Data processing method and device, medium, electronic equipment and program product

Abstract

The invention relates to a data processing method and device, a medium, electronic equipment and a program product, and the data processing method comprises the steps: obtaining M pieces of buried point data generated by a preset object in M scene domains, the buried point data being generated when a preset event of the preset object is triggered; converting the M pieces of burying point data into M pieces of burying point conversion data with the same dimension quantity, the same dimension semantics and the same dimension data format; and for each piece of burying point conversion data, aggregating data under an event dimension according to at least one of a time dimension, a user information dimension and a geographic dimension to obtain aggregated data of a preset event, and displaying the aggregated data. By converting, aggregating and uniformly displaying the burying point data, a user can be assisted to more clearly know the triggering conditions of events in different scene domains, so that the burying point data analysis efficiency can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular, to a data processing method, device, medium, electronic device and program product. Background Art

[0002] Tracking is to collect some information in a specific process of the application. That is, based on tracking, user behavior data or other specified types of information can be collected in a specific process node of the application. Here, the collected data and information can be called tracking data. On this basis, the collected tracking data is processed and subsequent product optimization is carried out based on the processing results, or the processing results are used to study user behavior habits, etc. Therefore, how to better process tracking data is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The present disclosure provides a data processing method, device, medium, electronic device and program product, which can improve the efficiency of point-of-sight data processing by converting and aggregating point-of-sight data in different scene domains.

[0004] In a first aspect, the present disclosure provides a data processing method, comprising: Obtain M pieces of buried data generated by a preset object in M ​​scene domains, wherein the buried data is generated when a preset event for the preset object is triggered, and the buried data includes multiple data dimensions, and the buried data generated in different scene domains have different numbers of dimensions, dimension semantics, and dimension data formats; Convert the M pieces of tracking data into M pieces of tracking conversion data with the same number of dimensions, the same dimension semantics, and the same dimension data format, wherein the data dimensions of the M pieces of tracking conversion data include an event dimension, and at least one of a time dimension, a user information dimension, and a geographic dimension; For each piece of the buried point conversion data, the data under the event dimension is aggregated according to at least one of the time dimension, the user information dimension and the geographic dimension to obtain the aggregated data of the preset event, and the aggregated data is displayed.

[0005] In a second aspect, the present disclosure provides a data processing device, comprising: an acquisition module, configured to acquire M pieces of buried data generated by a preset object in M ​​scene domains, wherein the buried data is generated when a preset event for the preset object is triggered, and the buried data includes multiple data dimensions, and the buried data generated in different scene domains have different numbers of dimensions, dimension semantics, and dimension data formats; a conversion module configured to convert the M pieces of tracking data into M pieces of tracking conversion data having the same number of dimensions, the same dimension semantics, and the same dimension data format, wherein the data dimensions of the M pieces of tracking conversion data include an event dimension, and at least one of a time dimension, a user information dimension, and a geographic dimension; The aggregation module is configured to aggregate the data under the event dimension according to at least one of the time dimension, the user information dimension and the geographic dimension for each of the buried point conversion data, obtain the aggregated data of the preset event, and display the aggregated data.

[0006] In a third aspect, the present disclosure provides a computer-readable medium having a computer program stored thereon, which implements the steps of the method described in the first aspect when the computer program is executed by a processing device.

[0007] In a fourth aspect, the present disclosure provides an electronic device, including: a storage device having a computer program stored thereon; A processing device is used to execute the computer program in the storage device to implement the steps of the method in the first aspect.

[0008] In a fifth aspect, the present disclosure provides a computer program product, comprising a computer program, which implements the steps of the method described in the first aspect when executed by a processor.

[0009] Through the above technical scheme, after obtaining M pieces of buried point data generated by the preset object in M ​​scene domains, the cost of data analysis can be reduced by converting and aggregating the M pieces of buried point data, wherein the buried point data is generated when the preset event of the preset object is triggered, and the buried point data may include multiple data dimensions. The buying point data generated in different scene domains have different number of dimensions, dimension semantics and dimension data formats. On this basis, the M pieces of buried point data are converted into M pieces of buried point conversion data with the same number of dimensions, the same dimension semantics and the same dimension data format. Here, the data dimensions of the M pieces of buried point conversion data include the event dimension, and at least one of the time dimension, the user information dimension and the geographic dimension. Then, for each piece of buried point conversion data, the data under the event dimension is aggregated according to at least one of the time dimension, the user information dimension and the geographic dimension to obtain the aggregated data of the preset event, and the aggregated data is displayed. Since this scheme converts the M pieces of buried point data into a unified format, the number of dimensions, the dimension semantics and the dimension data format of the buried point data under different scene domains are consistent, so the data analysis efficiency can be improved. In addition, for each buried point conversion data, this solution aggregates the data under the event dimension according to at least one of the time dimension, user information dimension and geographic dimension, and displays the aggregated data, which can make the final displayed content more clearly and accurately reflect the differences between the data. In other words, this solution can uniformly process the data under multiple scene domains, without having to process each scene domain separately, and can avoid analyzing the data under M scene domains separately, thereby improving the efficiency of buried point data processing.

[0010] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings: Figure 1 It is an example diagram of point-of-sight data processing in different scene domains in the related technology shown according to an exemplary embodiment of the present disclosure.

[0012] Figure 2 The figure is a schematic diagram of an application environment of a data processing method according to an exemplary embodiment of the present disclosure.

[0013] Figure 3 The figure is a flow chart of a data processing method according to an exemplary embodiment of the present disclosure.

[0014] Figure 4 This is a display example diagram of the same preset object under application A in a data processing method according to an exemplary embodiment of the present disclosure.

[0015] Figure 5 This is a display example diagram of the same preset object under application B in a data processing method according to an exemplary embodiment of the present disclosure.

[0016] Figure 6 This is an example diagram showing data from different scenario domains displayed in the same visual analysis chart in a data processing method according to an exemplary embodiment of the present disclosure.

[0017] Figure 7 This is an example diagram of visually displaying aggregated data of preset events of a preset object in a scene domain A in a data processing method according to an exemplary embodiment of the present disclosure.

[0018] Figure 8 This is an example diagram of visually displaying aggregated data of preset events of a preset object in a B scene domain in a data processing method according to an exemplary embodiment of the present disclosure.

[0019] Fig. 9 This is an example diagram showing data of different scenario domains displayed in different visual analysis charts in a data processing method according to an exemplary embodiment of the present disclosure.

[0020] Fig.10 This is an example diagram showing data of scene domain A and scene domain B displayed on different types of visual analysis charts in a data processing method according to an exemplary embodiment of the present disclosure.

[0021] Fig.11 It is a flowchart of a data processing method according to an exemplary embodiment of the present disclosure.

[0022] Fig.12 The diagram is an example of a process of configuring tasks for a task engine in a data processing method according to an exemplary embodiment of the present disclosure.

[0023] Fig.13 It is a diagram showing an example framework for performing data processing in a data processing method according to an exemplary embodiment of the present disclosure.

[0024] Fig.14 It is a structural block diagram of a data processing device according to an exemplary embodiment of the present disclosure.

[0025] Fig.15 It is a structural block diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0029] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0030] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0031] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0032] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0033] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0034] 1. Bury point Tracking is a code snippet inserted into an application or website to capture data about specific events, such as user clicks, page views, etc. This data can be called tracking data, which can be used to analyze user behavior, improve product features, and optimize user experience.

[0035] 2. Application Subject Application entities refer to entities that users operate or interact with on applications, websites, or other digital platforms. These entities can be various types of content, functions, or elements. Application entities include, but are not limited to, images, videos, text, buttons, links, pages, and forms.

[0036] For example, images can be image elements in applications, websites, or other digital platforms, such as product images, advertising images, user avatars, etc.; videos can be video content in applications, websites, or other digital platforms, such as video advertisements, user-uploaded videos, live broadcasts, etc.; copy can be text content in applications, websites, or other digital platforms, such as product descriptions, user reviews, news information, etc.

[0037] 3. Multiple scene domains Multi-scenario domains can be a range covering multiple different business scenarios or fields. In digital applications and Internet services, different scenario domains may involve different user behaviors, business processes and data indicators, that is, each scenario domain has its specific business goals, user groups and operating models.

[0038] For example, common scenario domains may include e-commerce scenario domains, social media scenario domains, online education scenario domains, financial service scenario domains, medical and health scenario domains, smart home scenario domains, and entertainment and media scenario domains. Moreover, these scenario domains may also be subdivided into multiple sub-scenario domains, such as an e-commerce scenario domain may involve scenario domains of different merchants.

[0039] 4. Multiple scene domain tracking Multi-scene domain tracking can be the same preset object applied in multiple scene domains, and tracking is reported according to the tracking system of different scene domains. For example, picture A is reported in the tracking system of the first scene domain, and picture A is reported in the tracking system of the second scene domain.

[0040] 5. Data reflux Data reflow refers to the process of processing and analyzing the collected data and feeding the analysis results back to the original data source. In the process of data analysis and business optimization, data reflow is an important link, which can apply the results of data analysis to the business system, thereby realizing data-driven business optimization and decision support.

[0041] In the embodiment of the present disclosure, data backflow may be a process of returning data from other scenario domains to the own domain via a data link.

[0042] 6. Data cleaning Data cleaning refers to the preprocessing of data before data analysis to remove erroneous, incomplete, inconsistent or inaccurate parts of the data to ensure the quality and usability of the data.

[0043] 7. Data regularization Data regularization refers to the process of organizing, cleaning and processing the collected raw data according to certain rules and standards for subsequent data analysis and application. In the process of data processing and analysis, the raw data often has various irregularities, incompleteness or inconsistencies, and needs to be processed by data regularization to make it conform to the unified data format and standards and improve the quality and availability of the data. Data regularization can be used to aggregate and clean irregular and unclear data in a way that makes it easy to understand.

[0044] 8. Visual analysis Visual analysis refers to the use of visualization methods such as charts, graphs, and maps to convert data into intuitive and easy-to-understand graphical displays, so that users can more intuitively understand the data and discover the relationships and patterns between data, thereby supporting decision-making and business optimization.

[0045] The main goal of visual analysis is to transform abstract data into intuitive graphical displays, presenting the structure, patterns, trends, and anomalies of the data in a visual way, helping users to understand the data more deeply, discover patterns and insights in the data, and make more accurate and effective decisions.

[0046] 9. Lightweight multi-dimensional data layer A lightweight multidimensional data layer is a data architecture concept that can be used to store and manage data organized in a multidimensional manner. This data layer is often used to support fast data analysis and complex query operations, especially in the fields of business intelligence, data warehousing, and data mining.

[0047] In the disclosed embodiment, visual analysis can be used to present the data scattered in the database on the system interface through various analysis views for visual semantic expression. Here, the analysis view may include reports, trend charts, and funnels.

[0048] In the related technology, when the same subject is applied in different scene domains of the burying point system, different burying point data usually need to form a separate data return link according to the burying point system of the scene domain, and be presented based on its corresponding visualization chart.

[0049] As an example, Figure 1As shown, during the application of the same application subject in scene domain A, the tracking data related to the application subject can be cleaned from the original tracking data under the tracking system of scene domain A, and a lightweight multi-dimensional data layer of the application effect of the application subject in scene domain A is generated, and then the data analysis of the application effect of the application subject in scene domain A is presented in the form of a visual chart.

[0050] In addition, if Figure 1 As shown, during the application of the same application subject in the B scene domain, the tracking data related to the application subject can be cleaned from the original tracking data under the B scene domain tracking system, and a lightweight multi-dimensional data layer of the application effect of the application subject in the B scene domain is generated, and then the data analysis of the application effect of the application subject in the B scene domain is presented in the form of a visual chart.

[0051] Different dimensional semantics and dimensional levels in different tracking point systems make it impossible for related technologies to gather data in one place and conduct unified visual analysis of the gathered data. Figure 1 It is known that the data backflow link capability in related technologies lacks portability. Each time an application subject applies a new scenario domain, it needs to complete the entire process from data understanding, data cleaning, data regularization to visual presentation, which increases the cost of data processing. Here, the application subject can be called a preset object.

[0052] In view of this, the embodiments of the present disclosure provide a data processing method, device, medium, electronic device and program product, and propose to process the M pieces of buried point data after acquiring M pieces of buried point data corresponding to M scene domains to obtain aggregated data of preset events, and display the aggregated data of the preset events in the M scene domains based on the aggregated data, which can reduce the cost of buried point data processing to a certain extent.

[0053] The following introduces the application scenarios of the external personality determination method provided by the embodiments of the present disclosure.

[0054] The data processing method provided by the embodiment of the present disclosure can uniformly visualize the buried data in M ​​scene domains. Specifically, the data processing method can be applied to scenes that require buried data processing, and the method can be used in products in these scenes. For example, advertising delivery systems, marketing venue floor systems, hardware usage systems, and different application business focus systems.

[0055] The advertising delivery system is a platform for managing and optimizing advertising delivery, including functions such as ad creation, delivery, monitoring and optimization. It has functions such as ad exposure statistics, click statistics, conversion rate statistics and user behavior analysis. Specifically, the advertising delivery system collects user behavior data on the advertising page through data embedding points, which is used to evaluate advertising effects, optimize advertising delivery strategies and improve advertising delivery effects.

[0056] For example, the advertising delivery system involves M scene domains. The same preset object can collect different embedding data in different scene domains. Here, the embedding data can be the click statistics and conversion rate statistics of users for a certain advertisement. Different scene domains can be different e-commerce applications. On this basis, the embedding data collected in different scene domains are uploaded to the server. After that, the server can perform unified analysis and processing on the embedding data in different scene domains, that is, perform data conversion and data aggregation, etc. Finally, the processing results of the embedding data corresponding to different scene domains can be uniformly visualized, which can greatly reduce the cost of advertising delivery.

[0057] The marketing venue floor system is a platform for managing and optimizing marketing venue activities, including the layout of the venue, the setting of booths, the arrangement of activities, etc. It has functions such as venue traffic statistics, booth traffic statistics, activity participation statistics, and user behavior path analysis. Specifically, the marketing venue floor system can collect user behavior data in the venue through data embedding points, which is used to evaluate the effect of activities, optimize the venue layout and activity design.

[0058] The hardware usage system is a platform for managing and monitoring the usage of hardware devices, including device activation, online time, fault statistics, etc. It has functions such as device activation statistics, device online time statistics, fault statistics and analysis, and user behavior analysis. Specifically, the hardware usage system can collect user operation behavior on the device and device usage data through data embedding points to optimize device design and maintenance management.

[0059] Different application business focus systems can be data collection systems designed for different application scenarios and business needs. The system can include e-commerce applications, social media applications, online education applications, health management applications, etc. Different application business focus systems have functions such as user registration and login statistics, user behavior analysis, business conversion statistics, user retention and repurchase statistics, etc. Specifically, different application business focus systems can collect user behavior data in the application through data collection, which is used to understand user behavior, optimize product design and improve user experience.

[0060] In order to better understand a data processing method, device, readable medium, electronic device, and program product provided by the embodiments of the present disclosure, the application environment applicable to the embodiments of the present disclosure is first described below.

[0061] See also Figure 2 , Figure 2 FIG. 1 is a schematic diagram showing an application environment of a data processing method provided by an embodiment of the present disclosure. As an implementation mode, the data processing method provided by an embodiment of the present disclosure can be applied to an electronic device. The electronic device can be, for example, Figure 2 The server 210 shown in the figure can be connected to the terminal device 220 through a network. The network is used to provide a medium for a communication link between the server 210 and the terminal device 220. The network can include various connection types, such as a wired communication link, a wireless communication link, etc., which is not limited in the embodiment of the present disclosure.

[0062] It should be understood that Figure 2 The server 210, network and terminal device 220 are merely illustrative. Depending on the implementation requirements, there may be any number of servers 210, networks and terminal devices 220. For example, the server 210 may be a physical server 210, or a server 210 cluster composed of multiple servers 210, etc. The terminal device 220 may be a mobile phone, a tablet, a desktop computer, a laptop computer, a smart wearable device, etc.

[0063] In some embodiments, the terminal device can run applications with various functions, such as shopping applications, educational applications, financial applications, social applications, and entertainment applications, etc., collect and upload the buried data through the applications running on the terminal device, and send the buried data to the server through the terminal device. The server can run the data processing method of the embodiment of the present disclosure, and process the buried data uploaded by the terminal device through the data processing method. In addition, the server can visualize the processing results of the buried data, and can also send the processing results to the terminal device, and visualize the processing results through the terminal device. Exemplarily, the server can be a service engine.

[0064] The embodiments of the present disclosure are further explained below with reference to the accompanying drawings.

[0065] Figure 3 is a flowchart of a data processing method according to an exemplary embodiment of the present disclosure, referring to Figure 3 , the data processing method may include the following steps: S310: Obtain M pieces of buried point data generated by a preset object in M ​​scene domains.

[0066] In the embodiment of the present disclosure, the buried data may be generated when a preset event for a preset object is triggered, and the buried data may include multiple data dimensions. The buried data generated in different scene domains have different numbers of dimensions, dimension semantics, and dimension data formats.

[0067] Here, the preset object may be an application subject, and the M pieces of tracking data may be generated by the same application subject in M ​​scene domains, where the M scene domains may include scene domains with different tracking systems. In other words, if the scene domains where the preset object is located are different, the corresponding tracking data obtained will also be different. The M scene domains can be called multiple scene domains.

[0068] In the embodiment of the present disclosure, the preset object may be a transaction entity, which includes but is not limited to pictures, videos, text, audio, interactive interfaces, product services, buttons, links, pages, and forms.

[0069] As an example, when the preset object is a picture, the picture can generate different tracking data in different applications, such as Figure 4 The application body shown is a picture 401, which includes a skirt, which can be a commodity. The commodity can be sold under application A or on other platforms such as Figure 5 The B application is shown as sold under the B application. By comparison Figure 4 and Figure 5 It is known that the same preset object "skirt" can be sold in different applications. In the process of collecting buried data, the buried data about the picture 401 collected for the A application can be the first buried data; the buried data about the picture 401 collected for the B application can be the second buried data.

[0070] In some implementations, the tracking data may include user identification information, behavior information, environment information, page information, and other information.

[0071] User identification information may include user ID (Identifier), device ID, session ID, etc. Among them, user ID can be used to uniquely identify the ID of the user; device ID can be used to uniquely identify the ID of the device used by the user; session ID can be the session identifier of the user in the application.

[0072] Behavior information may include behavior type, behavior target, behavior time, etc. The behavior type may be a specific behavior type of the user, such as browsing, clicking, purchasing, and sharing; the behavior target may be a specific object or target of the user's behavior, such as a product, advertisement, or article; and the behavior time may be the timestamp of the user's behavior.

[0073] Environmental information may include application version, device type, operating system, application channel, etc. The application version may be the version number of the application used by the user; the device type may be the type of electronic device used by the user, such as a mobile phone, tablet, or computer; the operating system may be the operating system type and version number of the user's device; and the application channel may be the download channel of the application used by the user.

[0074] Page information may include page name, page path, page parameters, etc. The page name may be the name or page identifier of the page visited by the user; the page path may be the specific path of the page visited by the user; and the page parameters may be specific parameter information in the page, such as the page parameters may include the product ID and article ID, etc.

[0075] The interaction information may include the operation type, operation target, and operation result, etc. The operation type may be the specific operation type of the user, such as click, slide, and input, etc. The operation target may be the specific object or target of the user operation, such as button, link, form, etc. The operation result may be the result or status of the user operation, such as successful operation, failed operation, canceled operation, etc.

[0076] It should be noted that, in the embodiment of the present disclosure, the M scene domains may include scene domains with different point-of-sale systems, and different point-of-sale systems may have different numbers of point-of-sale data dimensions, different dimension semantics, or different dimensional data formats. The number of dimensions of the point-of-sale data may be the number of levels when the point-of-sale data is organized and analyzed according to different dimensions; the dimensional semantics of the point-of-sale data may be the meaning or significance represented by the point-of-sale data; and the dimensional data format of the point-of-sale data may be a standardized description of the organization and structure of the point-of-sale data.

[0077] In the embodiments of the present disclosure, the tracking point system can be a complete tracking point system, which can be used to collect, store and analyze user behavior data. The complete tracking point system can include tracking point solution design, tracking point data collection, tracking point data storage, tracking point data analysis, data visualization and report display, and tracking point data optimization and iteration.

[0078] In summary, the buried data in the embodiments of the present disclosure are generated when a preset event of a preset object is punished. The buried data may include multiple data dimensions. The buried data generated in different scene domains have different numbers of dimensions, dimension semantics, and dimension data formats.

[0079] S320: Convert the M pieces of buried point data into M pieces of buried point conversion data with the same number of dimensions, the same dimension semantics, and the same dimension data format.

[0080] As an optional method, after acquiring M pieces of buried point data, the embodiment of the present disclosure may perform data conversion on the M pieces of buried point data to obtain M pieces of buried point conversion data. Specifically, the M pieces of buried point data are converted into M pieces of buried point conversion data with the same number of dimensions, the same dimension semantics, and the same dimension data format. Here, the data dimensions of the M pieces of buried point conversion data may include an event dimension, and may include at least one of a time dimension, a user information dimension, and a geographic dimension.

[0081] As an example, during the data conversion process, the embodiment of the present disclosure may convert M pieces of buried point data into M pieces of buried point conversion data having the same number of dimensions, dimension semantics, and dimension data format.

[0082] As another example, the embodiment of the present disclosure may also convert M pieces of buried point data into M pieces of buried point conversion data having the same dimensional number, dimensional semantics, or dimensional data format.

[0083] That is to say, the embodiment of the present disclosure can convert M pieces of buried point data into M pieces of buried point conversion data having at least one of the same dimensional number, dimensional semantics, and dimensional data format.

[0084] As an optional method, before converting the M pieces of buried point data, the embodiment of the present disclosure may perform a data cleaning operation. Among them, data cleaning can be used to clean and process the buried point source data to ensure the accuracy, completeness and consistency of the data. The data cleaning process may include removing duplicate data, processing missing values ​​and abnormal values.

[0085] Deduplication can be used to remove duplicate data records to ensure the uniqueness of the data; processing missing values ​​and outliers can be used to discover and process missing values ​​and outliers in the data to make the data more complete and accurate.

[0086] In the disclosed embodiments, data conversion may include dimensional conversion, wherein dimensional conversion may be to reorganize and convert data according to different dimensions for subsequent data analysis and mining, that is, to unify the dimensions. From the above introduction, it is known that the number of dimensions, dimension semantics, and dimension data formats corresponding to different point-of-sale systems may also be different, in order to facilitate the subsequent data analysis. The disclosed embodiments can perform dimensional conversion on M points of point-of-sale data collected in different scene domains. Here, dimensional conversion may include at least one of conversion of the number of dimensions, conversion of the meaning of dimensions, and conversion of the data format under the dimensions.

[0087] Exemplarily, there are two buried point data corresponding to two scene domains, and the number of dimensions of these two buried point data is different, the number of dimensions of the first buried point data is 2, and the number of dimensions of the second buried point data is 1. In the process of dimensional conversion, the embodiment of the present disclosure can convert the number of dimensions of the first buried point data to the same as the number of dimensions of the second buried point data, that is, convert the number of dimensions of the first buried point data to 1. At this time, the data with the same dimension as the first buried point data can be retained, and the data with different dimensions can be discarded. For example, the dimensions of the first buried point data are time and user information, and the dimension of the second buried point data is time. At this time, the data related to the user information dimension can be deleted.

[0088] Optionally, the embodiment of the present disclosure may also keep the number of dimensions of the first buried point data unchanged, and convert the number of dimensions of the second buried point data to 2. In this process, the information of the newly added dimension may be the default, or may be re-collected from the corresponding scene domain. Continuing with the above example, the dimensions of the first buried point data are time and user information, and the dimensions of the second buried point data are time, and user information is missing. At this time, the buried point data can be re-adopted from the corresponding scene domain corresponding to the corresponding indication, and the user information corresponding to the buried point data can be used as the user information of the second buried point data. The embodiment of the present disclosure does not explicitly limit how to perform the dimension conversion, and it can be selected according to the actual situation.

[0089] S330: For each buried point conversion data, aggregate the data under the event dimension according to at least one of the time dimension, the user information dimension and the geographic dimension to obtain aggregated data of the preset event, and display the aggregated data.

[0090] In the embodiment of the present disclosure, the data dimensions of the M buried point conversion data may include an event dimension, and at least one of a time dimension, a user information dimension, and a geographic dimension. After converting the M buried point data to obtain the M buried point conversion data, the embodiment of the present disclosure may perform a data aggregation operation. Here, data aggregation may be referred to as data regularization. Data regularization may be used to integrate and reconstruct the M buried point conversion data for subsequent data analysis and mining.

[0091] After data conversion and data aggregation, the embodiment of the present disclosure can obtain a data set with higher quality and clearer structure, which can be used for subsequent data analysis and mining, such as user behavior analysis, user portrait analysis, and business conversion analysis.

[0092] In a specific implementation, the disclosed embodiment may aggregate the buried point conversion data under the event dimension according to at least one of the time dimension, the user information dimension, and the geographic dimension to obtain aggregated data of the preset event.

[0093] As an example, the embodiments of the present disclosure may aggregate the buried point conversion data under the event dimension according to the time dimension, the user information dimension or the geographic dimension to obtain the aggregated data of the preset event.

[0094] As another example, the disclosed embodiment can aggregate the buried point conversion data under the event dimension according to the time dimension, the user information dimension and the geographic dimension to obtain the aggregated data of the preset event. How to aggregate the data is not clearly defined here and can be selected according to the actual situation.

[0095] As an optional method, the embodiment of the present disclosure may display the aggregated number. Specifically, the embodiment of the present disclosure may display the aggregated data in a visual analysis chart.

[0096] Among them, visual analysis charts can be used to intuitively display aggregated data in the form of charts to help users better understand the data and discover patterns and trends. Common visual analysis charts may include line charts, pie charts, bar charts, funnel charts, scatter plots, and tree diagrams.

[0097] In some implementations, aggregated data of preset events corresponding to the same preset object in M ​​scene domains may be displayed in the same visual analysis chart, such as Figure 6 The circle shown may refer to the aggregated data of the preset object in scene domain A, and the five-pointed star may refer to the aggregated data of the preset object in scene domain B. By using different charts to refer to different scene domains, it is not only convenient for users to distinguish different scene domains, but also convenient to compare data in different scene domains.

[0098] In addition, if Figure 6 As shown, in the process of visually displaying the aggregated data of the preset event under M scene domains, the embodiment of the present disclosure may display a scene selection control 610, which may include multiple sub-controls, and different scene domains may correspond to different sub-controls. For example, the control corresponding to the A scene domain may be the first sub-control 611, and the control corresponding to the B scene domain may be the second sub-control 612.

[0099] In response to a selection instruction input by a user for the scene selection control 610, the embodiment of the present disclosure may display aggregated data of preset events in the corresponding scene domain. Figure 7 As shown, when the user triggers the first sub-control 611 corresponding to the A scene domain, the embodiment of the present disclosure can visually display the aggregated data of the preset events of the preset object in the A scene domain. Figure 8 As shown, when the user triggers the second sub-control 612 corresponding to the B scene domain, the embodiment of the present disclosure can visually display the aggregated data of the preset events of the preset object in the B scene domain.

[0100] In other embodiments, in the process of displaying the aggregated data of the preset event, the embodiment of the present disclosure can display the information corresponding to the M scene domains in different visual analysis charts. That is, the aggregated data of the preset event of the preset object under the M scene domains can be displayed at the same time, and can be displayed in different visual analysis charts. Here, the types of different visual analysis charts can be the same, such as Fig. 9 As shown, the aggregate data of preset events of the same preset object in scene domain A can be displayed in the form of a line graph, and the aggregate data of preset events of the same preset object in scene domain B can also be displayed in the form of a line graph.

[0101] Optionally, in the process of visually displaying the buried point conversion data, the types of different visual analysis charts may also be different. Fig.10 As shown, the aggregate data of preset events of the same preset object in scene domain A can be displayed in the form of a pie chart, and the aggregate data of preset events of the same preset object in scene domain B can also be displayed in the form of a line chart.

[0102] How to uniformly display the aggregated data of the preset events of the preset object in M ​​scene domains is not explicitly limited here, and can be selected according to the actual situation of the user. For example, the user can display the aggregated data of a specific preset event by clicking the control corresponding to the scene domain, or can also display the aggregated data of the preset event in the corresponding scene domain by clicking the data in the specific visual analysis chart.

[0103] Through the above technical solution, after obtaining M pieces of buried point data, data conversion is performed on the M pieces of buried point data to obtain M pieces of buried point conversion data. On this basis, data aggregation is performed on the M pieces of buried point conversion data to obtain aggregated data of preset events. On this basis, the aggregated data is displayed. Since the M pieces of buried point data are generated by the same preset object applied in M ​​scene domains, there is no need to perform separate analysis when analyzing and visually displaying the data in the M scene domains. That is, the present disclosure can perform unified analysis and processing on buried point data of different systems, which can reduce the cost of data analysis to a certain extent.

[0104] In some implementations, the M scene domains correspond to M point-of-sale data tables for storing point-of-sale data, and the M scene domains correspond to the same conversion data table, which includes an event dimension and at least one of a time dimension, a user information dimension, and a geographic dimension. Converting the M points of point-of-sale data into M points of point-of-sale conversion data having the same number of dimensions, the same dimension semantics, and the same dimension data format may include the following steps: For a first dimension in a conversion data table, determine the similarity between the semantics of the first dimension and the semantics of each dimension in M ​​tracking data tables, wherein the conversion data table includes an event dimension and at least one of a time dimension, a user information dimension, and a geographic dimension, and the first dimension is any dimension in the conversion data table; The dimension with the highest similarity to the first dimension in each of the buried point data tables is used as the dimension to be converted; The data under the dimension to be converted obtained from the M buried point data tables are converted into the same data format and written into the first dimension to obtain the M buried point conversion data.

[0105] In the embodiment of the present disclosure, M scene domains may correspond to M burial point data tables for storing burial point data, and the M scene domains may correspond to the same conversion data table, which may include an event dimension, and may include at least one of a time dimension, a user information dimension, and a geographic dimension.

[0106] Among them, the event dimension can be used to indicate the operation dimension triggered by the user for the preset object. For example, the event dimension can include browsing events, click events, etc. The time dimension can be used to indicate the time when the user triggers the preset event, such as the time point of the year and month when the preset event is triggered; the user information dimension is used to indicate the information of the user who triggers the preset event, such as the age group of the user who triggers the preset event; the geographic dimension is used to indicate the location information of the preset event, such as the city in which the user triggers the preset event.

[0107] In the process of performing data conversion, the embodiment of the present disclosure can determine the similarity between the semantics of the first dimension and the semantics of each dimension in the M buried point data tables for the first dimension in the conversion data table. The first dimension can be any dimension in the conversion data table. On this basis, the dimension with the highest similarity to the first dimension in the M buried point data tables is used as the dimension to be converted, and then the data under the dimension to be converted is obtained, and the obtained data is converted to the same data format, that is, the unified data format is written into the first dimension to obtain M buried point conversion data. By traversing each dimension in the conversion data table, the conversion operation of the buried point data can be realized.

[0108] As an example, the first dimension is the time dimension. The disclosed embodiment can search for the dimension with the highest similarity to the time dimension in M ​​buried point data tables, that is, search for the time information in the M buried point data. On this basis, convert these time information into a unified format and write them into the first dimension in the converted data table. For example, the first time information included in the first buried point data table is January 2, 2024, and the second time information included in the second buried point data table is 2024 / 02 / 05. At this time, the two time information can be converted into a unified data format. Specifically, the first time information is converted into 2024-01-02, and the second time information is converted into 2024-02-05.

[0109] It should be noted that after traversing each dimension in the conversion data table, if there is still data in the tracking data table that has not been traversed, the embodiment of the present disclosure can directly discard the data, that is, indirectly clean up the data that is not related to the conversion data table.

[0110] Here, the unified data format can be determined based on the data format of the first dimension in the converted data, that is, the data under the dimension to be converted can be converted into this format based on the data format of the first dimension. Optionally, the unified data format can also be determined based on M pieces of buried point data, that is, the disclosed embodiment can count the data formats of the M pieces of buried point data, and take the format with the largest number as a unified data format, and on this basis, convert the formats of other buried point data into the unified data format. For example, it is determined through statistics that the format of the time information in the M pieces of buried point data is the largest in the format of XXXX-XX-XX, and this format can be used as a unified data format.

[0111] By converting the data under the dimension to be converted obtained from M buried point data tables into the same data format and then writing the data into the first dimension, the efficiency of data conversion can be improved.

[0112] In some implementations, aggregating data under the event dimension according to at least one of the time dimension, the user information dimension, and the geographic dimension to obtain aggregated data of a preset event may include the following steps: Divide data of at least one of a time dimension, a user information dimension, and a geographic dimension into data segments to obtain a plurality of data segments; For each data segment, the number of occurrences of the preset event under the event dimension is accumulated to obtain the number of times the preset event corresponding to the data segment is triggered.

[0113] From the above introduction, we know that in addition to the event dimension, the buried point conversion data can also include at least one of the time dimension, the user information dimension and the geographical dimension. In the process of performing the data aggregation operation, the embodiment of the present disclosure can first perform a segmentation operation to obtain multiple data segments. On this basis, the data of each data segment is aggregated separately to obtain the number of times the corresponding preset event is triggered. The number of triggering times can be used as the aggregated data of the preset event.

[0114] As an example, the disclosed embodiment may divide the data in the time dimension, user information dimension, or geographic dimension into data segments to obtain multiple data segments, and these data segments may belong to the same dimension. For example, the data in the time dimension is divided into data segments to obtain multiple data segments, and the multiple data segments include first time period data, second time period data, and third time period data.

[0115] As another example, the disclosed embodiment may divide the data in the time dimension and the user information dimension into data segments respectively to obtain multiple data segments, and these data segments may belong to different dimensions. For example, the data in the time dimension is divided into data segments to obtain data of the first time period, data of the second time period, and data of the third time period; the user information dimension information is divided to obtain data of the first age group, data of the second age group, and data of the third age group. At this time, the multiple data segments may include data of the first time period, data of the second time period, data of the third time period, data of the first age group, data of the second age group, and data of the third age group.

[0116] After obtaining multiple data segments, the disclosed embodiment can accumulate the number of occurrences of the preset event under the event dimension for each data segment to obtain the number of times the preset event corresponding to the data segment is triggered. Continuing with the above example, by counting the number of views of users corresponding to the first age group data, the aggregated data of the age group can be obtained.

[0117] By dividing data into segments of different dimensions and performing aggregation operations based on the segmentation results, data analysis can be achieved more efficiently and simply.

[0118] In some implementations, the data under the time dimension includes the time data of triggering the preset event, and the data under the event dimension includes the event type data of the preset event. Dividing the data of at least one of the time dimension, the user information dimension, and the geographic dimension into data segments to obtain multiple data segments may include the following steps: For the time dimension, the moment data under the time dimension is divided into time periods according to a preset time unit to obtain multiple time periods; For each of the data segments, accumulating the number of occurrences of the preset event under the event dimension to obtain the number of times the preset event corresponding to the data segment is triggered may include the following steps: For each of the time periods, the number of times each type of event data is triggered under the event dimension within the time period is accumulated and aggregated to obtain the number of times each type of preset event is triggered within the time period.

[0119] For example, for data at multiple time points between 8 o'clock and 9 o'clock, for a click event, the event type data may be a click type identifier corresponding to each time point.

[0120] In a specific implementation, the disclosed embodiment can implement data segment division based on the time dimension, that is, divide the time period of the moment data under the time dimension according to the preset time unit to obtain multiple time periods of the same time unit. For example, January to March is divided into one time period, and March to June is divided into another time period. These divisions can be performed based on event type data.

[0121] On this basis, the disclosed embodiment can accumulate and aggregate the number of times each type of event data in the event dimension in each time period to obtain the number of times each type of preset event is triggered in the time period. For example, by aggregating the browsing events triggered by users for preset objects in the time period from January to March, the number of times the browsing events are triggered in the time period from January to March can be obtained. For another example, by aggregating the click events triggered by users for preset objects in the time period from January to March, the number of times the click events are triggered in the time period from January to March can be obtained.

[0122] By aggregating the trigger information of preset events within the same time period, the buried data can be processed more accurately and clearly from different dimensions.

[0123] In some implementations, dividing data of at least one of the time dimension, the user information dimension, and the geographic dimension into data segments to obtain a plurality of data segments may include the following steps: For the user information dimension, the data under the user information dimension is divided into age groups according to the preset age units to obtain data segments of multiple age groups; For each data segment, the event data corresponding to the event dimension is aggregated by accumulation to obtain the number of times the preset event corresponding to the data segment is triggered, which may include the following steps: For each age group, the number of times each type of event data corresponding to the age group is accumulated and aggregated under the event dimension to obtain the number of times each type of preset event is triggered within the age group.

[0124] In a specific implementation, the disclosed embodiment can implement data segmentation based on the user information dimension, that is, divide the data under the user information dimension into data segments according to preset time and age units to obtain multiple age segments with the same age unit. For example, age 25 to 30 is divided into one age segment, and age 35 to 40 is divided into another age segment.

[0125] On this basis, the embodiments of the present disclosure can accumulate and aggregate the number of times each type of event data in the event dimension in each age group is triggered to obtain the number of times each type of preset event in the age group is triggered.

[0126] In some implementations, dividing data of at least one of the time dimension, the user information dimension, and the geographic dimension into data segments to obtain a plurality of data segments may include the following steps: For the geographical dimension, the data under the geographical dimension is divided according to the location information to obtain data segments of multiple regions; For each data segment, the event data corresponding to the event dimension is aggregated by accumulation to obtain the number of times the preset event corresponding to the data segment is triggered, which may include the following steps: For each area segment, the number of times each type of event data corresponding to the same area segment under the event dimension is accumulated and aggregated to obtain the number of times each type of preset event in the area segment is triggered.

[0127] In a specific implementation, the disclosed embodiment can realize the division of data segments based on the geographic dimension, that is, the data under the geographic dimension is divided into data segments according to the area where the buried data is generated, and multiple data segments of the same area are obtained. On this basis, the disclosed embodiment can accumulate and aggregate the number of times each type of event type data under the event dimension in each regional segment is in each regional segment, and obtain the number of times each type of preset event in the regional segment is triggered.

[0128] In some implementations, the preset events include browsing events and click events. For each data segment, the corresponding event data under the event dimension is aggregated by accumulation to obtain the number of times the preset event corresponding to the data segment is triggered, which may include the following steps: For each data segment, the browsing events under the event dimension are accumulated to obtain the number of times the browsing events are triggered, and the click events under the event dimension are accumulated to obtain the number of times the click events are triggered; The click rate of the preset object corresponding to the data segment is determined according to the number of times the click event is triggered and the number of times the browse event is triggered.

[0129] In the embodiment of the present disclosure, the preset event may include a browsing event and a click event. In the process of obtaining the number of times the preset event corresponding to the data segment is triggered, the embodiment of the present disclosure may accumulate the browsing event and the click event respectively to obtain a first triggering number corresponding to the browsing and a second triggering number corresponding to the click. On this basis, the click rate of the preset object may be obtained by combining the first triggering number and the second triggering number.

[0130] Exemplarily, the click rate of the preset object may be the ratio of the second triggering number to the first triggering number, that is, the click rate of the preset object may be the ratio of the number of clicks to the number of views. For example, click rate = number of clicks / number of views.

[0131] It should be noted that, in the embodiments of the present disclosure, the preset events may also include purchase events, collection events, and forwarding events, etc. The specific preset events are not explicitly limited here and can be selected according to actual conditions.

[0132] In some implementations, the data dimension of the M pieces of buried point conversion data further includes a scene domain dimension, and aggregating the data under the event dimension according to at least one of the time dimension, the user information dimension, and the geographic dimension to obtain aggregated data of the preset event may include the following steps: For any scene domain dimension, the data under the event dimension is aggregated according to at least one of the time dimension, the user information dimension, and the geographic dimension of the corresponding scene domain dimension to obtain aggregated data of the preset event under the scene domain dimension; Depending on the display of aggregated data, the following steps may be included: Compare the aggregated data under M scene domain dimensions to obtain comparative information; The comparison information of the preset event in M ​​scene domains is displayed.

[0133] In the embodiments of the present disclosure, the data dimensions of the tracking data may include not only the number of dimensions, dimension semantics, and dimension data format, but also the scene domain dimension. The embodiments of the present disclosure may aggregate the data under the event dimension according to at least one of the corresponding time dimension, user information dimension, and geographic dimension for any scene domain dimension to obtain aggregated data of the preset event under the scene domain dimension.

[0134] For example, the embodiments of the present disclosure may aggregate browsing data in the time dimension, user information dimension, and geographic dimension under the scene domain A to obtain the number of views corresponding to the scene domain A. For another example, the embodiments of the present disclosure may aggregate browsing data in the time dimension, user information dimension, and geographic dimension under the scene domain B to obtain the number of views corresponding to the scene domain B.

[0135] On this basis, the disclosed embodiment can compare the aggregated data under M scene domain dimensions to obtain comparison information, and display the comparison information of the preset events under M scene domains. For example, there are three scene domain dimensions, which are scene domain A, scene domain B, and scene domain C. Through aggregation, the three views corresponding to these three scene domains can be obtained, and the three views can be compared to obtain comparison information, and finally the comparison information can be displayed. This can enable users to view the comparison results more clearly to a certain extent, thereby improving the efficiency of buried data analysis.

[0136] As an optional method, the embodiment of the present disclosure can be executed based on a task engine. Therefore, before performing data conversion and data aggregation operations, the embodiment of the present disclosure can first configure the task engine, wherein the task engine can be used to obtain M points of buried data generated by a preset object in M ​​scene domains based on the task configuration, and then display the aggregated data.

[0137] The task engine is a software component or service that is used to automatically execute predetermined background tasks, which can be periodic or triggered by specific events. In the disclosed embodiment, the task engine can be used to automate and simplify the entire point data return and analysis process.

[0138] In order to implement the data processing method, the embodiment of the present disclosure may configure the task engine before processing the M pieces of buried point data. After the task configuration is completed, the M pieces of buried point data generated by the preset object in the M scene domains are obtained based on the configured task engine, and the M pieces of buried point data are converted and aggregated to obtain aggregated data. On this basis, the aggregated data obtained by aggregation is displayed using the configured task engine.

[0139] It should be noted that, in the process of configuring the task engine, the embodiment of the present disclosure may first perform a task engine initialization operation. Specifically, the task engine service is initialized, a database is configured, and then the service (task engine) is started and the status of the service is detected.

[0140] Among them, initializing the task engine service may include configuring the environment, preparing data sources, defining task processes, resource allocation, service detection, security settings, error handling, logging, user interface, scheduled task settings, testing, documentation and training, etc. By initializing the task engine, a system that has no access to the task engine can have a task engine, that is, the task engine initialization can serve as a preparation for accessing the task engine.

[0141] In the embodiment of the present disclosure, the configuration database during the task engine initialization process may include configuring the data source database and configuring the service application database. Among them, the data source database can be used to store the burial point source data, that is, the storage of the burial point data source table can be realized by configuring the data source database. Here, the burial point data source table can be used to store the burial point data generated by the corresponding scene domain. In addition, in the process of configuring the data source database, the embodiment of the present disclosure can configure the database address of the data source.

[0142] Optionally, the service application database can be used to store data generated by the task engine, that is, the service application database can be used to store data generated by the data processing process. Exemplarily, the service application database can be used to store data generated by data cleaning, or can be used to store data generated by data regularization, etc. Here, data regularization may include data conversion and data aggregation. In addition, in the process of configuring the service application database, the embodiment of the present disclosure can configure the address of the target analysis database.

[0143] The main purpose of detecting the status of the service is to ensure the availability and stability of the task engine. Specifically, the embodiments of the present disclosure can check the availability of the service engine, monitor the performance indicators of the service engine, check the resources used by the service engine, and confirm whether other services or systems that the service engine depends on are running normally.

[0144] By configuring the task engine, the disclosed embodiment can simply and efficiently process M pieces of buried data, and can realize a unified visual display of aggregated data of preset events in M ​​scene domains.

[0145] In some embodiments, task configuration for the task engine may include: configuring a point-of-sale data table for each scenario domain; configuring dimension mapping rules for converting the point-of-sale data in the point-of-sale data table to a conversion data table, and data conversion can be achieved based on the dimension mapping rules; configuring a regularization task for converting the point-of-sale data in the point-of-sale data table to the conversion data table, and data aggregation can be achieved based on the regularization task.

[0146] In addition, the M pieces of buried point data are processed to obtain buried point conversion data, including the following steps: According to the dimension mapping rules and the regularization tasks, the buried point data in the buried point data table is converted into a conversion data table.

[0147] As an optional method, in the process of configuring tasks for the task engine, the embodiment of the present disclosure can configure a point-burying data table for each application scenario. Here, the point-burying data table may include a point-burying data source table and a subject point-burying data table. The point-burying data source table can be used to store the point-burying data generated by the corresponding scenario domain, and the subject point-burying data table can be used to store the point-burying data after cleaning the point-burying data. The embodiment of the present disclosure configures the point-burying data table for each application scenario, which can be a point-burying data table for the subject under each application scenario domain.

[0148] In addition, the embodiments of the present disclosure may configure a data cleaning timer task, which may be used to instruct the service engine on how to clean the buried data and the time of data cleaning, that is, when to execute the data cleaning task.

[0149] As another optional method, the embodiment of the present disclosure can configure the dimension mapping rules for mapping the buried point data in the buried point data table to the conversion data table. Based on the dimension mapping rules, the embodiment of the present disclosure can quickly and effectively implement dimension mapping when executing dimension mapping. That is to say, according to the dimension mapping rules, the service engine can map the dimensions of the buried point data in different scene domains in the buried point data table to the same dimension, which can facilitate subsequent visual display. Here, the dimension mapping rules can be called conversion rules.

[0150] In addition, the embodiment of the present disclosure can be configured with a regularization task for regularizing the burial point data in the burial point data table into a conversion data table. The regularization task can be called an aggregation task. Based on the regularization task, the embodiment of the present disclosure can regularize the burial point data in different scene domains in the burial point data table into the same conversion data table.

[0151] It should be noted that the configuration of the above-mentioned main tracking data table, the configuration of the data cleaning timing task, the configuration of the dimension mapping rules and the configuration of the regularization task can be executed in parallel or in sequence. The embodiment of the present disclosure does not limit the specific configuration order.

[0152] Preferably, the embodiment of the present disclosure can execute the configuration of the task engine in the following order: configure the main tracking point data source table; configure the data cleaning scheduled task; configure the dimension mapping rules from the tracking point data to the conversion data table; configure the regularization task.

[0153] In some embodiments, after being configured, the task engine can use the pre-configured configuration to perform data conversion and data aggregation on the M points of buried data to obtain aggregated data. Specifically, the disclosed embodiment can convert the buried data in the buried data table into a conversion data table according to the dimension mapping rules and the regularization tasks.

[0154] Through the above technical scheme, the disclosed embodiment can configure the main tracking data table corresponding to each scene domain, configure how to clean the data, configure how to dimensionally map the data, and configure the diagram and regularize the data. In this way, the tracking data of different systems can be regularized and aggregated, and unified analysis can be performed, which can greatly reduce the cost of understanding data analysis.

[0155] In the disclosed embodiment, the buried point data table may include a buried point data source table and a main buried point data table. Here, the buried point data source table can be used to store the buried point data generated by the corresponding scene domain, and the main buried point data table can be used to store the buried point data after cleaning the buried point data.

[0156] Through the above introduction, before performing data conversion on M pieces of buried point data, the embodiment of the present disclosure can first perform a data cleaning operation. Specifically, the buried point data in the buried point data source table is cleaned to obtain a main buried point data table. According to the dimension mapping rules, the embodiment of the present disclosure can convert the buried point data in the main buried point data table into the conversion data table, and regularize and aggregate the buried point data in the main buried point data table according to the regularization task to obtain aggregated data.

[0157] From the above introduction, we know that task configuration of the task engine may involve configuring the main point-of-sale data table, configuring the data cleaning timed task, configuring the dimension mapping rules, and configuring the data normalization task, etc. After configuring the task engine, the embodiment of the present disclosure can process the point-of-sale data based on the task engine.

[0158] Specifically, the embodiment of the present disclosure can clean the buried point data in the buried point data source table to obtain the main buried point data table, wherein the buried point data source table can be used to store the buried point data generated by the same preset object in different scenarios, and the main buried point data table is used to store the buried point data after cleaning. In order to better understand the buried point data source table and the main buried point data table, the embodiment of the present disclosure provides the following Table 1 and Table 2.

[0159] Table 1

[0160] Table 1 may be a buried point data source table, which may be data stored in a data source database, and may include M buried point data. In other words, the buried point data source table may be a table for storing the subject's buried point raw data in a specified scene domain.

[0161] Table 2 may be a subject buried point data table, which may be a table obtained by cleaning the buried point data source table shown in Table 1, and may be stored in the service application database. In other words, the subject buried point data table may be a storage table for the buried point data of the subject that meets the scope of the analysis requirements.

[0162] Table 2

[0163] As another optional method, after obtaining the main buried point data table, the embodiment of the present disclosure can map the buried point data in the main buried point data table to the conversion data table according to the pre-configured dimension mapping rules, and aggregate the buried point data in the main buried point data table according to the regularization task to obtain aggregated data.

[0164] Here, the conversion data table can also be called a target regularization data table, that is, the embodiment of the present disclosure can update the data in the subject embedding data table to the target regularization data table through dimension mapping and data regularization. Among them, the target regularization data table can be shown in Table 3: Table 3

[0165] Table 3 may be a conversion data table, which may be a table obtained after mapping (converting) and regularizing (aggregating) the main buried point data shown in Table 2. Table 3 is similar to Table 2 and may also be stored in the service application database. In other words, the conversion data table may be a table designed according to the analysis requirements, and the data in the main buried point data table may be cleaned as required to be stored in the regularized table.

[0166] The data in the above-mentioned tracking data source table, main tracking data table and conversion data table can be set according to different specific scenario domains. Different scenario domains will result in different fields and indicator details in the corresponding tables. The above-mentioned tables are only examples and the actual situation shall prevail.

[0167] As an example, when the scenario domain is a marketing scenario, the fields of the embedding data source table may include user ID, event occurrence time, event type (click / exposure), subject ID, subject type, and the location where the event occurred; the fields of the subject embedding data table may include user ID, event occurrence time, event type (click / exposure), subject ID, and the location where the event occurred; the fields of the conversion data table may include subject ID, the location where the event occurred, number of clicks, number of exposures, and click-through rate.

[0168] As another example, when the scenario domain is a purchase business scenario, the fields of the tracking data source table may include user ID, event occurrence time, event type (order created / payment successful), subject ID, subject type, and the location where the event occurred; the fields of the subject tracking data table may include user ID, event occurrence time, event type (order created / payment successful), subject ID, and the location where the event occurred; the fields of the conversion data table may include subject ID, the location where the event occurred, the number of orders placed, the number of successful payments, and the payment success rate.

[0169] Through the above-mentioned technical scheme, the embodiment of the present disclosure can utilize the cleaning timer tasks pre-configured in the service engine to clean the data in the tracking point data source table to obtain the main tracking point data table, and can convert and aggregate the data in the main tracking point data table according to the pre-configured dimension mapping rules and regularization tasks to obtain aggregated data. With the support of the service engine, the embodiment of the present disclosure can simply and efficiently realize the reflux and analysis of the tracking point data of M scene domains.

[0170] In some implementations, configuring the task engine for a task may further include determining whether the conversion data table exists; if it is determined that the conversion data table exists, configuring the subject embedding data table. Optionally, if it is determined that the conversion data table does not exist, configuring the conversion data table.

[0171] In the embodiment of the present disclosure, the subject burying point data table can be configured based on the conversion data table, that is, before configuring the subject burying point data table, the embodiment of the present disclosure can first determine whether there is a conversion data table in the service engine. In other words, it is determined whether the service engine is configured with a conversion data table. If it is determined that the conversion data table is pre-configured, the embodiment of the present disclosure can configure the subject burying point data table based on the conversion data table.

[0172] On the contrary, if it is determined that the service engine does not have a conversion data table, that is, the service engine is not configured with a conversion data table, then the embodiment of the present disclosure can first configure the conversion data table, that is, initialize the configuration target lightweight multi-dimensional regularized data table. On this basis, the configured conversion data table is used to configure the main point data table.

[0173] It should be noted that in the embodiment of the present disclosure, the conversion data table can be configured according to customer needs. If the customer needs are different, the corresponding conversion data tables are different. On the contrary, if the customer needs are the same, the corresponding conversion data tables are the same. In addition, when the customer needs are the same, the conversion data tables corresponding to the M scene domains can be the same.

[0174] In the process of determining whether a conversion data table exists in the service engine, the embodiment of the present disclosure may first obtain customer needs, and on this basis, determine whether a conversion data table corresponding to the customer needs exists; if it is determined that a conversion data table corresponding to the customer needs exists, then it is determined that the conversion data table exists.

[0175] Through the above technical scheme, the configuration of the main buried point data table can be realized accurately and effectively, and when a conversion data table exists, the main buried point data table is configured based on the conversion data table. When the conversion data table does not exist, a normalized table is initialized, and the main buried point data table is configured using the conversion data table obtained by initialization. In this way, the flexibility of the data table configuration can be guaranteed to a certain extent.

[0176] In some implementations, configuring a task for a task engine may further include configuring a visual analysis chart.

[0177] As an optional method, the embodiment of the present disclosure can configure the visual analysis chart, and the configuration of the visual sharing chart can be configured based on the conversion data table. In other words, after the conversion data table is configured, the embodiment of the present disclosure can configure the visual analysis chart based on the conversion data table.

[0178] In addition, displaying the aggregated data of the preset event in M ​​scene domains according to the aggregated data includes: displaying the aggregated data of the preset event of the preset object in M ​​scene domains according to the visualization analysis chart according to the buried point conversion data. If the visualization analysis charts are different, the display of the aggregated data of the preset event of the preset object in the corresponding scene domain is also different.

[0179] It should be noted that the configuration of the visual analysis chart in the embodiment of the present disclosure may be performed on the basis of the conversion data table, that is, after the conversion data table is configured, the embodiment of the present disclosure may configure the visual analysis chart.

[0180] Through the above-mentioned technical scheme, the embodiment of the present disclosure uses pre-configured visual analysis charts to display the aggregated data of preset events of preset objects in M ​​scene domains, which makes it convenient for users to simply and intuitively observe the usage of the same subject in different scene domains, and based on the pre-configured visual analysis charts, users can flexibly select the data corresponding to the scene domain they need.

[0181] In some implementations, the data processing method may further include: configuring the task engine through a plug-in.

[0182] In the embodiment of the present disclosure, the configuration of the task engine can be carried out through a plug-in, that is, the data reflow and analysis of the M scene domains can be achieved through a small amount of plug-in configuration, where the plug-in configuration may include table configuration, task configuration, and dimension mapping configuration.

[0183] The configuration of the task engine through plug-ins can quickly respond to the demands of the demand side, which can greatly save the time cost of point data processing. While improving performance, the embodiment of the present disclosure can migrate the capabilities of the task engine.

[0184] Next, a dialogue flow of the data processing method of the embodiment of the present disclosure is described in conjunction with a specific application scenario.

[0185] In some embodiments, in combination Fig.11 A point-of-sale data processing process provided in an embodiment of the present disclosure is described.

[0186] The disclosed embodiment can collect the buried point data on the terminals held by different users, and then store the M buried point data generated by the collected preset objects in M ​​scene domains in the data source database. At the same time, the disclosed embodiment can also perform task configuration on the task engine. On this basis, the disclosed embodiment can use the configured task engine to perform data conversion and data aggregation on the M buried point data in the data source database to obtain aggregated data.

[0187] Among them, M pieces of buried point data can be stored in a buried point data source table. The disclosed embodiment can clean the M pieces of buried point data in the buried point data source table to obtain a main buried point data table. On this basis, the main buried point data table can be dimensionally converted and data regularized to obtain a conversion data table. Finally, the disclosed embodiment can visualize the buried point conversion data in the conversion data table according to a pre-configured visual analysis chart.

[0188] In the embodiment of the present disclosure, the process of configuring the task engine can be as follows: Fig.12 As shown, based on Fig.12 It can be known that before starting the configuration, the embodiment of the present disclosure can initialize the task engine first. Specifically, the embodiment of the present disclosure can initialize the service of the task engine, configure the database of the data source, configure the database of the service application, and start the service (task engine) and detect the status of the service.

[0189] After the service is successfully initialized, the embodiment of the present disclosure can set the configuration task, that is, set the reflow task. Specifically, determine whether there is a target normalized data table (conversion data table). If there is a target normalized data table, configure the subject's buried point data table (subject buried point data table) under the application scenario domain, and configure the data cleaning timer task. On this basis, configure the dimension mapping conversion configuration (dimensional mapping rules) from the buried point data to the target normalized table, and configure the normalization task (normalization timer task). Then, trigger the task link to check the availability of the data normalization link.

[0190] Optionally, if it is determined that the target normalized data table does not exist, the embodiment of the present disclosure can initialize the configuration of the target lightweight multi-dimensional normalized data table and configure the relevant visual analysis chart, and after configuring the conversion data table and the visual analysis chart, configure the main body of the application scenario domain. In addition, after configuring the visual analysis chart, the embodiment of the present disclosure can perform visual inspection by visual analysis display to check the visual analysis chart.

[0191] Compared with the related technologies, the data processing process of the disclosed embodiment is simple, and the entire analysis access task can be completed based on a small number of roles, which can greatly release productivity and quickly respond to business needs.

[0192] In order to more clearly understand the difference between this solution and the related art, the present disclosure embodiment provides the following Fig.13 The example diagram shown is based on Fig.13 It can be known that the embodiment of the present disclosure can first perform data mapping configuration and task configuration before executing the buried data processing. On this basis, the driving task engine can perform unified visualization of the data in different scene domains.

[0193] Based on the same concept, the embodiment of the present disclosure also provides a data processing device, which can become part or all of the electronic device through software, hardware, or a combination of software and hardware. Fig.14 As shown, the data processing device 1400 may include an acquisition module 1410 , a conversion module 1420 and an aggregation module 1430 .

[0194] The acquisition module 1410 is configured to acquire M pieces of buried point data generated by a preset object in M ​​scene domains, wherein the buried point data is generated when a preset event for the preset object is triggered, and the buried point data includes multiple data dimensions, and the buried point data generated in different scene domains have different numbers of dimensions, dimension semantics, and dimension data formats; The conversion module 1420 is configured to convert the M pieces of tracking data into M pieces of tracking conversion data with the same number of dimensions, the same dimension semantics, and the same dimension data format, wherein the data dimensions of the M pieces of tracking conversion data include an event dimension, and at least one of a time dimension, a user information dimension, and a geographic dimension; The aggregation module 1430 is configured to aggregate the data under the event dimension according to at least one of the time dimension, the user information dimension and the geographic dimension for each of the buried point conversion data, obtain the aggregated data of the preset event, and display the aggregated data.

[0195] In a possible implementation, the M scene domains correspond to M tracking data tables for storing the tracking data, and to the same conversion data table; the conversion module 1420 is configured to determine, for a first dimension in the conversion data table, the similarity between the semantics of the first dimension and the semantics of each dimension in the M tracking data tables, the conversion data table including an event dimension, and at least one of a time dimension, a user information dimension, and a geographic dimension, the first dimension being any dimension in the conversion data table; the dimension in each of the tracking data tables that has the highest similarity to the first dimension is taken as the dimension to be converted; the data under the dimension to be converted obtained from the M tracking data tables is converted into the same data format and written into the first dimension to obtain the M tracking conversion data.

[0196] In a possible implementation, the aggregation module 1430 includes: a division submodule, configured to divide the data of at least one of the time dimension, the user information dimension and the geographic dimension into data segments to obtain a plurality of data segments; The aggregation submodule is configured to perform cumulative aggregation on the number of occurrences of the preset event under the event dimension for each of the data segments, so as to obtain the number of times the preset event corresponding to the data segment is triggered.

[0197] In a possible implementation manner, the data under the time dimension includes the time data of triggering the preset event, and the data under the event dimension includes the event type data of the preset event; The division submodule is further configured to divide the moment data under the time dimension into time periods according to a preset time unit for the time dimension, so as to obtain a plurality of time periods of the same time unit; The aggregation submodule is also configured to, for each of the time periods, accumulate and aggregate the number of times each type of event type data under the event dimension corresponds to the time period, so as to obtain the number of times each type of preset event is triggered within the time period.

[0198] In a possible implementation manner, the preset event includes a browsing event and a click event; The aggregation submodule is also configured to, for each of the data segments, accumulate the browsing events under the event dimension to obtain the number of times the browsing events are triggered, and to accumulate the click events under the event dimension to obtain the number of times the click events are triggered; and determine the click rate of the preset object corresponding to the data segment based on the number of times the click events are triggered and the number of times the browsing events are triggered.

[0199] In a possible implementation manner, the data dimension of the M pieces of buried point conversion data further includes a scene domain dimension, and the aggregation module 1430 is further configured to aggregate the data under the event dimension according to at least one of the time dimension, the user information dimension, and the geographic dimension corresponding to the scene domain dimension for any of the scene domain dimensions, to obtain aggregated data of the preset event under the scene domain dimension; The aggregation module 1430 is further configured to compare the aggregated data under the M scene domain dimensions to obtain comparison information; and display the comparison information of the preset event under the M scene domains.

[0200] Based on the same concept, an embodiment of the present disclosure further provides an electronic device, including: a storage device having a computer program stored thereon; A processing device is used to execute the computer program in the storage device to perform the steps of any of the above-mentioned data processing methods.

[0201] Fig.15 An electronic device (eg, Figure 1 A schematic diagram of the structure of a terminal device or server in the system 1500.

[0202] The electronic device 1500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1502 or a program loaded from a storage device 1508 into a random access memory (RAM) 1503. In the RAM 1503, various programs and data required for the operation of the electronic device 1500 are also stored. The processing device 1501, the ROM 1502, and the RAM 1503 are connected to each other via a bus 1504. An input / output (I / O) interface 1505 is also connected to the bus 1504.

[0203] Typically, the following devices may be connected to the I / O interface 1505: an input device 1506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1509. The communication device 1509 may allow the electronic device 1500 to communicate with other devices wirelessly or by wire to exchange data. Although Fig.15 The electronic device 1500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0204] Based on the same concept, an embodiment of the present disclosure further provides a computer-readable medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned data processing methods are implemented.

[0205] Based on the same concept, an embodiment of the present disclosure further provides a computer program product, including a computer program, which implements the steps of any of the above data processing methods when executed by a processor.

[0206] It should be understood that the above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) to form a technical solution.

[0207] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0208] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims. Regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Claims

1. A data processing method, characterized in that: include: Obtain M pieces of buried data generated by a preset object in M ​​scene domains, wherein the buried data is generated when a preset event for the preset object is triggered, and the buried data includes multiple data dimensions, and the buried data generated in different scene domains have different numbers of dimensions, dimension semantics, and dimension data formats; Convert the M pieces of tracking data into M pieces of tracking conversion data with the same number of dimensions, the same dimension semantics, and the same dimension data format, wherein the data dimensions of the M pieces of tracking conversion data include an event dimension, and at least one of a time dimension, a user information dimension, and a geographic dimension; For each piece of the buried point conversion data, the data under the event dimension is aggregated according to at least one of the time dimension, the user information dimension and the geographic dimension to obtain the aggregated data of the preset event, and the aggregated data is displayed.

2. The method according to claim 1, characterized in that The M scene domains correspond to M buried point data tables for storing the buried point data, and correspond to the same conversion data table; the converting the M buried point data into M buried point conversion data with the same number of dimensions, the same dimension semantics, and the same dimension data format includes: For a first dimension in the conversion data table, determine the similarity between the semantics of the first dimension and the semantics of each dimension in the M tracking data tables, wherein the conversion data table includes an event dimension and at least one of a time dimension, a user information dimension, and a geographic dimension, and the first dimension is any dimension in the conversion data table; The dimension with the highest similarity to the first dimension in each of the buried point data tables is used as the dimension to be converted; The data under the dimension to be converted obtained from the M buried point data tables are converted into the same data format and written into the first dimension to obtain the M buried point conversion data.

3. The method according to claim 1, characterized in that The aggregating the data under the event dimension according to at least one of the time dimension, the user information dimension, and the geographic dimension to obtain the aggregated data of the preset event includes: Dividing the data of at least one of the time dimension, the user information dimension and the geographic dimension into data segments to obtain a plurality of data segments; For each of the data segments, the number of occurrences of the preset event under the event dimension is accumulated to obtain the number of times the preset event corresponding to the data segment is triggered.

4. The method according to claim 3, characterized in that The data under the time dimension includes the time data of triggering the preset event, and the data under the event dimension includes the event type data of the preset event; The step of dividing the data of at least one of the time dimension, the user information dimension and the geographic dimension into data segments to obtain a plurality of data segments includes: For the time dimension, divide the moment data under the time dimension into time periods according to a preset time unit to obtain a plurality of time periods; For each of the data segments, accumulating the number of occurrences of the preset event under the event dimension to obtain the number of times the preset event corresponding to the data segment is triggered includes: For each of the time periods, the number of times each type of event data is triggered under the event dimension within the time period is accumulated and aggregated to obtain the number of times each type of preset event is triggered within the time period.

5. The method according to claim 3, characterized in that: The preset events include browsing events and click events; the accumulating the number of occurrences of the preset events under the event dimension to obtain the number of times the preset events corresponding to the data segment are triggered includes: For each of the data segments, the browsing events under the event dimension are accumulated to obtain the number of times the browsing events are triggered, and the click events under the event dimension are accumulated to obtain the number of times the click events are triggered; The click rate of the preset object corresponding to the data segment is determined according to the number of times the click event is triggered and the number of times the browse event is triggered.

6. The method according to claim 1, characterized in that The data dimension of the M pieces of buried point conversion data also includes a scene domain dimension; the data under the event dimension is aggregated according to at least one of the time dimension, the user information dimension, and the geographic dimension to obtain the aggregated data of the preset event, including: For any of the scene domain dimensions, the data under the event dimension is aggregated according to at least one of the time dimension, the user information dimension, and the geographic dimension corresponding to the scene domain dimension to obtain aggregated data of the preset event under the scene domain dimension; The displaying of the aggregation includes: Comparing the aggregated data under the M scene domain dimensions to obtain comparison information; The comparison information of the preset event in the M scene domains is displayed.

7. A data processing device, characterized in that: include: an acquisition module, configured to acquire M pieces of buried data generated by a preset object in M ​​scene domains, wherein the buried data is generated when a preset event for the preset object is triggered, and the buried data includes multiple data dimensions, and the buried data generated in different scene domains have different numbers of dimensions, dimension semantics, and dimension data formats; a conversion module configured to convert the M pieces of tracking data into M pieces of tracking conversion data having the same number of dimensions, the same dimension semantics, and the same dimension data format, wherein the data dimensions of the M pieces of tracking conversion data include an event dimension, and at least one of a time dimension, a user information dimension, and a geographic dimension; The aggregation module is configured to aggregate the data under the event dimension according to at least one of the time dimension, the user information dimension and the geographic dimension for each of the buried point conversion data, obtain the aggregated data of the preset event, and display the aggregated data.

8. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processing device, the steps of the data processing method according to any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that: include: a storage device having a computer program stored thereon; A processing device, used to execute the computer program in the storage device to implement the steps of the data processing method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the data processing method according to any one of claims 1 to 6 are implemented.

Images