Electronic apparatus for processing data and its operation method

Abstract

The data processing method of an electronic device according to one embodiment of the present invention is characterized by including the following steps: obtaining a data source related to an e-commerce service; applying a data pipeline to the data source to generate measurement data; and confirming information on the time delay between obtaining the data source and generating the measurement data.

Description

Electronic devices and their operating methods for data processing This invention relates to an electronic device for data processing and a method of operating the same. More specifically, this invention relates to an electronic device and a method of operating the same for verifying time delays in a data processing flow that generates measurement data from a data source related to an e-commerce service. As internet use becomes increasingly widespread, the e-commerce market is expanding. In particular, with the spread of infectious diseases, the proportion of people purchasing goods in physical stores is decreasing, while the proportion purchasing goods through e-commerce services provided by computers or smartphones is rapidly increasing. With the expansion of the e-commerce market, not only is the proper handling of the large amounts of data provided in e-commerce services crucial, but the need to manage the time required for data processing to improve user experience is also growing. For example, when issuing coupons based on the number of user orders, the data generated from user orders is processed to produce metrics related to the number of user orders. Based on this metrics, coupons corresponding to the number of user orders are then issued. However, if there is a delay in the generation of these metrics, coupon issuance may be delayed, impacting user experience. In such cases, e-commerce service providers can assess the time required to generate the metrics to identify problems during data generation or improve user experience by providing relevant information to users. As mentioned above, by confirming the time required for the data sources generated during the use of e-commerce services to produce the desired metric data, it is possible to quickly respond to problems that occur in e-commerce services and provide users with smooth and effective services. Therefore, the necessity of information related to data processing time is becoming increasingly prominent. In this regard, see previous documents such as KR 10-2022-0152916 A and US 11762754 B1. [The problem the invention aims to solve] The purpose of this invention is to provide a method for generating measurement data from data sources related to e-commerce services. Furthermore, the purpose of this invention is to quickly identify service-related problems by confirming the time required to generate measurement data from the data source. Furthermore, the purpose of this invention is to provide users with a smooth and efficient service based on information about the time required to generate measurement data from the data source. The technical problem to be solved by this invention is not limited to the technical problems mentioned above. Those skilled in the art will clearly understand other technical problems not mentioned below based on the description below. [Technical Means for Solving the Problem] The data processing method of an electronic device according to one embodiment of the present invention is characterized by including the following steps: obtaining a data source related to an e-commerce service; applying a data pipeline to the data source to generate measurement data; and confirming information on the time delay between obtaining the data source and generating the measurement data. In one embodiment, the above-described data processing method is characterized in that: the data pipeline includes a first data processing flow and a second data processing flow; the step of generating the above-described measurement data includes the following steps: applying the first data processing flow to the data source to generate first result data; and applying the second data processing flow to the first result data to generate the above-described measurement data; and the step of confirming the above-described time delay includes the following steps: confirming the entry time and end time corresponding to each of the first data processing flow and the second data processing flow; and based on the entry time and end time corresponding to each of the first data processing flow and the second data processing flow, confirming the information of the above-described time delay corresponding to the data source. In one embodiment, the above-described data processing method is characterized in that the step of confirming the entry time and end time corresponding to each of the first data processing flow and the second data processing flow includes the following step: recording the entry time and end time corresponding to each of the first data processing flow and the second data processing flow in the header of the message corresponding to the data source. In one embodiment, the above-described data processing method is characterized by further including the following steps: generating information for recording the entry time and end time of each of the first data processing flow and the second data processing flow for the data source corresponding to the preset period in the data source. In one embodiment, the data processing method is characterized in that the preset period is determined based on at least one of the characteristics of the data source or the characteristics of the measurement data. In one embodiment, the above-described data processing method is characterized in that: the data source includes at least one of order data, log data, item data, and user membership data associated with the above-described e-commerce service. In one embodiment, the above data processing method is characterized in that: the above measurement data includes at least one of user order-related measurement data, page-related measurement data provided in the above e-commerce service, and item-related measurement data. In one embodiment, the above-described data processing method is characterized in that: each of the first data processing flow and the second data processing flow includes at least one of the following: an ingestion flow, which collects data that can be processed on the data pipeline based on the obtained data source; an aggregation flow, which processes the data that needs to be aggregated from the collected data; a non-aggregation flow, which processes the data that does not need to be aggregated from the collected data; and a publish flow, which updates and publishes the data when the value of the data processed by the ingestion flow or the non-aggregation flow changes. In one embodiment, the data processing method is characterized in that at least one of the aggregation process and the non-aggregation process consists of a plurality of instances, and the time delay information further includes information of the instances. In one embodiment, the above data processing method is characterized by further including the step of displaying a dashboard that includes information on the time delay. In one embodiment, the data processing method is characterized in that when the time delay exceeds a threshold, the dashboard further includes information about the time delay. In one embodiment, the above data processing method is characterized in that the dashboard further includes average time delay information corresponding to the type of measurement data. In one embodiment, the above-described data processing method is characterized by further including the following steps: obtaining a first data source from a user terminal; confirming a preset time period corresponding to the type of measurement data, wherein the measurement data corresponds to the first data source; confirming whether the measurement data has been generated after the preset time period has elapsed since the first data source was received; and, if the measurement data has not been generated, providing information related to the generation of the measurement data to the operator terminal. In one embodiment, the above-described data processing method is characterized in that the preset time period is determined based on the average time delay corresponding to the type of the above-described measurement data. In one embodiment, the data processing method is characterized in that when the measurement data meets preset conditions, the dashboard further includes information related to the measurement data and the preset conditions. One embodiment of the present invention is characterized by a non-transitory storage medium that is a non-transitory computer-readable storage medium that records a program for performing the above-described method on a computer. An embodiment of the present invention provides an electronic device for data processing, characterized by comprising: a transceiver for transmitting and receiving information with other devices; and a processor for acquiring a data source related to an e-commerce service, applying a data pipeline to the data source to generate measurement data, and confirming information on the time delay between acquiring the data source and generating the measurement data. [Effects of the Invention] This invention can provide customized services to users based on various metrics generated from data sources related to e-commerce services, thereby improving user experience. Furthermore, this invention can determine the time required to generate measurement data from the data source, quickly identify service-related issues and take appropriate action, thereby improving the user experience. Furthermore, this invention can provide users with information on the time required to generate measurement data from the data source, thereby providing real-time information according to user requirements. This can increase user trust in e-commerce services. The effects that can be obtained by the present invention are not limited to those mentioned above. Those skilled in the art to which this invention pertains can clearly understand other effects not mentioned based on the following description. In describing the embodiments, descriptions of technical content well-known in the technical field to which this invention pertains and not directly related to this invention are omitted. This is because omitting redundant descriptions clearly conveys the essence of the invention, thus avoiding confusion regarding its main points. For the same reasons, some constituent elements are exaggerated, omitted, or represented in a generalized manner in the accompanying drawings. Furthermore, the dimensions of each constituent element do not perfectly reflect their actual dimensions. In each drawing, the same or corresponding constituent elements are assigned the same reference symbols. The advantages and features of the present invention, and the methods for achieving such advantages and features, will become clear from the accompanying drawings and the embodiments described in detail below. However, the present invention is not limited to the embodiments disclosed below and can be implemented in various different forms. These embodiments are provided only to fully disclose the invention and to enable those skilled in the art to fully understand the scope of the invention. The invention is defined only by the claims of the present invention. Throughout this specification, the same reference numerals denote the same constituent elements. At this point, it should be understood that the combination of each block of the flowchart with the flowchart itself can be implemented by computer program commands. These computer program commands can be loaded onto the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment. Therefore, these commands, executed by the processor of the computer or other programmable data processing equipment, produce methods for implementing the functions described in the blocks of the flowchart. In order to implement the functions in a specific manner, these computer program commands can also be stored in computer-usable or computer-readable memory that is accessible to a computer or other programmable data processing equipment. Therefore, the commands stored in such computer-usable or computer-readable memory can also produce manufactured items containing command methods for implementing the functions described in the blocks of the flowchart. The computer program commands can also be loaded onto a computer or other programmable data processing equipment. Therefore, a series of action steps are performed on the computer or other programmable data processing equipment to produce a process executed by the computer. Thus, the commands executed by the computer or other programmable data processing equipment can also provide steps for executing the functions described in the blocks of the flowchart. Furthermore, each block can represent a module, segment, or portion of code comprising one or more executable instructions used to perform a specific logical function. It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may be executed out of order. For example, two blocks shown successively may actually be executed simultaneously, or occasionally in reverse order depending on their corresponding functions. In this embodiment, the term "~component" refers to software or hardware components such as FPGA (field-programmable gate array) or ASIC (application-specific integrated circuit), and the "~component" performs certain functions. However, the "~component" is not limited to software and hardware. The "~component" can be constructed in a manner residing in addressable storage media, or it can be constructed by reproducing one or more processors. Therefore, as an example, the "~component" includes components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, programs, sub-regularities, code fragments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided internally by the components and the "~component" can be combined into a smaller number of components and the "~component," or they can be further separated into other components and the "~component." Furthermore, the constituent elements and "~" can also be implemented by means of one or more CPUs (central processing units) within the regeneration device or secure multimedia card. The expression "at least one of a, b and c" as used throughout the specification may include "a alone", "b alone", "c alone", "a and b", "a and c", "b and c", or "all three of a, b and c". The term "terminal" as used below can refer to a computer or a portable terminal that can connect to a server or other terminal via a network. Here, a computer includes, for example, a laptop, desktop, or tablet computer equipped with a web browser. A portable terminal, as a wireless communication device ensuring portability and mobility, can include, for example, IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution) communication terminals, as well as all types of handheld wireless communication devices such as smartphones and tablets. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily implement the invention. However, the present invention can be implemented in various different forms and is not limited to the embodiments described herein. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Figure 1 is a schematic diagram illustrating the configuration of various embodiments of a system for processing data. Referring to FIG1, the data processing system of various embodiments of the present invention may include an electronic device 100 and a user terminal 110. On the other hand, the system shown in FIG1 only illustrates the components relevant to embodiments of the present invention. Therefore, those skilled in the art will understand that, in addition to the components shown in FIG1, other common components may be included. In one embodiment, an application providing e-commerce services can be installed on a user terminal 110 corresponding to a user of the e-commerce service. The application connects to a server associated with the e-commerce service under the control of the application and utilizes the e-commerce service based on information exchange with the server. The e-commerce application can include applications for buyers, sellers, delivery personnel, etc., depending on the user's identity. That is, the user terminal 110 can be used by various users, including customers who purchase goods through the service, sellers who sell goods through the service, and delivery personnel who deliver purchased goods from sellers to customers. The user terminal 110 can be a mobile device such as a smartphone or tablet computer, or a static device such as a desktop computer. Any machine capable of setting up and executing e-commerce or delivery intermediary service applications can be used as the user terminal 110 without restriction. In one embodiment, user terminal 110 can exchange information with electronic device 100, which includes a system for processing data. For example, when an order is generated in user terminal 110, electronic device 100 can obtain data related to the order. Also, when a user action occurs in user terminal 110, such as entering a page or clicking a button, electronic device 100 can obtain related log data. Furthermore, electronic device 100 can obtain data sources such as inventory changes or new item registration information from user terminals 110 corresponding to item sellers. Electronic device 100 can store the data sources obtained from user terminal 110 in its database and can generate various metrics required for managing e-commerce services by processing the stored data. For example, electronic device 100 can provide personalized services or quickly detect and respond to system problems by collecting and processing log data from users utilizing e-commerce services. This improves user experience and increases service efficiency. Figure 2 is a schematic diagram illustrating the operation of a data processing system according to one embodiment of the present invention. More specifically, Figure 2 is a schematic diagram illustrating the process by which an electronic device according to one embodiment of the present invention generates measurement data from a data source. Referring to Figure 2, the electronic device 100 can obtain data sources from the user terminal 110. In one embodiment, the data sources obtained from the user terminal 110 may include at least one of the following: order data associated with the e-commerce service, log data, item data, and user membership data. Order data associated with the e-commerce service is data generated during the user's ordering of items, and may include order time, item information, settlement information, and delivery information. Log data associated with the e-commerce service is log information generated by the user during their use of the e-commerce service platform, and may include user login information, page access records, search term information, and click events. Item data associated with the e-commerce service is data related to items sold on the e-commerce service, and may include item information, seller information, item inventory, item reviews and ratings, and discount information. Membership data associated with the e-commerce service is data related to memberships offered on the e-commerce service, and may include whether the user has joined a membership, membership level, membership history, membership cancellation or renewal information, and promotional participation history. On the other hand, in this invention, the data sources available to the electronic device 100 are not limited to the examples described above, and any data that can be generated in relation to e-commerce services can be obtained as a data source. The data pipeline that generates measurement data from a data source obtained by electronic device 100 from user terminal 110 may include an ingestion process 101, which collects data that can be processed on the data pipeline based on the data source obtained from user terminal 110. In one embodiment, the ingestion process may include at least one of extracting, transforming, or loading data included in the data source. Data extraction may include the process of extracting the data required to generate measurement data from various data included in the data source. Data transformation may include the process of processing or transforming the extracted data to a form that is easy to analyze or to convert it into a data form that can be processed in electronic device 100. Data loading may include the process of loading the processed data into a data storage library. If the collection of data that can be processed on the data pipeline in the collection process 101 has been completed, the electronic device 100 can, depending on whether data aggregation is required, implement a non-aggregation process 102 for processing data that does not need to be aggregated, or an aggregation process 103 for processing data that needs to be aggregated. For example, when generating metrics such as individual product order details, user information, or order processing status, data processing can be performed using non-aggregation process 102 without a separate data calculation process. This data processing includes extracting the desired data from the collected data or performing additional processing on it. On the other hand, when obtaining order data related to a user's item orders as a data source to generate metrics such as the number of orders for each customer, daily order volume trends, or monthly sales, the numerical data included in the order data can be used to perform data calculations using aggregation process 103. As described above, data can be processed using either non-aggregation process 102 or aggregation process 103, depending on whether data aggregation is required, thereby efficiently processing data according to its characteristics. In one embodiment, non-aggregated processes and aggregated processes may include a plurality of instances. Furthermore, each of the plurality of instances may be determined based on the type of data source. For example, an aggregated process may include a plurality of instances differentiated according to the type of data source, such as an instance for processing order data, an instance for processing log data, and an instance for processing item data. As described above, dividing the process into a plurality of instances enables efficient handling of large-scale data. Furthermore, by classifying the plurality of instances according to the type of data source, each instance can process data independently and in parallel. This not only improves overall data processing performance but also allows for the selection of a more suitable data processor to handle the corresponding instance based on the characteristics of each data source, thereby efficiently processing data. Moreover, by using a plurality of instances, even if an error occurs in one instance, other instances can continue to operate and process data, thereby providing higher availability and maintaining the stability of the entire system. In one embodiment, the electronic device 100 may implement a publish process 104, which publishes data processed by a non-aggregation process 102 or an aggregation process 103. For example, the publish process 104 may include the process of transferring data processed in previous steps of the data pipeline to an external system. In this case, if necessary, the published data may be stored in a data repository for subsequent querying or analysis. Furthermore, if there are changes between the time the data was obtained and the time the data was published, the process of updating the data may also be included in the publish process 104. Through the above process, measurement data is generated, which e-commerce service providers can use to develop business strategies or provide users with desired information. On the other hand, in this invention, the process constituting the data pipeline for generating measurement data from the data source is not limited to the above example, and may include any data processing process for processing the data source to generate the desired measurement data. In one embodiment, the electronic device 100 may implement a monitoring mechanism to identify and manage the time latency between the time of obtaining a data source and the time of generating measurement data. To this end, the electronic device 100 may track the time latency corresponding to each data processing flow within the data pipeline. For example, the electronic device 100 may identify the entry time and leave time of each of a plurality of data processing flows, and based on this, may identify the time latency corresponding to the entire data pipeline for a specific data source. At this time, the entry and exit times corresponding to each of the plurality of data processing flows included in the data pipeline may be recorded in the header of the message corresponding to the data source, and as data processing flows are performed according to the data pipeline, entry and exit time information corresponding to previous data processing flows may be accumulated and provided. As described above, when information on the time latency of each of the data processing flows included in the data pipeline becomes available, delays or bottlenecks in each flow can be identified and measures taken. In one embodiment, the message corresponding to the data source may be a Kafka message, which may be generated in response to the data source when the electronic device 100 obtains the data source and enters the data pipeline starting from the collection process 101. Relatedly, referring to Figure 2, an example of the time delay information recorded in the header of the message corresponding to each data processing flow is shown. First, when a data source is obtained from the user terminal 110 and the collection flow 101 is entered, a message corresponding to the data source is generated. The header of the message records information indicating that the time of entering the collection flow 101 is 2023 / 12 / 12 20:20:20 and the time of ending the collection flow 101 is 2023 / 12 / 12 20:20:21. From this, it can be known that the time required for the collection flow 101 is 1 second. Next, when performing non-aggregation process 102 to process the result data already processed in collection process 101, the information indicating that the time of entry into non-aggregation process 102 is 2023 / 12 / 12 20:20:22 and the time of end of non-aggregation process 102 is 2023 / 12 / 12 20:20:25 can be recorded in the header of the message corresponding to the data source. At this time, the entry and end time information of collection process 101 corresponding to the previous process of non-aggregation process 102 can be accumulated to record the entry and end time information of non-aggregation process 102. Thus, it can be known that the time required for non-aggregation process 102 is 3 seconds. Similarly, for aggregation process 103, the time delay information related to collection process 101 can also be accumulated, and the time delay information of aggregation process 103 can be recorded in the header of the message corresponding to the data source. Furthermore, in the case of publishing process 104, since it is a process that processes all data generated from non-aggregation process 102 and aggregation process 103, after publishing process 104 ends, the header of the message corresponding to the data source can include all time delay information related to non-aggregation process 102 and aggregation process 103, and these can be accumulated to include time delay information related to publishing process 104. At this time, the recording order of time delay information related to non-aggregation process 102 and time delay information related to aggregation process 103 can be determined based on the entry time or end time corresponding to non-aggregation process 102 and aggregation process 103. Also, duplicate parts in the time delay information related to non-aggregation process 102 and time information related to aggregation process 103 can be recorded only once. When measurement data is finally generated with the end of publishing process 104, electronic device 100 can confirm the time delay information corresponding to the data source based on the recorded information. In this example, the time interval between the time when the data source enters the collection process 101 and the time when the publishing process 104 ends is the time delay corresponding to the data source, and based on the time information shown in Figure 2, it can be known that the time delay is 17 seconds. In one embodiment, as shown in FIG2, the time delay information recorded in the header of the message corresponding to the data source may further include at least one of the following: information indicating which data processing flow in the data pipeline the entry and exit times correspond to (station in FIG2), and information indicating which instance of the flow corresponds to (instance in FIG2). In this case, instance information may also be included only when the flow consists of multiple instances. In one embodiment, the electronic device 100 may confirm time delay information only for a portion of the acquired data sources. Furthermore, the electronic device 100 may determine the period for confirming time delay information for a specific data source by considering various characteristics of the data source or the measurement data to be acquired. For example, when a data source is data that is updated in real time, such as order data, the electronic device 100 may set a period of 1 second to confirm the time delay information of that data source. On the other hand, when the data source is log data that is updated at a fixed period, the electronic device 100 may confirm the time delay information of that data source at a longer period. As described above, by determining the period for confirming time delay information based on the characteristics of the data source or measurement data, time delay information can be efficiently monitored for the data source, which helps with performance management. In one embodiment, the electronic device 100 may generate a dashboard 105 including time delay information related to the data pipeline that generates the measurement data, and the generated dashboard 105 may be displayed via a display built into the electronic device 100 or an external display connected to the electronic device 100. The dashboard 105 may include only the time delay information corresponding to the entire data pipeline, or it may include process processing time information for each data processing flow within the data pipeline. In one embodiment, the electronic device 100 may generate a dashboard 105 including information related to the measurement data generated by the data pipeline. The dashboard 105 may include various measurement data related to e-commerce services, and e-commerce service providers may establish business strategies or identify and respond to problems occurring in the service based on the measurement data displayed in the dashboard 105. Hereinafter, various embodiments of the dashboards of the present invention and methods for using the information displayed in the dashboards will be described with reference to FIGS. 4 to 5. Figures 3a and 3b are flowcharts illustrating the operation of electronic devices that implement data processing methods according to various embodiments of the present invention. First, referring to FIG3a, in step S310, the electronic device 100 can obtain data sources related to the e-commerce service from multiple user terminals 110. As mentioned above, the data sources obtained by the electronic device 100 may include at least one of order data, log data, item data, and user membership data related to the e-commerce service, but are not limited to this, and may obtain any data that can be generated in relation to the e-commerce service as a data source. In step S320, the electronic device 100 can apply data pipelines to the acquired data source to generate measurement data. At this time, different measurement data are generated depending on the type of data source acquired. For example, when order data associated with e-commerce services is used as a data source, the data source may include order time, item information, and user information. Based on this, an appropriate data pipeline is applied to process the order data, thereby generating metrics including the user's total number of orders, total order amount, and membership information. More specifically, for example, when a user's membership level on an e-commerce service changes based on their total number of orders or total order amount, the user's total number of orders and total order amount can be updated based on data sources associated with the user's orders, and metrics including the user's membership level and whether the membership level has changed can be generated. For example, when obtaining log data from users' e-commerce services, including page visit records, item searches, and page load times, as a data source, a data pipeline for processing this log data can be applied to generate metrics such as page visitor count or page load time. For instance, page visitor count can be calculated as the number of existing users accessing the page, and page load time can be calculated as the average page load time, thus including these metrics in the overall metrics. As mentioned above, different measurement data can be generated depending on the type of data included in the data source. In this case, the process of applying the data pipeline to the obtained data source to generate measurement data may include the process shown in Figure 2 above, and the types of data processing flows included in the data pipeline may vary depending on the type of data source or the measurement data to be generated. In step S330, the electronic device 100 can confirm the time delay information between the time of obtaining the data source and the time of generating the measurement data, based on the data source obtained in step S310. E-commerce service providers can use time delay information between the acquisition time of data sources and the generation time of measurement data to optimize service performance. For example, they can identify data sources that require a longer time to acquire measurement data and optimize the data flow used to process those sources, thereby shortening the acquisition time. Furthermore, time delay information can be used to monitor the acquisition of data sources and the generation of measurement data in real time, issuing alerts when delays exceed the expected time for prompt action. E-commerce service providers can also use time delay information to optimize resource allocation for data processing and prioritize the processing of critical data sources. As mentioned above, by utilizing time delay information up to the point where data pipelines are applied to data sources to generate measurement data as the final result, e-commerce services can be optimized, thereby improving the customer experience. Therefore, e-commerce service providers need to identify this time delay. According to one embodiment, Figure 3b illustrates the process of confirming information on the time delay until a data pipeline comprising multiple data processing flows is applied to the data source to generate measurement data. Referring to Figure 3b, in step S331, the electronic device 100 can confirm the entry time and end time corresponding to each of the plurality of data processing flows constituting the data pipeline. At this time, the entry time corresponding to a data processing flow refers to the time when the data source or data processed in a previous data flow is transmitted to that data processing flow and the data processing flow begins; the end time corresponding to a data processing flow refers to the time when the data processing corresponding to that data processing flow is completed and the result data corresponding to that data processing flow is generated. For example, when the data pipeline used to generate measurement data from a data source includes a first data processing flow and a second data processing flow, the first data processing flow can be applied to the data source obtained in the electronic device 100 to generate first result data, and the second data processing flow can be applied to the first result data to generate measurement data. In this case, the entry time corresponding to the first data processing flow refers to the time when the data source is transmitted to the module used for the first data processing flow and the first data processing flow begins, and the end time refers to the time when the data processing corresponding to the first data processing flow is completed and the first result data is generated. Similarly, the entry time corresponding to the second data processing flow refers to the time when the first result data is transmitted to the module used for the second data processing flow and the second data processing flow begins, and the end time refers to the time when the data processing corresponding to the second data processing flow is completed and the measurement data is generated. As described above with reference to FIG2, the entry and exit times corresponding to each data processing flow constituting the data pipeline can be recorded in a message generated when the electronic device 100 obtains a data source and the data source enters the data pipeline for data processing. In particular, this message can be recorded in the header of the message to facilitate the collection of relevant information. Furthermore, as multiple data processing flows constituting the data pipeline proceed sequentially, the entry and exit times corresponding to each data processing flow can be accumulated and recorded. In one embodiment, the entry and exit times corresponding to each of the plurality of data processing flows constituting the data pipeline can be recorded in the header of the message corresponding to each of the plurality of data processing flows. For example, when the data pipeline consists of a first data processing flow and a second data processing flow, the entry and exit times corresponding to the first data processing flow can be recorded in the header of a first message, and the entry and exit times corresponding to the second data processing flow can be recorded in the header of a second message. The second message can be generated when data enters the second data processing flow on the data pipeline, and the entry and exit time information corresponding to the first data processing flow can be obtained from the first message corresponding to the first data processing flow and recorded in the header of the second message. Furthermore, when there is a third data processing flow that differs from the first data processing flow preceding the second data processing flow, the entry and end times corresponding to the third data processing flow can be obtained not only from the first message but also from the third message corresponding to the third data processing flow, and recorded in the header of the second message. As described above, the header of the second message records not only the entry and end times corresponding to the second data processing flow but also the entry and end times corresponding to the first and third data processing flows preceding the second data processing flow. Therefore, even if only the second message is confirmed, the entry and end time information corresponding to the previous data processing flows can be confirmed, thereby enabling efficient management of time delay information. Subsequently, in step S332, the electronic device 100 can determine the time delay corresponding to the data source based on the entry time and end time corresponding to each of the plurality of data processing flows. For example, when the data pipeline used to generate measurement data from the data source includes a first data processing flow and a second data processing flow, step S331 can be used to confirm the entry time and end time corresponding to each of the first and second data processing flows, and based on this, the time interval between the entry time corresponding to the first data processing flow and the end time corresponding to the second data processing flow can be confirmed as the time delay value corresponding to the data source. Referring again to Figure 3a, in step S340, the electronic device 100 can display a dashboard including time delay information for the data source obtained in step S310. In one embodiment, the electronic device 100 may display a dashboard including information derived from an analysis of the service and / or users utilizing the service based on time delay information confirmed through previous steps. As described above, the dashboard provides important business intelligence to e-commerce service providers and offers the ability to visually confirm various information. Such a dashboard may include various types of information. Hereinafter, with reference to Figures 4 and 5, examples of dashboards including various types of information related to service utilization, including time delay information, will be described. Figure 4 is a diagram of an instrument panel according to one embodiment of the present invention. Referring to Figure 4, the dashboard 400 may include process processing time information 410, 420, and 430 for each data processing flow within the data pipeline, and time delay information 440 corresponding to the entire data pipeline. The dashboard 400 may include process processing time information for the entire plurality of data processing flows within the data pipeline, or it may include process processing time information for a portion of the data processing flows selected by the e-commerce service provider. In one embodiment, the process processing time information and time delay information may be updated in real time, or they may be updated according to the periodicity of the confirmed time delay information. As described above, by displaying process processing time and delay information on the dashboard, e-commerce service providers can visually assess the overall performance of the system. Furthermore, for data displayed on the dashboard that is expected to be abnormal, system errors can be identified and addressed in advance, even without additional user error reports. Additionally, when buyers or sellers make inquiries related to data processing, the average processing time can be determined, and this information can be provided to the buyers or sellers, thereby increasing user satisfaction and trust in the e-commerce service. In one embodiment, the dashboard 400 may include information 411, 421, 431, 441 that sets threshold values ​​for each of the process processing time information 410, 420, 430 and time delay information 440. The set threshold values ​​may be values ​​set based on data obtained from previous data processing flows, or they may be data processing time target values ​​set by the e-commerce service provider for a specific data source. In one embodiment, when the process processing time or the time delay corresponding to the entire data pipeline exceeds a set threshold, the dashboard can display information about the process processing time or time delay. The displayed information may include information about which process experienced the exceeding threshold processing time or time delay, and may also include information about the degree of deviation from the threshold. Furthermore, by transmitting relevant notifications or messages to the e-commerce service provider's terminal, the provider can be informed that a process processing time or time delay exceeding the threshold has occurred. Through such warning messages, the e-commerce service provider can quickly identify the problem and take appropriate action. For example, when a threshold is set based on data obtained from previous data processing flows, a processing time exceeding the threshold indicates a delay in data processing compared to previously normal service delivery. In such cases, e-commerce service providers can use notifications or messages, or dashboards providing relevant data, to identify which part of the process experienced the delay at what point in time. They can also analyze the data source or the module processing the corresponding flow at that point in time to understand the cause of the problem and take corrective action. Furthermore, if the estimated time required to resolve the issue is longer than expected, relevant information can be provided to users of the e-commerce service to minimize user inconvenience. For example, when a target value for data processing time is set as a threshold, a processing time exceeding the threshold indicates that the data processing module's performance is not meeting expectations. In this case, the e-commerce service provider can identify which data processing module's performance is failing to meet expectations through notifications or messages, or through dashboards providing relevant data. Based on this information, the e-commerce service provider can upgrade the hardware corresponding to the relevant data processing module or optimize the corresponding data processing algorithm to achieve the target value. Alternatively, the required time can be shortened by parallelizing the relevant data processing flow. As described above, the e-commerce service provider can improve the performance of data processing flows based on the data processing time information provided by the dashboard, thereby achieving the target value. Figure 5 is a diagram of an instrument panel according to another embodiment of the present invention. Referring to Figure 5, the dashboard 500 may include the following areas: an area 510 including time delay information, an area 520 displaying notifications related to the time delay information, an area 530 displaying time delay information for a specific process within the data pipeline, and an area 540 displaying time delay information for the entire process within the data pipeline. First, the time delay information displayed in region 510 can include corresponding average time delay information depending on the type of metric. In this case, the average time delay information can be obtained from time delay information acquired over a fixed period (e.g., one week or one month) related to the corresponding metric. In one embodiment, if there is a time delay exceeding a preset threshold in the time delay information acquired related to the corresponding metric, that time delay can be excluded, and the remaining time delay information can be used to calculate the average time delay. This prevents outliers from distorting the average and obtains accurate average time delay information. Furthermore, the time delay information displayed in region 510 can also include only the time delay information corresponding to metrics with average time delay values ​​exceeding a preset benchmark. As described above, by displaying only metrics with time delay values ​​exceeding the benchmark, modules requiring optimization to minimize time delay can be easily identified. Area 520 can display various notifications related to time delay information. For example, area 520 can display messages that occur when the time delay corresponding to a specific process or the entire process exceeds a preset threshold, as described above with reference to FIG4. In this case, the message may include information about which data source and in which process the time delay exceeding the threshold occurred. Furthermore, if a specific metric is not generated within the expected time, a related message can be displayed in area 520. For example, when attempting to obtain a first data source from a user terminal to generate the first metric, the expected time required to generate the first metric can be confirmed, and if the first metric is still not generated after the expected time, a message can be displayed. By displaying this message, the service provider can confirm whether there are errors in the relevant data processing flow and can respond quickly when errors are found. In this case, the expected time required to generate the first metric can be determined based on the average time delay corresponding to the type of the first metric. For example, 120% of the average time delay can be determined as the expected time, and if the metric is still not generated after the expected time, it can be determined that an anomaly has occurred and a message can be displayed. Furthermore, when the generated measurement data meets preset conditions, relevant information can be displayed in area 520. For example, by applying a data pipeline to a data source related to the ordering of an item to generate measurement data related to the order quantity of item 1, relevant information can be displayed in area 520 when the order quantity of item 1 meets preset conditions. The service provider can confirm from this information that item 1 has met the expected demand and can make decisions such as inventory management or supply chain planning for item 1 based on this. For example, log data related to service usage can be used as a data source to generate metrics related to the loading time of page 1. When the loading time of page 1 exceeds a set threshold, relevant information can be displayed in area 520. The service provider can use this information to confirm that the loading time of page 1 is increasing, and can use this information to consider page optimization or server resource adjustments, thereby improving the user experience. In addition to the examples above, any service-related information can also be displayed on the dashboard in the following situations: order processing time exceeds the set maximum allowed time, server traffic becomes higher than expected, user activity decreases during a specific period, or core components such as the data pipeline or server are expected to fail. As mentioned above, e-commerce service providers can set multiple conditions related to specific metrics and display them on the dashboard when the conditions are met. This allows for real-time monitoring of various service-related information and enables appropriate measures to be taken as necessary to maintain and improve the stability and performance of the system and services. The time delay information for a specific process displayed in area 530 and the time delay information for the entire process displayed in area 540 may include the content described above with reference to Figure 4. Therefore, the description of the content that is repeated in Figure 4 is omitted. Furthermore, the dashboard 500 may also include information on the generated metrics. In one embodiment, the metrics included in the dashboard may be user order-related metrics, such as the number of orders, order amount, and membership information. The service provider can select a specific user to check on the dashboard and display the metrics generated in relation to that user. For example, the metrics included in the dashboard may be page-related metrics provided in the service, such as page load time, number of page visitors, and bounce rate. The service provider can select a specific page to check on the dashboard and display the metrics generated in relation to that page. For example, the metrics included in the dashboard may be item-related metrics, such as the popularity and inventory level of an item or category. The service provider can check the popular items and categories throughout the service on the dashboard, and can also select a store to check its inventory information. E-commerce service providers can use the various information included in the dashboards shown in Figures 4 and 5 to manage their services and develop business strategies to improve user experience. For example, when an e-commerce service provider is configured to issue coupons based on the number of user purchases, the data pipeline must process the data generated from user purchases to produce metrics related to the number of purchases before it can determine whether to issue a coupon. In this case, the average time to generate these metrics is 10 seconds. If a user wants to confirm whether a coupon has been issued within 10 seconds of the purchase completion page being displayed, the coupon may not have been issued yet. Therefore, users may express dissatisfaction with the delayed coupon issuance. To prevent this, service providers that display an average delay of 10 seconds in the metrics related to the number of user purchases on their dashboard can provide users with information about events or items that can pique their interest on the purchase completion page, allowing them to focus on other interests before the coupon is issued. This improves the user experience and minimizes dissatisfaction caused by delayed coupon issuance. For example, real-time customer support services can be operated using metrics on response times to user inquiries or requests. When delays occur in responding to user inquiries or requests, relevant information can be displayed on a dashboard, and the service provider can send alerts to the customer support team to ensure rapid responses and improve user satisfaction. Furthermore, information on estimated response times can be provided based on average response times to user inquiries or requests, thereby minimizing user dissatisfaction caused by response delays. As mentioned above, e-commerce service providers can not only visually identify various service-related information through dashboards that include time delay information and generated metrics, but also use the information provided to detect service-related errors or develop business strategies to optimize services, thereby efficiently managing services and improving user experience. Figure 6 is a schematic diagram illustrating the various configurations of an electronic device according to one embodiment of the present invention. Referring to FIG6, electronic device 600 may include transceiver 610, processor 620, and memory 630. FIG6 only shows the components relevant to this embodiment in electronic device 600. Therefore, it will be apparent to those skilled in the art that electronic device 600 may further include other common components in addition to those shown in FIG6. Since electronic device 600 of FIG6 may be included in electronic device 100 of FIG1 and FIG2, descriptions of content repeated in FIG1 to FIG5 are omitted. The transceiver 610 can communicate with other devices. Therefore, the electronic device 600 can send and receive information with other devices via the transceiver. For example, the electronic device 600 can communicate with the user terminal 110 or with other devices via the transceiver. Here, communication, i.e., the transmission and reception of data, can be achieved via wired or wireless means. Therefore, transceiver 610 may include a wired communication module connected to the Internet via a LAN (Local Area Network), a mobile communication module connected to a mobile communication network via a mobile communication base station to transmit and receive data, a short-range communication module utilizing communication methods such as Wi-Fi (Wireless Local Area Network) or Bluetooth (Zigbee) (WPAN) communication methods, a satellite communication module utilizing GPS (Global Positioning System) or GNSS (Global Navigation Satellite System), or combinations thereof. The processor 620 is responsible for controlling the overall operation of the electronic device 600. To this end, the processor 620 can perform various information calculations and processing. For example, the processor 620 can perform various calculations and processing to generate measurement data based on the data source received from the user terminal 110 via the transceiver 610. The processor 620 can be implemented as a computer or a similar device, depending on hardware, software, or a combination thereof. In terms of hardware, the processor 620 can be implemented as a circuit that processes electrical signals to perform control functions; in terms of software, it can be implemented as a program that drives the hardware processor 620. On the other hand, unless specifically mentioned in the following description, it can be interpreted as the operation of an electronic device being performed by the control of the processor 620. That is, when a module implemented in the system for processing data is executed, the module can be interpreted as controlling the processor 620 to perform the aforementioned operations of the electronic device. For example, the processor 620 can obtain data sources related to the use of e-commerce services from multiple user terminals via transceiver 610, and can generate desired measurement data based on the obtained data sources. Furthermore, the processor 620 can confirm the data processing time information of the entire process or part of the process of generating measurement data from the data source, and can display the data processing time information or the generated measurement data information on the dashboard through the display included in the electronic device 600 or a separate display connected to the electronic device 600. Memory 630 can store various types of information. Memory 630 can store data temporarily or semi-permanently. For example, the memory 630 of electronic device 600 can store the operating system (OS) used to drive electronic device 600, data used to manage websites, or data used to generate Braille and application programs (e.g., website applications). Furthermore, memory 630 can store modules in the form of computer code as described above. Examples of memory 630 may include hard disk drives (HDDs), solid state drives (SSDs), flash memory, read-only memory (ROM), and random access memory (RAM). This type of memory 630 can be provided in either an internal or removable form. In short, various embodiments can be implemented by various mechanisms. For example, various embodiments can be implemented by hardware, firmware, software, or combinations thereof. In the case of implementation via hardware, the methods of various embodiments can be implemented by one or more ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc. When implemented using firmware or software, the methods of various embodiments can be implemented in the form of modules, programs, or functions that perform the functions or actions described above. For example, software code can be stored in memory and driven by a processor. The memory can be located inside or outside the processor and can send and receive data with the processor via various known mechanisms. The electronic device described in the above embodiments may include a processor, memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with external devices, and a user interface device such as a touch panel, keys, or buttons. Computer-readable code or program commands executable on the processor, implemented using software modules or algorithms, can be stored on a computer-readable recording medium. Here, computer-readable recording media include magnetic storage media (e.g., ROM (read-only memory), RAM (random-access memory), floppy disks, hard disks, etc.) and optical reading media (e.g., CD-ROM, Digital Versatile Disc (DVD)). The computer-readable recording medium is distributed across a networked computer system, enabling distributed storage and execution of computer-readable code. The medium can be read by a computer, stored in memory, and executed in a processor. This embodiment can be represented by functional blocks and various processing steps. These functional blocks can be implemented by different numbers of hardware and / or software components performing specific functions. For example, the embodiment can employ integrated circuits capable of performing various functions via the control of one or more microprocessors or other control devices, such as memory, processing, logic, lookup tables, etc. The components can be executed by software programs or software elements. Similarly, this embodiment includes algorithms implemented as combinations of data structures, programs, conventions, or other programs, and therefore can be implemented using programming or scripting languages ​​such as C, C++, Java, and assemblers. Functionally, it can be implemented by algorithms executed in one or more processors. Furthermore, this embodiment can employ prior art for electronic environment setup, signal processing, and / or data processing. Terms such as "mechanism," "component," "mechanism," and "structure" are used broadly and are not limited to mechanical and physical structures. The above terms may be associated with processors and include the meaning of a series of software processes. The above embodiment is only one example, and other embodiments can be implemented within the scope of the invention application described below. 100: Electronic Device 101: Collection Process 102: Non-aggregated Process 103: Aggregated Process 104: Release Process 105: Dashboard 110: User Terminal 400: Dashboard 410, 420, 430: Processing Time Information 411, 421, 431, 441: Critical Value Information 440: Time Delay Information 500: Dashboard 510: Region 520: Region 530: Region 540: Region 600: Electronic Device 610: Transceiver 620: Processor 630: Memory S310: Step S320: Step S330: Step S331: Step S332: Step S340: Step Figure 1 is a schematic diagram illustrating the configuration of a system for processing data according to various embodiments. Figure 2 is a schematic diagram illustrating the configuration of a system for processing data according to one embodiment of the present invention. Figures 3a and 3b are flowcharts illustrating the operation of an electronic device for implementing a method for processing data according to various embodiments of the present invention. Figure 4 is a diagram illustrating an instrument panel according to one embodiment of the present invention. Figure 5 is a diagram illustrating an instrument panel according to one embodiment of the present invention. Figure 6 is a diagram illustrating the various configurations of an electronic device according to one embodiment of the present invention. S310: Steps S320: Steps S330: Steps S340: Steps

Claims

1. A data processing method performed via an electronic device, comprising the steps of: obtaining a data source related to an e-commerce service; applying a data pipeline to the data source to generate measurement data; and confirming information on the time delay between obtaining the data source and generating the measurement data; wherein, The aforementioned metric data is generated by executing one or more data processing flows included in the aforementioned data pipeline on the aforementioned data source; wherein, the aforementioned time delay corresponds to the time consumed from the time the aforementioned data source generates the aforementioned metric data by completing the execution of the aforementioned one or more data processing flows; wherein the aforementioned one or more data processing flows include a first data processing flow and a second data processing flow; the steps for generating the aforementioned metric data include the following steps: applying the aforementioned first data processing flow to the aforementioned data source to generate first result data; and applying the aforementioned second data processing flow to the aforementioned first result data to generate the aforementioned metric data; and the steps for confirming the aforementioned time delay information include the following steps: confirming the entry time and end time corresponding to each of the aforementioned first data processing flow and the aforementioned second data processing flow; and based on the aforementioned entry time and the aforementioned end time corresponding to each of the aforementioned first data processing flow and the aforementioned second data processing flow, confirming the aforementioned time delay information corresponding to the aforementioned data source; wherein, the aforementioned entry time corresponding to the aforementioned first data processing flow corresponds to the time at which the aforementioned first data processing flow is applied to the aforementioned data source. Wherein, the end time corresponding to the first data processing flow corresponds to the time when the first result data is generated upon completion of the first data processing flow; wherein, the entry time corresponding to the second data processing flow corresponds to the time when the second data processing flow is applied to the first result data; wherein, the end time corresponding to the second data processing flow corresponds to the time when the measurement data is generated upon completion of the second data processing flow.

2. The data processing method of Request 1, wherein the step of confirming the entry time and end time corresponding to each of the first data processing flow and the second data processing flow mentioned above includes the following steps: Recording the entry time and end time corresponding to each of the first data processing flow and the second data processing flow mentioned above in the header of the message corresponding to the data source mentioned above.

3. The data processing method of request item 1 further includes the following steps: For the data source corresponding to the preset period in the above data source, generate information to record the entry time and end time of each of the above first data processing flow and the above second data processing flow.

4. The data processing method of request item 3, wherein the preset period is determined based on at least one of the characteristics of the data source or the characteristics of the measurement data.

5. The data processing method as described in Request 1, wherein the aforementioned data sources include at least one of the following: order data, log data, item data, and user membership data associated with the aforementioned e-commerce service.

6. The data processing method as described in Request 1, wherein the aforementioned measurement data includes at least one of the following: user order-related measurement data, page-related measurement data provided in the aforementioned e-commerce service, and item-related measurement data.

7. The data processing method of Request 1, wherein each of the first data processing flow and the second data processing flow comprises at least one of the following: a collection flow, which collects data that can be processed on the data pipeline based on the obtained data source; an aggregation flow, which processes the data that needs to be aggregated from the collected data; a non-aggregation flow, which processes the data that does not need to be aggregated from the collected data; and a publishing flow, which updates and publishes the data when the value of the data processed by the collection flow or the non-aggregation flow changes.

8. The data processing method as described in Request 7, wherein at least one of the above-mentioned aggregation process and the above-mentioned non-aggregation process consists of a plurality of instances, and the above-mentioned time delay information further includes information of the above-mentioned instances.

9. The data processing method of request item 1 further includes the following steps: Displaying a dashboard that includes information on the aforementioned time delay.

10. The data processing method of claim 9, wherein when the aforementioned time delay exceeds a threshold, the dashboard further includes information about the aforementioned time delay.

11. The data processing method as described in Request 9, wherein the aforementioned dashboard further includes average time delay information corresponding to the type of measurement data.

12. The data processing method of Request 1 further includes the following steps: obtaining a first data source from a user terminal; confirming a preset time period corresponding to the type of measurement data, wherein the measurement data corresponds to the first data source; confirming whether the measurement data has been generated after the preset time period elapsed since the first data source was received; and, if the measurement data has not been generated, providing information related to the generation of the measurement data to the operator terminal.

13. The data processing method of request item 12, wherein the preset time period is determined based on the average time delay corresponding to the type of the aforementioned measurement data.

14. The data processing method of claim 9, wherein when the aforementioned measurement data meets preset conditions, the aforementioned dashboard further includes information related to the aforementioned measurement data and the aforementioned preset conditions.

15. A non-transitory computer-readable storage medium containing a program for performing the method described in claim 1 on a computer.

16. An electronic device for data processing, comprising: transceiver It can send and receive information with other devices; A processor acquires a data source related to an e-commerce service, applies a data pipeline to the data source to generate measurement data, and confirms information about the time delay between acquiring the data source and generating the measurement data; wherein the measurement data is generated by executing one or more data processing flows included in the data pipeline on the data source; wherein the time delay corresponds to the time consumed from the time the data source generates the measurement data to the completion of executing the one or more data processing flows; wherein the one or more data processing flows include a first data processing flow and a second data processing flow; wherein the processor generates the measurement data by: applying the first data processing flow to the data source to generate first result data; and applying the second data processing flow to the first result data to generate the measurement data; and wherein the processor confirms information about the time delay by: Confirm the entry time and end time corresponding to each of the first and second data processing flows described above; and based on the entry time and end time corresponding to each of the first and second data processing flows described above, confirm the time delay information corresponding to the data source; wherein, the entry time corresponding to the first data processing flow describes the time at which the first data processing flow is applied to the data source; wherein, the end time corresponding to the first data processing flow describes the time at which the first result data is generated upon completion of the first data processing flow; wherein, the entry time corresponding to the second data processing flow describes the time at which the second data processing flow is applied to the first result data; wherein, the end time corresponding to the second data processing flow describes the time at which the measurement data is generated upon completion of the second data processing flow.

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