be configured to reside on an addressable storage medium, or to regenerate one or more processors. Thus, as an example, a “module” or “part” may include components such as software components, object-oriented software components, class components, task components, as well as at least one of processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. Components and “modules” or “parts” may be further combined with a smaller number of components and “modules” or “parts” to provide internal functionality, or further separated into additional components and “modules” or “parts.”

[0037] According to one embodiment of the present disclosure, a “module” or “part” may be embodied in a processor and memory. “Processor” should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some environments, “processor” may also refer to application-specific semiconductors (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), and the like. “Processor” may also refer to combinations of processing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled with a DSP core, or any other such combination. “Memory” should also be broadly interpreted to include any electronic component capable of storing electronic information. The term "memory" can also refer to a variety of processor-readable media, such as RAM (Random Access Memory), ROM (Read Only Memory), NVRAM (Non-Volatile Random Access Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic or optical data storage devices, and registers. When a processor can read information from or record information into memory, the memory is said to be in electronic communication with the processor. Memory integrated into a processor is in electronic communication with the processor.

[0038] In this disclosure, “System” may include, but is not limited to, at least one of a server device and a cloud device. For example, a system may consist of one or more server devices. Another example is that a system may consist of one or more cloud devices. Yet another example is that a system may consist of both a server device and a cloud device. Yet another example is that a system may refer to a device that provides data monitoring.

[0039] In this disclosure, a series of actions to detect the presence and / or accuracy of data may be referred to as "data monitoring." Alternatively, "data monitoring" may mean data profiling, which involves aggregating information related to data and / or statistics on the data. For example, the action of aggregating the total number of data items in a dataset contained in a database may be referred to as data monitoring.

[0040] In this disclosure, “item” can refer to a value within a dataset, or it can refer to a collection of data treated as a single unit. For example, an item can refer to a record, row, or tuple in a database, or to a single value within a record, row, or tuple.

[0041] In this disclosure, “basic statistics” can refer to values ​​that represent fundamental features that describe or characterize the data. For example, “basic statistics” may include the minimum, maximum, mean, standard deviation, and quartiles of data values ​​in a dataset.

[0042] In this disclosure, “User” may refer to a user who uses an application that provides data monitoring, or a user account of an application that provides data monitoring. Here, a user account may refer to an account generated and used by an application that provides data monitoring, or data associated therewith. In this disclosure, “User” may mean “User Account,” and “User Account” may mean “User.”

[0043] In this disclosure, “machine learning model” can include any model used to infer the ground truth for a given input. According to one embodiment, the machine learning model can include an artificial neural network model comprising an input layer, a number of hidden layers, and an output layer, where each layer can include multiple nodes.

[0044] In this disclosure, “Display device” may refer to any display device associated with a computing device. “Display device” may refer to any display device that is controlled by or provided by a computing device and is capable of displaying any information / data.

[0045] In this disclosure, “each of the A” or “each of the A” may refer to each of all components included in the A, or to each of some of the components included in the A. For example, each of one or more datasets may refer to each of all datasets included in one or more datasets, or to each of some of the datasets included in one or more datasets.

[0046] Figure 1 shows a detailed configuration of a data monitoring system 100 according to one embodiment of the present disclosure. The data monitoring system 100 may include, but is not limited to, a management system 120, an aggregation system 160, a visualization system 180, and an anomaly detection system 190, and some of the configurations of the illustrated data monitoring system 100 may be omitted or other configurations may be included. Also, although the management system 120, aggregation system 160, visualization system 180, and anomaly detection system 190 are shown as separate units in Figure 1, the system is not limited to this, and any combination of two or more of the management system 120, aggregation system 160, visualization system 180, and anomaly detection system 190 can operate as a single unit. Additionally or alternatively, each of the management system 120, aggregation system 160, visualization system 180, and anomaly detection system 190 can be embodied as two or more subunits.

[0047] Furthermore, the data monitoring system 100 includes a data metadata DB 130 and a source data DB 140, as well as a backend database 150 and a data monitoring indicator storage 170. Although each configuration is shown separately, this is merely an example, and the storage media used by the data monitoring system 100 can consist of one or any number of them. Also, Figure 1 shows the data metadata DB 130 and source data DB 140 as multiple databases, which can mean that the data metadata DB 130 and source data DB 140 consist of multiple databases located in different locations. However, the data metadata DB 130 and source data DB 140 can also consist of a single database.

[0048] User 110 can select the data to be monitored. In one embodiment, User 110 can select at least one dataset from a plurality of datasets to be monitored. For example, a dataset may be a database, a DB table, or a data column. User 110 can determine the monitoring type for the selected dataset to be monitored. In another embodiment, the user can select at least one dataset from a plurality of datasets on other computing devices that are accessible from the user's computing device to be monitored. Multiple monitoring types may include, but are not limited to, basic, numeric, and categorical types.

[0049] Of the various monitoring types, the general type can refer to one that aggregates at least one of the following: duplicate item count or distinct item count in a dataset. Duplicate item count can refer to the number of duplicate items when there are multiple items in the dataset that have the same value. For example, if the dataset contains seven items with the values ​​{2,2,3,3,3,5,6}, there are two instances of "2" and three instances of "3", so the duplicate item count can be aggregated to 5. In other embodiments, the duplicate item count can indicate the number of duplicate items for a specific value in the dataset. For example, if system 100 is set to aggregate the duplicate item count for the value "2", the duplicate item count can be aggregated to 2. Distinct item count can refer to the number of items from which duplicate values ​​have been removed. In the example dataset above, if duplicate values ​​are removed, the dataset contains the values ​​{2,3,5,6}, so the distinct item count can be aggregated to 4.

[0050] A numerical type can refer to a type of data that aggregates basic statistics from a dataset. For example, a numerical type can refer to a type of data that aggregates at least one of the minimum, maximum, mean, standard deviation, or quartiles of the items included in a dataset.

[0051] The categorical type can refer to one type of statistical indicator used to calculate a dataset. In one example, the categorical type can refer to a type of calculation that aggregates the percentage of items in each category relative to the total number of items. For example, in the example dataset above, the values ​​corresponding to each category are "2", "3", "5", and "6", respectively, and the number of items in each category is 2, 3, 1, and 1, respectively. Therefore, the percentages for each category can be calculated as 28.57%, 42.86%, 14.29%, and 14.29%.

[0052] Monitoring types are not limited to the examples described above and may include other types. The functions of the monitoring types described above are illustrative, and what is described as being aggregated or monitored by each monitoring type can be performed by other monitoring types. Furthermore, monitoring types may not be clearly divided into multiple types such as general, numerical, and categorical, but rather configured to perform the aforementioned functions as a single, integrated configuration. Alternatively, there may be different types of monitoring types that appropriately distribute and perform the aforementioned functions in other ways.

[0053] The management system 120 can refer to a system configured to allow user 110 to select a dataset to monitor. In one embodiment, the management system 120 can provide user 110 with a catalog of databases that user 110 can select to monitor. Depending on user 110's selection of a specific database, the management system 120 can provide a catalog of DB tables within that database. Next, depending on user 110's selection of a specific DB table, the management system 120 can provide user 110 with a catalog of data columns contained within that DB table. The management system 120 can receive catalogs, associated information / data, etc., from the source data DB 140 in order to provide user 110 with the selectable database catalogs, DB table catalogs within those databases, data column catalogs, etc. Subsequently, user 110 can select data columns to monitor from the data column catalog and determine the monitoring type. For this purpose, the management system 120 can receive information about the monitoring type associated with the received data from the data metadata DB 130.

[0054] The management system 120 can analyze the dataset and provide the user 110 with a recommended monitoring type from among several monitoring types. In one embodiment, the management system 120 can provide the user 110 with a recommended monitoring type based on the dataset's metadata. To this end, the management system 120 can receive the dataset's metadata from the data metadata DB 130. For example, if the dataset's data type is numeric, the management system 120 can provide a numeric type as the recommended monitoring type, and if it is character type, it can provide a general type or a categorical type. In another embodiment, the management system 120 can provide a categorical type as the recommended monitoring type if the number of unique items in the data included in the dataset is below a predetermined threshold. In yet another embodiment, the management system 120 can provide the user with a recommended monitoring type by inputting the dataset and the dataset's metadata into a machine learning model that has been trained to derive a recommended monitoring type based on the input data and information associated with the input data.

[0055] The management system 120 can provide user 110 with information related to datasets that user 110 can select, via the user terminal. The management system 120 receives data metadata, including information related to datasets, from the data metadata DB 130, and can provide at least a portion of the data metadata to user 110 via the user terminal, so that user 110 can select at least one dataset to monitor. Specifically, the data metadata can include information related to each dataset contained in the data, i.e., any information that indicates or characterizes the dataset. For example, it can include, but is not limited to, the name of the dataset, data type, generation time, storage location, creator, modification date, modifier, and size.

[0056] The backend DB 150 may store information related to the dataset to be monitored selected by the user 110, or information related to the monitoring type, via the management system 120. For example, the backend DB 150 may store information such as the name and storage location of the database, DB table, or data column selected by the user 110 for monitoring, and information related to the monitoring type for the selected database, etc.

[0057] The aggregation system 160 can calculate analysis results for the monitoring targets selected by the user 110. To calculate the analysis results, the aggregation system 160 can receive information related to the selected dataset and information related to the monitoring type for said dataset from the management system 120 or the backend DB 150. Additionally or alternatively, the aggregation system 160 can receive the datasets related to the selected monitoring targets from the source data DB 140.

[0058] The aggregation system 160 can generate analysis logic based on the received information. For example, the aggregation system 160 can be configured to generate database queries based on the received information.

[0059] The aggregation system 160 can apply the generated analysis logic to the monitored dataset to calculate the analysis results. Here, the analysis results can include any information that describes or characterizes the monitored dataset. For example, it can include, but is not limited to, the number of duplicate items, the number of unique items, the minimum, maximum, mean, standard deviation or quartiles of items included in the dataset, and the percentage of items in each category relative to the total number of items. Additionally or alternatively, the aggregation system 160 can calculate the total number of items or the number of null items as an analysis result. The aggregation system 160 can store the calculated analysis results in the data monitoring indicator storage 170 or provide them to the visualization system 180.

[0060] In one embodiment, the aggregation system 160 can calculate analysis results for the monitored dataset at predetermined time intervals (e.g., one day). In another embodiment, the aggregation system 160 can calculate analysis results in response to sensing the occurrence of changes in metadata for at least one dataset in the data metadata DB 130 and / or in at least one dataset in the source data DB 140.

[0061] The visualization system 180 can provide the user 110 with a visualized screen based on the data aggregated by the aggregation system 160 via the user terminal. The specific content of the screen that the visualization system 180 provides to the user 110 will be described in detail in Figures 6 to 9.

[0062] The anomaly detection system 190 can detect anomalies in the monitored dataset based on the analysis results aggregated by the aggregation system 160. As shown in the figure, the anomaly detection system 190 can receive the analysis results from the data monitoring indicator storage 170. Additionally or alternatively, the anomaly detection system 190 can be configured to directly receive the analysis results from the aggregation system 160.

[0063] In one embodiment, after detecting an anomaly in a dataset, the anomaly detection system 190 can send an anomaly detection notification related to the detected anomaly to a user terminal associated with the dataset. Here, the user terminal associated with the dataset may be the terminal of the administrator or person in charge of the dataset, and information on each user terminal associated with a dataset may be pre-stored in the backend DB 150. The anomaly detection notification sent by the anomaly detection system 190 can include any information related to the anomaly detection. For example, it may include, but is not limited to, information regarding the name of the dataset where the anomaly occurred, its storage location, its position in the data hierarchy structure, the time of anomaly detection, and the content of the detection.

[0064] In one embodiment, the anomaly detection system 190 can detect an anomaly if the total number of items in a dataset increases or decreases faster than a predetermined first speed range. That is, it can detect an anomaly if the total number of items in a dataset fluctuates rapidly. In another embodiment, the anomaly detection system 190 can detect an anomaly if the total number of items in at least one dataset increases slower than a predetermined second speed range, or does not increase within a predetermined time. That is, it can detect an anomaly if data is generated slower than expected or not generated at all in relation to the dataset.

[0065] In one embodiment, the anomaly detection system 190 can detect an anomaly when, if the determined monitoring type is numerical, at least one of the minimum, maximum, mean, standard deviation, or quartile of an item included in at least one dataset increases or decreases faster than a predetermined third speed range. In other words, it can detect an anomaly when the characteristics of the data change rapidly.

[0066] In one embodiment, the anomaly detection system 190 can detect an anomaly when the number of categories included in at least one dataset increases or decreases, provided that the determined monitoring type is categorical. In other words, it can detect an anomaly when the number of categories in the dataset, i.e., the number of groups categorized within the dataset, changes.

[0067] In one embodiment, the anomaly detection system 190 can detect the occurrence of an anomaly by inputting at least one dataset selected as the monitoring target and a monitoring type into a machine learning model that has been trained to detect the occurrence of an anomaly based on input data and information related to the input data.

[0068] The first, second, and / or third speed ranges mentioned above can be set differently based on information associated with the dataset. Specifically, the speed range or speed can be set based on the number of records in the dataset, the number of inputs / outputs per unit time, the data type, etc. For example, the speed range can be set to a higher speed range as the number of records in the dataset increases.

[0069] In Figure 1, for the sake of simplification, user terminals associated with user 110 are not shown, but such user terminals can be configured to communicate with each other via wired and / or wireless connections to at least some of the management system 120, aggregation system 160, visualization system 180, and anomaly detection system 190.

[0070] Figure 2 is a schematic diagram showing a configuration in which an information processing system 230 is connected to communicate with a plurality of user terminals 210_1, 201_2, and 210_3 in order to provide data monitoring according to one embodiment of the present disclosure. The information processing system 230 may include a system that can provide data monitoring to the plurality of user terminals 210_1, 201_2, and 210_3 via a network 220. The information processing system 230 may include at least one of the management system 120, aggregation system 160, visualization system 180, or anomaly detection system 190 described in Figure 1. Furthermore, any one of the plurality of user terminals 210_1, 201_2, and 210_3 may mean a user terminal associated with user 110 described in Figure 1.

[0071] According to one embodiment, the information processing system 230 may include computer-executable programs (e.g., downloadable applications), one or more server devices and / or databases capable of storing, providing, and executing data, and one or more distributed computing devices and / or distributed databases for a cloud computing service infrastructure. For example, the information processing system 230 may include a separate system for data monitoring (e.g., a server device or a cloud device). The data monitoring provided by the information processing system 230 can be provided to users via a data monitoring application installed on each of the multiple user terminals 210_1, 201_2, and 210_3.

[0072] Multiple user terminals 210_1, 210_2, and 210_3 can communicate with the information processing system 230 via the network 220. The network 220 can be configured to enable communication between the multiple user terminals 210_1, 210_2, and 210_3 and the information processing system 230. Depending on the installation environment, the network 220 may consist of a wired network such as Ethernet, PLC (Power Line Communication), telephone line communication equipment, and RS-serial communication, a mobile communication network, a wireless network such as WLAN (Wireless LAN), Wi-Fi (registered trademark), Bluetooth (registered trademark), and ZigBee (registered trademark), or a combination thereof. The communication method is not limited and includes not only communication methods that utilize communication networks that can include the network 220 (e.g., mobile communication networks, wired internet, wireless internet, broadcasting networks, satellite networks, etc.), but also short-range wireless communication between user terminals 210_1, 210_2, and 210_3. For example, network 220 may include one or more of the following networks: PAN (personal area network), LAN (local area network), CAN (campus area network), MAN (metropolitan area network), WAN (wide area network), BBN (broadband network), and the Internet. Network 220 may also include, but is not limited to, one or more of the following network topologies: bus network, star network, ring network, mesh network, star-bus network, tree, or hierarchical network.

[0073] In Figure 2, a smartphone terminal 210_1, a tablet terminal 210_2, and a laptop computer terminal 210_3 are shown as examples of user terminals, but the user terminals 210_1, 210_2, and 210_3 can be any computing device capable of wired and / or wireless communication and on which a data monitoring application can be installed and run. For example, user terminals can include smartphones, mobile phones, navigation systems, desktop computers, laptop computers, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, wearable devices, IoT (Internet of Things) devices, VR (virtual reality) devices, AR (augmented reality) devices, and the like. Furthermore, although Figure 2 shows three user terminals 210_1, 210_2, and 210_3 communicating with the information processing system 230 via the network 220, the system is not limited to this configuration, and a different number of user terminals can be configured to communicate with the information processing system 230 via the network 220.

[0074] In one embodiment, the information processing system 230 receives data monitoring request information, information related to one or more datasets associated with data monitoring, and / or information related to the monitoring type from user terminals 210_1, 210_2, and 210_3, and can transfer the analysis results aggregated based on the datasets to be monitored and the monitoring type to user terminals 210_1, 210_2, and 210_3. In this case, the information processing system 230 may include the aggregation system 160 described in Figure 1. For example, user terminals 210_1, 210_2, and 210_3 can attach / connect the analysis results to the user terminals 210_1, 210_2, and 210_3, or output them to an accessible display device.

[0075] According to one embodiment, the information processing system 230 can receive information related to the dataset to be monitored and the monitoring type in order to detect anomalies in the dataset to be monitored. According to another embodiment, the information processing system 230 can input information related to the dataset and the monitoring type into a machine learning model in order to detect anomalies in the dataset to be monitored. The information processing system 230 can provide the user terminal with anomaly detection results related to the anomalies detected by the machine learning model.

[0076] Figure 3 is a block diagram showing the internal configuration of a user terminal 210 and an information processing system 230 according to one embodiment of the present disclosure. The user terminal 210 can refer to any computing device capable of wired / wireless communication that can run applications such as data monitoring, and can include, for example, the smartphone terminal 210_1, tablet terminal 210_2, and laptop computer terminal 210_3 shown in Figure 2. As shown in the figure, the user terminal 210 can include a memory 312, a processor 314, a communication module 316, and an input / output interface 318. Similarly, the information processing system 230 can include a memory 332, a processor 334, a communication module 336, and an input / output interface 338. As shown in Figure 3, the user terminal 210 and the information processing system 230 can be configured to communicate information and / or data over the network 220 using their respective communication modules 316 and 336. Furthermore, the input / output device 320 can be configured to input information and / or data to the user terminal 210 or output information and / or data generated from the user terminal 210 via the input / output interface 318.

[0077] The memories 312 and 332 may include any non-temporary computer-readable recording medium. According to one embodiment, the memories 312 and 332 may include permanent mass storage devices such as RAM (random access memory), ROM (read-only memory), disk drives, SSDs (solid-state drives), and flash memory. In another example, permanent mass storage devices such as ROM, SSDs, flash memory, and disk drives may be included in the user terminal 210 and / or information processing system 230 as separate permanent storage devices distinct from the memory. Furthermore, the memories 312 and 332 may store an operating system and at least one program code (for example, code for an application related to data monitoring installed and driven on the user terminal 210).

[0078] Such software components can be loaded from a computer-readable recording medium separate from memories 312 and 332. Such a separate computer-readable recording medium may include recording media directly connectable to the user terminal 210 and the information processing system 230, but may also include computer-readable recording media such as floppy drives, disks, tapes, DVD / CD-ROM drives, and memory cards. As another example, software components may be loaded into memories 312 and 332 via a communication module, rather than from a computer-readable recording medium. For example, at least one program may be loaded into memories 312 and 332 based on a computer program (e.g., an application related to data monitoring) installed by a file provided via the network 220 by a developer or a file distribution system that distributes application installation files.

[0079] Processors 314, 334 can be configured to process computer program instructions by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to processors 314, 334 by memory 312, 332 and / or communication modules 316, 336. For example, processors 314, 334 can be configured to execute instructions received by program code stored in a recording device such as memory 312, 332.

[0080] Communication modules 316 and 336 can provide configurations and functions for the user terminal 210 and the information processing system 230 to communicate with each other via the network 220, and can also provide configurations and functions for the user terminal 210 and / or the information processing system 230 to communicate with other user terminals or other systems (e.g., a separate cloud system). For example, a request (e.g., data monitoring request information) generated by program code stored in a recording device such as memory 312 by the processor 314 of the user terminal 210 can be transmitted to the information processing system 230 via the network 220 under the control of the communication module 316. Conversely, control signals and commands provided under the control of the processor 334 of the information processing system 230 can be received by the user terminal 210 via the communication module 336 and the network 220, through the communication module 316 of the user terminal 210.

[0081] The input / output interface 318 may be a means for interfacing with the input / output device 320. For example, the input device may include devices such as a camera including an image sensor, a keyboard, a microphone, or a mouse, and the output device may include devices such as a display device, a speaker, or a haptic feedback device. In another example, the input / output interface 318 may be a means for interfacing with a device that integrates a configuration or function for performing input and output in one, such as a touchscreen. For example, when the processor 314 of the user terminal 210 processes instructions of a computer program loaded into memory 312, a service screen configured using information and / or data provided by the information processing system 230 or other user terminals 210 may be displayed on the display device via the input / output interface 318. In Figure 3, the input / output device 320 is shown not to be included in the user terminal 210, but it is not limited to this and can also be configured together with the user terminal 210. Furthermore, the input / output interface 338 of the information processing system 230 may be connected to the information processing system 230 or may be a means for interface with input and output devices (not shown) that the information processing system 230 may include. In Figure 3, the input / output interfaces 318 and 338 are shown as elements configured separately from the processors 314 and 334, but the system is not limited to this, and the input / output interfaces 318 and 338 can also be configured to be included in the processors 314 and 334.

[0082] The user terminal 210 and the information processing system 230 may include more components than those shown in Figure 3. However, it is not necessary to explicitly show most of the conventional components. According to one embodiment, the user terminal 210 can be implemented to include at least a portion of the input / output device 320 described above. The user terminal 210 may also further include other components such as a transceiver, a GPS (Global Positioning system) module, a camera, various sensors, and a database. For example, if the user terminal 210 is a smartphone, it may include components that are generally found in smartphones, and the user terminal 210 can be implemented to further include a variety of components such as an accelerometer, a gyroscope, a camera module, various physical buttons, buttons using a touch panel, input / output ports, and a vibrator for vibration.

[0083] In one embodiment, the processor 314 of the user terminal 210 can be configured to run a data monitoring application (for example, a dedicated data monitoring application or a web browser application capable of providing data monitoring). In this case, program code associated with the application can be loaded into the memory 312 of the user terminal 210. When the application is running, the processor 314 of the user terminal 210 can receive information and / or data provided by the input / output device 320 via the input / output interface 318, or receive information and / or data from the information processing system 230 via the communication module 316, process the received information and / or data, and store it in the memory 312. In addition, such information and / or data can be provided to the information processing system 230 via the communication module 316.

[0084] When an application providing data monitoring is running, the processor 314 can receive audio data, text, images, and video data input or selected by input devices such as a touchscreen, keyboard, camera including audio sensors and / or image sensors, and microphone, which are connected to the input / output interface 318. The received audio data, text, images, and / or video data can be stored in the memory 312 or provided to the information processing system 230 via the communication module 316 and network 220. The processor 314 of the user terminal 210 can transfer and output information and / or data to the input / output device 320 via the input / output interface 318. For example, the processor 314 of the user terminal 210 can output processed information and / or data via the input / output device 320, such as a display-capable device (e.g., a touchscreen or display device) or an audio-capable device (e.g., a speaker).

[0085] The processor 314 of the user terminal 210 can be configured to manage, process, and / or store information and / or data received from the input / output device 320, other user terminals, the information processing system 230, and / or multiple external systems. The information and / or data processed by the processor 314 can be provided to the information processing system 230 via the communication module 316 and the network 220. The processor 314 of the user terminal 210 can transfer and output information and / or data to the input / output device 320 via the input / output interface 318. For example, the processor 314 can output the received information and / or data, and the outputted information and / or data can be displayed on the screen of the user terminal.

[0086] The processor 334 of the information processing system 230 can be configured to manage, process, and / or store information and / or data received from multiple user terminals and / or multiple external systems. The information and / or data processed by the processor 334 can be provided to the user terminal 210 via the communication module 336 and the network 220.

[0087] Figure 4 shows an example of a data monitoring settings screen 400 according to one embodiment of the present disclosure. The settings screen 400 may include a first area 410 where the user can select a database, a second area 420 where the user can select a DB table within the database, and a third area 440 where the user can check the monitoring progress.

[0088] In the first area 410, a database catalog can be displayed. The database catalog can refer to the database catalog included in the data source. In one embodiment, the user can enter a search term to search for the name of the database to be monitored via a search window in the first area 410. At this time, the database catalog containing the search term may be filtered and displayed in the first area 410.

[0089] The user can select one of the database catalogs displayed in the first area 410. If a database is selected, the DB table catalog contained within the selected database 412 may be displayed in the second area 420. The user can use the table search window 430 to check the monitoring status of a table or to enter a search term to find the name of a DB table to modify. In this case, the DB table catalog containing the search term may be filtered and displayed in the second area 420.

[0090] The user can select one of the DB table catalogs displayed in the second area 420. If a DB table is selected, a catalog of data columns contained within the selected DB table is displayed, and the user can view or select the monitoring type for each data column. This will be explained in detail later in Figure 5.

[0091] The third area 440 can display the monitoring status of each DB table. In one embodiment, the monitoring status can be displayed using a first icon 442 indicating that each DB table is being monitored normally (On state), a second icon 444 indicating that monitoring is not in progress (Off state), and a third icon 446 indicating that monitoring has failed (Error state). Figure 4 shows that the monitoring status is set by icons, but this is not the only way in which the monitoring status can be displayed. For example, the monitoring status can be displayed using toggle buttons, allowing the user to check and change the monitoring status.

[0092] Users can change the monitoring status or monitoring type for DB tables displayed in the third area 440. For example, if a user selects a DB table with the first icon 442 displayed, a separate screen may appear allowing them to modify existing monitoring information or switch the monitoring status to Off. In another example, if a user selects a DB table with the second icon 444 displayed, the monitoring status may be switched to On, and a separate screen may appear allowing them to register new monitoring information.

[0093] The user can receive the reason for monitoring failure for any DB tables displayed in the third area 440 that failed to monitor. For example, the user can receive the reason for monitoring failure by selecting the third icon 446 or the DB table displaying the third icon 446. The reason for monitoring failure may be displayed, for example, in a pop-up format.

[0094] In one embodiment, if the monitoring type of a dataset contained in a DB table is not numeric, but the monitoring type of that dataset is set to numeric, monitoring may be displayed as failed. In another embodiment, if the monitoring type of a dataset is set to categorical, but the number of unique items in the data contained in the dataset is above a default threshold, monitoring may be displayed as failed. In yet another embodiment, monitoring may be displayed as failed if the dataset for which the monitoring type has been set does not actually exist, or if the user does not have read permission to access that dataset.

[0095] In Figure 4, a screen is displayed where the user can select the database and DB tables to be monitored, and the monitoring status of the DB tables is shown. However, this is not the only way to do this. For example, instead, the database catalog could be displayed in the second area 420, and the monitoring status of each database could be displayed in the third area 440. Furthermore, the areas 410, 420, 440, and the search window 430 are not limited to those shown in Figure 4. Some components of the areas 410, 420, 440, or the search window 430 may be omitted, or their position, size, or components may be configured differently from those shown in Figure 4.

[0096] Figure 5 shows an example of the monitoring type setting screen 500 for each dataset in the dataset catalog 520 according to one embodiment of the present disclosure. The user can determine the monitoring type 540 for each dataset and, if an anomaly is detected from the dataset to be monitored by the anomaly detection notification setting 560, can choose whether or not to send an anomaly detection notification. In addition, the user can specify the partition name of the dataset, specify the partition format, and set the data generation time for the dataset in the dataset settings 510.

[0097] The dataset catalog 520 can display datasets and associated information (e.g., the name of the dataset) that can be used to determine the monitoring type. In Figure 5, the dataset is shown as a data column, but it is not limited to this; a dataset can be a database, DB table, or data column, etc.

[0098] The settings screen 500 can display the data type 530 of the data included in each dataset. The data type 530 can include any information indicating the type of data. As shown in Figure 5, it can be, but is not limited to, a numeric type (e.g., Bigint, Double, Int, Double, etc.) or a character type (e.g., String, etc.).

[0099] The settings screen 500 can display the monitoring type 540 for each dataset. The monitoring type may include, but is not limited to, general, numerical, and categorical types. The user can decide whether or not to monitor each dataset and / or the monitoring type 540. Specifically, the user can exclude datasets they do not want to monitor from the datasets to be monitored, or decide on the monitoring type for each dataset from general, numerical, and categorical types. In one embodiment, the settings screen 500 can be configured so that the user can decide on one monitoring type using a dropdown box or radio buttons. In another embodiment, the settings screen 500 can be configured so that the user can decide on one or more types using checkboxes or the like.

[0100] The monitoring type 540 can be set based on information associated with the dataset. The monitoring type 540 can be set based on the metadata of the dataset. In one embodiment, the monitoring type 540 can be set to numeric type if the data type of the dataset is numeric, and to general type or categorical type if it is character type. In another embodiment, the monitoring type 540 can be set to categorical type if the number of unique items in the data included in the dataset is less than or equal to a default threshold. In yet another embodiment, the monitoring type 540 can be set to one of the derived types by inputting the dataset and the metadata of the dataset (e.g., the data type of the dataset) into a machine learning model that has been trained to derive monitoring types based on the input data and information associated with the input data. In this case, one or more monitoring types can be set.

[0101] The settings screen 500 can display a description 550 for each dataset. The description 550 is used to send an anomaly detection notification when an anomaly is detected in the dataset.

[0102] The settings screen 500 may include an anomaly detection notification setting 560, allowing the user to choose whether or not to send an anomaly detection notification. In Figure 5, the anomaly detection notification setting 560 is shown as a toggle button, but it is not limited to this. The user can use the anomaly detection notification setting 560 to set notifications for all datasets in the dataset catalog 520, or to set notifications individually for one or more datasets.

[0103] In the Anomaly Detection Notification Settings 560, users can add, delete, or modify user terminals that send anomaly detection notifications. When the notification is activated in the Anomaly Detection Notification Settings 560, if an anomaly is detected in the dataset, an anomaly detection notification can be sent to the default user terminal.

[0104] Figure 6 shows an example of a screen 600 that displays the results of a comprehensive monitoring analysis according to one embodiment of the present disclosure. The screen 600 may include a first area 610 for the user to select a dataset for which to query the monitoring analysis results, and a second area 620 for the user to select a monitoring type so that comprehensive analysis results or analysis results according to one monitoring type are output. The screen 600 may also include a third area 630 for setting a period for calculating the total number of items included in the dataset, a fourth area 640 for displaying the total number of items in the dataset at a specific day and / or time, and the percentage increase or decrease in the total number of items compared to the previous day, and a fifth area 650 for visualizing the trend of the total number of items within the period set in the third area 630. Furthermore, screen 600 may include a sixth area 660 for visualizing the trend in the number of Null items in the dataset over the period set in the third area 630, a seventh area 670 for specifying the day and / or time to query the number of Null items in the dataset, and an eighth area 680 for displaying the number of Null items in each part of the dataset and the ratio of Null items to the total number of items.

[0105] The areas included within the screen are not limited to those shown in Figure 6. Some areas or components within each area may be omitted, or their position, size, or components may be configured differently from those shown in Figure 6.

[0106] In the first area 610, the user can select one or more datasets from which to query monitoring analysis results. For example, the user can select a data source, a database within the data source, a DB table within the database, and one or more data columns within the DB table from which to query monitoring analysis results. Configurations related to the first area 610 are not shown in Figures 7 to 9, but can be applied in the same way as in Figure 6.

[0107] In the second area 620, the user can select an Overview item so that comprehensive analysis results are output, or select one of several monitoring types (Basic, Numeric, Categorical items) so that analysis results according to the monitoring type are output. Figure 6 explains the case assuming that comprehensive analysis results are selected in the second area 620. In one embodiment, when the user selects a specific monitoring type (one of the Basic, Numeric, or Categorical items), analysis results associated with multiple datasets specified to be monitored by that type in Figure 5 can be provided. Such analysis results are explained in detail in Figures 7 to 9.

[0108] In the third area 630, the user can set a period for calculating the total number of items included in the dataset. The period can be set by the user specifying one or more days and / or times. For example, the period can be set from a specific day and / or time to another day and / or time, set before a specific day and / or time, or set after a specific day and / or time.

[0109] In the fourth area 640, the total number of items in the dataset at a specific date and / or time, and the percentage increase or decrease in the total number of items compared to the previous day can be displayed. As shown in the figure, the fourth area 640 can display the total number of items at the date and time of the analysis result query, and the percentage increase or decrease in the total number of items compared to the previous day. The total number of items can be displayed in various formats. For example, information related to the total number of items can be displayed in a format that combines the number with a unit indicating the magnitude of the number (e.g., K, M, G, etc.). Additionally or alternatively, the fourth area 640 can display the increase or decrease in the total number of items compared to the previous day.

[0110] In the fifth area 650, the trend in the total number of items within the period set in the third area 630 can be visualized and displayed. In one embodiment, the total number of items within the period can be displayed in the form of a line graph for each day. In this case, the graph can be displayed with different lines for each month. The embodiment is not limited to this example, and the daily trend in the total number of items can be represented in various ways.

[0111] In the sixth area 660, the daily trend of the number of Null items in the dataset can be visualized and displayed. In one embodiment, the sixth area 660 can display a graph corresponding to each of the one or more data columns selected by the user in the first area 610. The graph can be displayed so that the number of Null items on the y axis changes with the change in the x axis over time. In this case, a baseline parallel to the x axis is displayed in each graph, and the height of the baseline can correspond to the average number of unique items or a predetermined threshold number in the one or more data columns selected by the user. However, the trend of the number of unique items can be represented in various forms. In another embodiment, the sixth area 660 may display a graph showing the number of Null items included in all of the data columns selected in the first area 610.

[0112] In the seventh area 670, the user can specify the day and / or time for querying the number of null items in the dataset. In the eighth area 680, at least one of the following can be displayed: the number of null items in the dataset for the day and / or time specified in the seventh area 670, the total number of items, or the ratio of the number of null items to the total number of items. In Figure 6, the above contents are displayed in the form of a table, but are not limited to this, and can be displayed in various forms such as pie charts.

[0113] Figure 7 shows an example of a screen 700 displaying general monitoring analysis results according to one embodiment of the present disclosure. The screen 700 may include a first area 710 in which the user selects whether to output comprehensive analysis results or analysis results according to one of the monitoring types, and a second area 720 in which the user sets a period for calculating the number of unique items and duplicate items in the dataset. The screen 700 may also include a third area 730 for visualizing the trend of the number of unique items within the period set in the second area 720, and a fourth area 740 for visualizing the trend of the number of duplicate items within the period set in the second area 720. The screen 700 may also include a fifth area 750 for the user to specify the day and / or time for querying the number of unique items and / or duplicate items in the dataset, and a sixth area 760 for displaying at least one of the number of unique items, the number of duplicate items, the total number of items, and the ratio of the number of unique items or duplicate items to the total number of items for each of the datasets.

[0114] Figure 7 explains the process assuming that in the first region 710, the analysis results for a dataset determined to be of the general monitoring type are selected to be output. The screen is not limited to what is shown in Figure 7, and some regions or parts of the configuration within each region may be omitted, or their position, size, or components may be configured differently from those shown in Figure 7.

[0115] In the second area 720, the user can set a period for calculating the number of unique items and / or duplicate items included in the dataset. The calculation period can be set by the user specifying one or more days and / or times. For example, the calculation period can be set from a specific day and / or time to another day and / or time, before a specific day and / or time, or after a specific day and / or time.

[0116] The third area 730 can visualize and display the daily or hourly trend of the number of unique items in the dataset, and the fourth area 740 can visualize and display the daily or hourly trend of the number of duplicate items in the dataset. In one embodiment, the third area 730 and / or the fourth area can display graphs corresponding to each of one or more data columns selected by the user. The graphs in the third area 730 and / or the fourth area 740 can be displayed such that the number of unique items and / or duplicate items on the y axis changes with the time change of the x axis. In this case, a baseline parallel to the x axis is displayed in each graph, and the height of the baseline can correspond to the average number of unique items and / or duplicate items in one or more data columns selected by the user. Alternatively, the height of the baseline can correspond to a predetermined threshold number. Not limited to this, the trends of the number of unique items and duplicate items can be represented in various forms.

[0117] In the fifth area 750, the user can specify the date and / or time on which to query the number of unique items and / or duplicate items in the dataset. In the sixth area 760, at least one of the following can be displayed: the number of unique items, the number of duplicate items, the ratio of the number of unique items to the total number of items, or the ratio of the number of duplicate items in the dataset at the date and / or time specified in the fifth area 750. In Figure 7, the above contents are displayed in the form of a table, but are not limited to this, and can be displayed in various forms such as a pie chart.

[0118] Figure 8 shows an example of a screen 800 that displays numerical monitoring analysis results according to one embodiment of the present disclosure. Screen 800 may include a first area 810 in which the user selects whether to output a comprehensive analysis result or an analysis result according to one of the monitoring types, and a second area 820 in which the user sets a period for calculating basic statistics in the dataset. Screen 800 may also include a third area 830 that visualizes the changes in basic statistics within the period set in the second area 820, and a fourth area 840 that visualizes the changes in the rate of change of basic statistics within the period set in the second area 820. Screen 800 may also include a fifth area 850 for the user to specify the day and / or time for querying basic statistics in the dataset, and a sixth area 860 that displays at least one of the basic statistics for each of the datasets. Basic statistics may include the minimum value, maximum value, mean, standard deviation, or quartiles of the items included in the dataset.

[0119] Figure 8 explains the process assuming that in the first region 810, the analysis results for a dataset determined by the monitoring type as numerical are selected to be output. The screen is not limited to what is shown in Figure 8, and some regions or parts of the configuration within each region may be omitted, or their position, size, or components may be configured differently from those shown in Figure 8.

[0120] In the second area 820, the user can set the period for calculating the basic statistics of items included in the dataset. The calculation period can be set by the user specifying one or more days and / or hours. For example, the calculation period can be set from a specific day and / or time to another day and / or time, set before a specific day and / or time, or set after a specific day and / or time.

[0121] The third area 830 can visualize and display the daily or hourly trends of the basic statistics of the dataset, and the fourth area 840 can visualize and display the daily or hourly trends of the rate of change of the basic statistics within the dataset. In one embodiment, the third area 830 displays one or more basic statistics charts, each containing a graph corresponding to one or more data columns selected by the user, and the fourth area 840 displays one or more basic statistics rate of change charts, each containing a graph corresponding to one of the data columns. When there are large differences in the values ​​of items within each dataset, it is difficult to check the basic statistics as a single chart, so the basic statistics rate of change chart can solve this problem.

[0122] One or more charts within the third region 830 and / or the fourth region 840 can be displayed such that the base statistic or rate of change of the base statistic on the y axis changes with respect to the time change on the x axis. In this case, each chart displays a baseline parallel to the x axis, and the height of the baseline can correspond to the mean of the base statistic or the mean of the rate of change of the base statistic in one or more data columns selected by the user. Alternatively, the height of the baseline can correspond to a default threshold or threshold ratio. Not limited to this, the trends of the base statistic or rate of change of the base statistic can be represented in a variety of forms.

[0123] In the fifth area 850, the user can specify the day and / or time for querying the basic statistics or the rate of change of basic statistics within the dataset. In the sixth area 860, at least one of the basic statistics or the rate of change of basic statistics for the dataset at the day and / or time specified in the fifth area 850 can be displayed. In Figure 8, the above contents are displayed in the form of a table, but are not limited to this, and can be displayed in various forms such as pie charts.

[0124] Figure 9 shows an example of a screen 900 that displays categorical monitoring analysis results according to one embodiment of the present disclosure. The screen 900 may include a first area 910 in which the user selects whether to output a comprehensive analysis result or an analysis result according to one of the monitoring types, and a second area 920 in which the user sets the period for calculating the percentage of the number of items in each category and / or the number of items in each category relative to the total number of items in the dataset. The screen 900 may also include a third area 930 that visualizes the trend of the percentage within the period set in the second area 920, and a fourth area 940 that visualizes the trend of the number of items within each category within the period set in the second area 920. Screen 900 may include a fifth area 950 for the user to specify the date and / or time for querying the number or percentage of items in each category of the dataset, a sixth area 960 for displaying the percentage of the number of items in each category relative to the total number of items in the dataset at the date and / or time set in the fifth area 950, and a seventh area 970 for displaying the number of items in each category at the date and / or time set in the fifth area 950.

[0125] Figure 9 explains the process assuming that in the first region 910, analysis results for a dataset where the monitoring type is determined to be categorical are output. The screen is not limited to what is shown in Figure 9, and some regions or parts of the configuration within each region may be omitted, or their position, size, or components may be configured differently from those shown in Figure 9.

[0126] In the second area 920, the user can set the period for calculating the percentage of items in each category relative to the total number of items in the dataset, and / or the number of items in each category. The calculation period can be set by the user specifying one or more days and / or times. For example, the calculation period can be set from a specific day and / or time to another day and / or time, before a specific day and / or time, or after a specific day and / or time.

[0127] In the third area 930, the daily or hourly trend of the percentage of the number of items in each category relative to the total number of items in the dataset can be visualized and displayed, and in the fourth area 940, the daily or hourly trend of the number of items in each category can be visualized and displayed. The percentage trends can be represented in various forms, such as ratio graphs.

[0128] In the fifth area 950, the user can specify the date and / or time on which to query the number or percentage of items within each category of the dataset. In the sixth area 960, the percentage of the number of items within each category of the dataset at the date and / or time specified in the fifth area 950 can be displayed. In Figure 9, the above content is displayed in the form of a pie chart, but it is not limited to this and can be displayed in various forms. In the seventh area 970, the number of items within each category at the date and / or time set in the fifth area 950 can be displayed.

[0129] Figure 10 is a flowchart of a data monitoring method 1000 according to one embodiment of the present disclosure. In one embodiment, the data monitoring method 1000 can be performed by a user terminal (e.g., at least one processor of user terminal 210) and / or an information processing system (e.g., at least one processor of information processing system 230). The method 1000 can be started by a step (S1010) in which a processor included in a computing device receives a first user input in which it selects at least one dataset to be monitored from among a plurality of datasets. In one embodiment, the processor can receive a first user input in which it selects at least one dataset to be monitored from among a plurality of datasets of other computing devices that can be connected to from the computing device.

[0130] The processor can then analyze at least one dataset and output a recommended monitoring type for at least one selected dataset from among several monitoring types (S1020). For example, the processor can output a recommended monitoring type for at least one dataset based on the metadata of at least one selected dataset. In another example, the processor can output a categorical type as the recommended monitoring type if the number of unique items in the data contained in at least one dataset is below a default threshold.

[0131] The processor can then receive a second user input which determines the monitoring type for at least one selected dataset from among several monitoring types (S1030). Here, the monitoring type may include, but is not limited to, general types, numerical types, and categorical types.

[0132] The processor can output analysis results for the data contained in at least one selected dataset, calculated based on the selected dataset and the determined monitoring type (S1040). In one embodiment, if the determined monitoring type is general, the processor can output at least one of the number of duplicate items or the number of unique items in at least one dataset. In another embodiment, if the determined monitoring type is numerical, the processor can output at least one of the minimum value, maximum value, mean, standard deviation, or quartile of the items contained in at least one dataset. In yet another embodiment, if the determined monitoring type is categorical, the processor can output the percentage of the number of items in each category relative to the total number of items in at least one dataset.

[0133] In one embodiment, the processor can output at least one of the total number of items or the number of null items included in at least one dataset.

[0134] In one embodiment, an anomaly detection notification can be sent to a user terminal associated with at least one dataset when an anomaly is detected in at least one dataset. For example, an anomaly detection notification can be sent to the user terminal when a first anomaly is detected in which the total number of items in at least one dataset increases or decreases faster than a predetermined first speed range. As another example, an anomaly detection notification can be sent to the user terminal when a second anomaly is detected in which the total number of items in at least one dataset increases slower than a predetermined second speed range or does not increase within a predetermined time. As yet another example, if the determined monitoring type is numerical, an anomaly detection notification can be sent to the user terminal when a third anomaly is detected in which at least one of the minimum value, maximum value, mean, standard deviation, or quartile of an item included in at least one dataset increases or decreases faster than a predetermined third speed range. As another example, if the determined monitoring type is categorical, an anomaly detection notification can be sent to the user terminal when a fourth anomaly is detected, in which the number of categories included in at least one dataset increases or decreases. As yet another example, a second user input and at least one dataset can be input to a machine learning model trained to detect whether an anomaly has occurred based on the input data and information related to the input data, and an anomaly detection notification can be sent to the user terminal when an anomaly is detected. Such anomaly detection is performed by the anomaly detection system 190 in Figure 1, and such anomaly detection notification can be sent to a user terminal associated with the user responsible for managing or monitoring the anomaly.

[0135] In one embodiment, the information processing system (e.g., the aggregation system 160 in Figure 1) generates analysis logic based on user input, at least one selected dataset, and a determined monitoring type, and applies the analysis logic to at least one dataset to calculate analysis results for the data contained in at least one dataset.

[0136] Figure 11 shows an example of an artificial neural network model 1100 according to one embodiment of the present disclosure. The artificial neural network model 1100 may be a statistical learning algorithm embodied in machine learning technology and cognitive science based on the structure of a biological neural network, or a structure that executes such an algorithm, as an example of a machine learning model.

[0137] According to one embodiment, the artificial neural network model 1100 can demonstrate a machine learning model with problem-solving capabilities by having nodes, which are artificial neurons that form a network through synaptic connections, repeatedly adjust the synaptic weights to learn to reduce the error between the correct output corresponding to a specific input and the inferred output, similar to a biological neural network. For example, the artificial neural network model 1100 can include any probabilistic model or neural network model used in artificial intelligence learning methods such as machine learning and deep learning.

[0138] According to one embodiment, the aforementioned recommended monitoring type extraction model and anomaly detection model can be generated in the form of an artificial neural network model 1100. For example, the artificial neural network model 1100 can receive a dataset and metadata of the dataset and extract a recommended monitoring type. As another example, the artificial neural network model 1100 can receive at least one dataset and monitoring type selected as the monitoring target and determine whether or not an anomaly has occurred.

[0139] The artificial neural network model 1100 is implemented as a multilayer perceptron (MLP) composed of multiple layers of nodes and their connections. The artificial neural network model 1100 according to this embodiment can be implemented using one of various artificial neural network model structures, including an MLP. As shown in Figure 11, the artificial neural network model 1100 consists of an input layer 1120 that receives an input signal or data 1110 from the outside, an output layer 1140 that outputs an output signal or data 1150 corresponding to the input data, and n hidden layers 1130_1 to 1130_n (where n is a positive integer) located between the input layer 1120 and the output layer 1140, which receive signals from the input layer 1120, extract characteristics, and transmit them to the output layer 1140. Here, the output layer 1140 receives signals from the hidden layers 1130_1 to 1130_n and outputs them to the outside.

[0140] The artificial neural network model 1100 can be trained using two methods: supervised learning, which optimizes the model for solving problems based on the input of teacher signals (correct answers), and unsupervised learning, which does not require teacher signals. In one embodiment, the information processing system can train the artificial neural network model 1100 by utilizing a dataset and its monitoring type, or by utilizing a dataset and its metadata.

[0141] In one embodiment, the information processing system can directly generate training data for training an artificial neural network model 1100. For example, the information processing system can generate a training dataset containing a dataset to be monitored and metadata of the dataset to be monitored. Subsequently, the information processing system can train the artificial neural network model 1100 to extract recommended monitoring types for the dataset to be monitored based on the generated training dataset. In another example, the information processing system can generate a training dataset containing a dataset to be monitored and monitoring types. Subsequently, the information processing system can train the artificial neural network model 1100 to detect anomalies in the dataset to be monitored based on the generated training dataset.

[0142] In one embodiment, the input variables of the artificial neural network model 1100 can include a dataset and metadata for the dataset. When the aforementioned input variables are input by the input layer 1120, the output variables output from the output layer 1140 of the artificial neural network model 1100 may be a recommended monitoring type for the dataset. In another embodiment, the input variables of the artificial neural network model 1100 can include at least one dataset selected for monitoring and a monitoring type. When the aforementioned input variables are input by the input layer 1120, the output variables output from the output layer 1140 of the artificial neural network model 1100 may indicate whether or not an abnormal phenomenon has occurred in the dataset.

[0143] In this way, multiple input variables and corresponding multiple output variables are matched in the input layer 1120 and output layer 1140 of the artificial neural network model 1100, and the synaptic values ​​between nodes etc. included in the input layer 1120, hidden layers 1130_1 to 1130_n and output layer 1140 are adjusted so that the correct output corresponding to a specific input can be extracted. Through this learning process, the hidden characteristics of the input variables of the artificial neural network model 1100 can be grasped, and the synaptic values ​​(or weights) between nodes etc. of the artificial neural network model 1100 can be adjusted so that the error between the output variable calculated based on the input variable and the target output is reduced. Furthermore, the information processing system can learn an algorithm for receiving a single dataset and learn in a way that minimizes the loss with respect to the recommended monitoring type information or anomaly occurrence information (i.e., annotation information) of the dataset.

[0144] Using the trained artificial neural network model 1100, the recommended monitoring type for the dataset can be extracted. Furthermore, the trained artificial neural network model 1100 can be used to detect whether or not abnormal developments occur within the dataset.

[0145] The methods described above may be provided as computer programs stored on a computer-readable recording medium for execution on a computer. The medium may continuously store computer-executable programs or temporarily store them for execution or download. The medium may also be a variety of recording or storage means in the form of a single or multiple hardware combination, and is not limited to a medium directly connected to a computer system, but may be distributed on a network. Examples of mediums include hard disks, magnetic media such as floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical mediums such as floptical disks, ROMs, RAMs, and flash memory, all configured to store program instructions. Other examples of mediums include recording or storage media managed by app stores and other sites and servers that supply or distribute various software.

[0146] The methods, operations, or techniques described herein can be implemented by a variety of means. For example, such techniques can be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will understand that the various exemplary logical blocks, modules, circuits, and algorithmic steps described herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate such mutual substitutability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described in terms of their functional aspects. Whether such functions are implemented as hardware or as software depends on the design requirements attached to the particular application and the overall system. Those skilled in the art may implement the functions described in a variety of ways for their respective specific applications, but such implementations do not deviate from the scope of this disclosure.

[0147] In hardware implementation, the processing units used to perform the technique may also be one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, computers, or combinations thereof.

[0148] Accordingly, the diverse exemplary logic blocks, modules, and circuits described herein may also be embodied or performed by any combination of general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gates and transistor logic, discrete hardware components, or any combination designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, a processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be embodied by a combination of computing devices, such as a DSP and a microprocessor, multiple microprocessors, one or more microprocessors associated with a DSP core, or any other combination of configurations.

[0149] In the embodiment of firmware and / or software, the technique can be embodied in instructions stored on a computer-readable medium such as RAM (random access memory), ROM (read-only memory), NVRAM (non-volatile random access memory), PROM (programmable read-only memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable PROM), flash memory, CD (compact disc), or magnetic or optical data storage devices. The instructions are executable by one or more processors, which can perform specific embodiments of the functions described herein.

[0150] When embodied as software, the techniques may be stored on or transferred via computer-readable media as one or more instructions or codes. Computer-readable media include any media that facilitates the transfer of computer programs from one location to another, and include both computer storage media and communication media. Storage media can be any available media accessible by a computer. As an unrestricted example, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other media accessible by a computer that can be used to transfer or store desired program code in the form of instructions or data structures. Furthermore, any connection may appropriately be referred to as computer-readable media.

[0151] For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, lead wire, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, lead wire, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of a medium. The terms "disk" and "disc" used in this application include CDs, laser discs, optical discs, DVDs (digital versatile discs), floppy disks, and Blu-ray discs, where a disk typically reproduces data magnetically, while a disc reproduces data optically using a laser. The aforementioned combinations, etc., must also be included within the scope of computer-readable media, etc.

[0152] Software modules may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, portable disks, CD-ROMs, or any other known form of storage medium. An exemplary storage medium may be linked to the processor so that the processor reads information from or writes information to the storage medium. Alternatively, the storage medium may be integrated into the processor. The processor and storage medium may reside within an ASIC. The ASIC may reside within a user terminal. Alternatively, the processor and storage medium may exist as separate components within the user terminal.

[0153] While the embodiments described above describe embodiments using one or more standalone computer systems, this disclosure is not limited to those embodiments and can be embodied in any computing environment, such as a network or a distributed computing environment. Furthermore, embodiments of the subject matter in this disclosure can be embodied in multiple processing chips or devices, and storage can be similarly affected across multiple devices. Such devices may include PCs, network servers, and portable devices.

[0154] While this specification describes some embodiments of the present disclosure, various modifications and alterations are possible without departing from what a person ordinarily skilled in the art would understand. Such modifications and alterations would fall within the scope of the claims appended to this specification. [Explanation of Symbols]

[0155] 110 users 120 Management Systems 130 Data Metadata Database 140 Source Data Database 150 backend databases 160 Aggregation System 170 Data Monitoring Metrics Storage 180 Visualization Systems 190 Anomaly Detection System

Claims

1. In a data monitoring method performed by at least one processor included in a computing device, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include analyzing at least one dataset and outputting a recommended monitoring type for at least one dataset selected by the first user input from among multiple monitoring types, A step of receiving a second user input that determines the monitoring type for at least one dataset selected in the first user input from among the plurality of monitoring types, The process includes the step of outputting an analysis result for the data contained in the at least one dataset, calculated based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input, A data monitoring method comprising the step of outputting the recommended monitoring type, wherein if the number of unique items in the data included in at least one dataset is less than or equal to a predetermined threshold, the recommended monitoring type is a categorical type, which is the type used to calculate statistical indicators for a dataset.

2. In a data monitoring method performed by at least one processor included in a computing device, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include analyzing at least one dataset and outputting a recommended monitoring type for at least one dataset selected by the first user input from among multiple monitoring types, A step of receiving a second user input that determines the monitoring type for at least one dataset selected in the first user input from among the plurality of monitoring types, The process includes the step of outputting an analysis result for the data contained in the at least one dataset, calculated based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input, A data monitoring method further comprising the step of outputting at least one of the total number of items or the number of null items included in the at least one dataset.

3. In a data monitoring method performed by at least one processor included in a computing device, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include analyzing at least one dataset and outputting a recommended monitoring type for at least one dataset selected by the first user input from among multiple monitoring types, A step of receiving a second user input that determines the monitoring type for at least one dataset selected in the first user input from among the plurality of monitoring types, The process includes the step of outputting an analysis result for the data contained in the at least one dataset, calculated based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input, A data monitoring method in which the step of outputting the analysis results includes, if the monitoring type determined by the second user input is a general type that aggregates at least one of the number of duplicate items or the number of unique items in a dataset, the step of outputting at least one of the number of duplicate items or the number of unique items in at least one dataset.

4. In a data monitoring method performed by at least one processor included in a computing device, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include analyzing at least one dataset and outputting a recommended monitoring type for at least one dataset selected by the first user input from among multiple monitoring types, A step of receiving a second user input that determines the monitoring type for at least one dataset selected in the first user input from among the plurality of monitoring types, The process includes the step of outputting an analysis result for the data contained in the at least one dataset, calculated based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input, A data monitoring method in which the step of outputting the analysis results includes, if the monitoring type determined by the second user input is a numerical type that aggregates basic statistics of a dataset, the step of outputting at least one of the minimum value, maximum value, mean, standard deviation, or quartile of an item included in at least one dataset.

5. In a data monitoring method performed by at least one processor included in a computing device, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include analyzing at least one dataset and outputting a recommended monitoring type for at least one dataset selected by the first user input from among multiple monitoring types, A step of receiving a second user input that determines the monitoring type for at least one dataset selected in the first user input from among the plurality of monitoring types, The process includes the step of outputting an analysis result for the data contained in the at least one dataset, calculated based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input, A data monitoring method in which the step of outputting the analysis results includes, if the monitoring type determined by the second user input is a categorical type which calculates a statistical indicator of the dataset, the step of outputting the percentage of the number of items in each category to the total number of items in at least one dataset.

6. In a data monitoring method performed by at least one processor included in a computing device, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include analyzing at least one dataset and outputting a recommended monitoring type for at least one dataset selected by the first user input from among multiple monitoring types, A step of receiving a second user input that determines the monitoring type for at least one dataset selected in the first user input from among the plurality of monitoring types, The process includes the step of outputting an analysis result for the data contained in the at least one dataset, calculated based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input, A data monitoring method in which, upon detection of an abnormal phenomenon occurring in at least one dataset, an anomaly detection notification is transmitted to a user terminal associated with the at least one dataset.

7. In a data monitoring method performed by at least one processor included in a computing device, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include: analyzing the metadata of at least one dataset and analyzing the content of the data contained in the dataset to output a recommended monitoring type from among a plurality of monitoring types for at least one dataset selected by the first user input; A step of receiving a second user input that determines the monitoring type for at least one dataset selected in the first user input from among the plurality of monitoring types, A data monitoring method comprising the step of outputting an analysis result for the data contained in the at least one dataset, calculated based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input.

8. The data monitoring method according to any one of claims 1 to 6, wherein the plurality of monitoring types include a general type which aggregates at least one of the number of duplicate items or the number of unique items in the dataset, a numerical type which aggregates basic statistics of the dataset, and a categorical type which calculates statistical indicators of the dataset.

9. The data monitoring method according to any one of claims 1 to 6, wherein the step of receiving the first user input includes receiving the first user input of selecting at least one dataset to be monitored from among a plurality of datasets of other computing devices that can be connected to the computing device.

10. The data monitoring method according to any one of claims 1 to 6, wherein the step of outputting the recommended monitoring type includes the step of outputting the recommended monitoring type based on metadata of at least one dataset selected in the first user input.

11. The data monitoring method according to claim 6, wherein an anomaly detection notification is transmitted to a user terminal in response to the detection of a first anomaly phenomenon in which the total number of items in at least one dataset increases or decreases faster than a predetermined first speed range.

12. The data monitoring method according to claim 6, wherein an anomaly detection notification is transmitted to a user terminal in response to the detection of a second anomaly phenomenon in which the total number of items in at least one dataset increases more slowly than a predetermined second speed range or does not increase within a predetermined time period.

13. The data monitoring method according to claim 6, wherein, if the determined monitoring type is a numerical type that aggregates basic statistics of a dataset, the anomaly detection notification is transmitted to the user terminal when it is detected that a third anomaly has occurred in which at least one of the minimum, maximum, mean, standard deviation, or quartiles of an item included in the at least one dataset increases or decreases faster than a predetermined third speed range.

14. The data monitoring method according to claim 6, wherein, if the determined monitoring type is a categorical type which calculates a statistical indicator of the dataset, the anomaly detection notification is transmitted to the user terminal in response to the detection of a fourth anomaly phenomenon in which the number of categories included in at least one dataset increases or decreases.

15. The data monitoring method according to claim 6, wherein the second user input and the at least one dataset are input to a machine learning model trained to detect the occurrence of an abnormal phenomenon based on the input data and information related to the input data, and when an abnormal phenomenon is detected, the abnormality detection notification is transmitted to the user terminal.

16. In a data monitoring method performed by at least one processor, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include: receiving a second user input that determines a monitoring type for at least one dataset selected by the first user input from among multiple monitoring types; and generating analysis logic based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input. The steps include applying the analysis logic to the at least one dataset and calculating the analysis results for the data contained in the at least one dataset, Includes, A data monitoring method further comprising the step of outputting at least one of the total number of items or the number of null items included in the at least one dataset.

17. In a data monitoring method performed by at least one processor, A first step of receiving user input to select at least one dataset from among multiple datasets to be monitored, The steps include: receiving a second user input that determines a monitoring type for at least one dataset selected by the first user input from among multiple monitoring types; and generating analysis logic based on the at least one dataset selected by the first user input and the monitoring type determined by the second user input. The steps include applying the analysis logic to the at least one dataset and calculating the analysis results for the data contained in the at least one dataset, Includes, A data monitoring method in which, upon detection of an abnormal phenomenon occurring in at least one dataset, an anomaly detection notification is transmitted to a user terminal associated with the at least one dataset.

18. The data monitoring method according to claim 17, wherein the plurality of monitoring types include a general type which aggregates at least one of the number of duplicate items or the number of unique items in the dataset, a numerical type which aggregates basic statistics of the dataset, and a categorical type which calculates statistical indicators of the dataset.

19. A computer-readable computer program for executing a data monitoring method according to any one of claims 1 to 18.

20. In information processing systems, Communication module and Memory and The system includes at least one processor connected to the memory and configured to execute at least one computer-readable program contained in the memory, The aforementioned at least one program, Upon receiving a first user input that selects at least one dataset from among multiple datasets to be monitored, A second user input is received that determines the monitoring type for at least one dataset selected in the first user input from among multiple monitoring types. Based on the first user input and the monitoring type determined by the second user input, an analysis logic is generated. The analysis logic is substituted into the at least one dataset to calculate the analysis results for the data contained in the at least one dataset. The command includes a command for outputting at least one of the total number of items or the number of null items included in the at least one dataset, Information processing system.

21. In information processing systems, Communication module and Memory and The system includes at least one processor connected to the memory and configured to execute at least one computer-readable program contained in the memory, The aforementioned at least one program, Upon receiving a first user input that selects at least one dataset from among multiple datasets to be monitored, A second user input is received that determines the monitoring type for at least one dataset selected in the first user input from among multiple monitoring types. Based on the first user input and the monitoring type determined by the second user input, an analysis logic is generated. The analysis logic is substituted into the at least one dataset to calculate the analysis results for the data contained in the at least one dataset. If, based on the analysis results, an abnormal phenomenon is detected in at least one dataset, the communication module shall transmit an abnormality detection notification to a user terminal associated with the at least one dataset, including a command for such notification. Information processing system.

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