A method, device, equipment and medium for displaying data tables based on business domains

Abstract

The present application discloses a method for displaying data tables based on business domains, including: obtaining M metadata from M data tables; determining the association relationships between the M data tables according to the M metadata; constructing a target network according to the association relationships between the M data tables, the target network including M data nodes, where the data nodes in the M data nodes have a corresponding relationship with the data tables in the M data tables, and each data node is used to store a data table; performing regional division processing on the target network to obtain at least one business domain; when an operation for a target viewing interface is obtained, displaying at least one data table corresponding to the target business domain through the interface of the terminal device. Embodiments of the present application also provide related devices, equipment, and storage media. The present application can realize the automatic integration of data tables based on business domains, facilitating developers to directly view the data tables of a certain business domain, thereby saving time costs and labor costs.

Description

Technical Field

[0001] This application relates to the field of Internet technologies, and in particular, to a method, apparatus, device, and medium for displaying data tables based on business domains. Background Art

[0002] Data aggregation refers to the process of aggregating and summarizing the business detail data stored in a database according to one or more specified dimensions. During the process of software application, a large amount of data will be generated. Usually, this data will be stored in the database in the form of data tables for developers to perform operations such as viewing or calling.

[0003] A large number of data tables are often stored in the database. If a developer needs to obtain data for a certain business (or a certain business domain), the developer needs to find the data tables related to this business from these data tables, and then aggregate these related data tables for subsequent processing and analysis.

[0004] However, since there are many types of business domains, and different types of business domains may involve different data tables, developers not only need to understand the data tables associated with a certain business (or a certain business domain), but also need to find these associated data tables from a large number of data tables, resulting in high time costs and labor costs. Summary of the Invention

[0005] Embodiments of this application provide a method, related apparatus, device, and storage medium for displaying data tables based on business domains, which can automate the integration of data tables based on business domains, facilitate developers to directly view the data tables corresponding to a certain business (or a certain business domain), thereby saving time costs and labor costs.

[0006] In view of this, on the one hand, this application provides a method for displaying data tables based on business domains, including:

[0007] Obtain M metadata from M data tables, where there is a corresponding relationship between the data tables in the M data tables and the metadata in the M metadata, and M is an integer greater than or equal to 2;

[0008] Determine the association relationship between the M data tables according to the M metadata;

[0009] Construct a target network according to the association relationship between the M data tables, where the target network includes M data nodes, and there is a corresponding relationship between the data nodes in the M data nodes and the data tables in the M data tables, and each data node is used to store a data table;

[0010] Perform regional division processing on the target network to obtain at least one business domain, where each business domain includes at least one data table;

[0011] When an operation on a target viewing interface is obtained, at least one data table corresponding to a target service domain is displayed through the interface of the terminal device, where the target viewing interface is the viewing interface corresponding to the target service domain among at least one service domain.

[0012] On the other hand, the present application provides a data table display device, including:

[0013] An acquisition module, configured to acquire M metadata from M data tables, where there is a corresponding relationship between the data tables in the M data tables and the metadata in the M metadata, and M is an integer greater than or equal to 2;

[0014] A determination module, configured to determine the association relationship between the M data tables according to the M metadata;

[0015] A construction module, configured to construct a target network according to the association relationship between the M data tables, where the target network includes M data nodes, there is a corresponding relationship between the data nodes in the M data nodes and the data tables in the M data tables, and each data node is used to store a data table;

[0016] A partitioning module, configured to perform regional partitioning processing on the target network to obtain at least one service domain, where each service domain includes at least one data table;

[0017] A display module, configured to display at least one data table corresponding to the target service domain through the interface of the terminal device when an operation on the target viewing interface is obtained, where the target viewing interface is the viewing interface corresponding to the target service domain among at least one service domain.

[0018] In a possible design, in another implementation manner of the other aspect of the embodiments of the present application, the M data tables at least include a first data table and a second data table;

[0019] The acquisition module is specifically configured to acquire first metadata from the first data table;

[0020] Acquire second metadata from the second data table;

[0021] The determination module is specifically configured to determine the target association relationship between the first data table and the second data table according to the first metadata and the second metadata, where the target association relationship includes at least one of the connection direction and the connection weight between the first data node and the second data node, the first data node is used to store the first data table, and the second data node is used to store the second data table.

[0022] In a possible design, in another implementation manner of the other aspect of the embodiments of the present application,

[0023] A determining module, specifically configured to determine the edge connection direction between a first data node and a second data node according to the technical metadata included in the first metadata and the technical metadata included in the second metadata;

[0024] Determine the edge weight between the first data node and the second data node according to the service metadata included in the first metadata and the service metadata included in the second metadata.

[0025] In a possible design, in another implementation manner of another aspect of the embodiments of the present application,

[0026] A determining module, specifically configured to obtain the data lineage corresponding to the first data table according to the technical metadata included in the first metadata;

[0027] Obtain the data lineage corresponding to the second data table according to the technical metadata included in the second metadata;

[0028] Determine an upstream data table from the first data table and the second data table according to the data lineage corresponding to the first data table and the data lineage corresponding to the second data table, where the data lineage belongs to the technical metadata;

[0029] If the upstream data table is the first data table, construct an edge between the first data node and the second data node;

[0030] If the upstream data table is the second data table, construct an edge between the second data node and the first data node.

[0031] In a possible design, in another implementation manner of another aspect of the embodiments of the present application,

[0032] A determining module, specifically configured to obtain the service name corresponding to the first data table according to the service metadata included in the first metadata;

[0033] Obtain the service name corresponding to the second data table according to the service metadata included in the second metadata;

[0034] Determine the association degree between the first data table and the second data table according to the service name corresponding to the first data table and the service name corresponding to the second data table;

[0035] Determine the edge weight between the first data node and the second data node according to the association degree between the first data table and the second data table.

[0036] In a possible design, in another implementation manner of another aspect of the embodiments of the present application,

[0037] A determination module, specifically configured to obtain a service description corresponding to a first data table according to service metadata included in first metadata;

[0038] Obtain a service description corresponding to a second data table according to service metadata included in second metadata;

[0039] Based on the service description corresponding to the first data table and the service description corresponding to the second data table, obtain the correlation degree between the first data table and the second data table through a semantic matching model;

[0040] Determine the edge weight between the first data node and the second data node according to the correlation degree between the first data table and the second data table.

[0041] In a possible design, in another implementation manner of another aspect of the embodiments of the present application,

[0042] A partitioning module, specifically configured to perform a partitioning process on data nodes in a target network to obtain N regions, where N is an integer greater than or equal to 1 and less than or equal to M;

[0043] Determine at least one business domain according to the N regions.

[0044] In a possible design, in another implementation manner of another aspect of the embodiments of the present application,

[0045] The partitioning module is specifically configured to obtain data nodes to be partitioned from the target network;

[0046] Obtain a first data node and a second data node according to the data nodes to be partitioned, where both the first data node and the second data node are data nodes adjacent to the data nodes to be partitioned;

[0047] Determine a first modularity according to the data nodes to be partitioned and the first data node;

[0048] Determine a second modularity according to the data nodes to be partitioned and the second data node;

[0049] If both the first modularity and the second modularity are greater than 0, and the first modularity is greater than the second modularity, determine that the data nodes to be partitioned and the first data node belong to the same region among the N regions;

[0050] If both the first modularity and the second modularity are greater than 0, and the first modularity is less than the second modularity, determine that the data nodes to be partitioned and the second data node belong to the same region among the N regions;

[0051] Until the algorithm termination condition is satisfied, obtain N regions.

[0052] In a possible design, in another implementation of another aspect of the embodiments of the present application,

[0053] A partitioning module, specifically configured to obtain a first region and a second region from a target network;

[0054] According to the first region and the second region, obtain a first gain value, where the first gain value is the difference between the sum of the number of internal edges in the first region and the number of internal edges in the second region, and the number of edges between the first region and the second region;

[0055] Obtain a first data node in the first region and a second data node in the second region;

[0056] Add the second data node to the first region to obtain an updated first region, and add the first data node to the second region to obtain an updated second region;

[0057] According to the updated first region and the updated second region, obtain a second gain value, where the second gain value is the difference between the sum of the number of internal edges in the updated first region and the number of internal edges in the updated second region, and the number of edges between the updated first region and the updated second region;

[0058] Determine a target gain value according to the first gain value and the second gain value;

[0059] If the target gain value is the maximum value among P gain values, determine that the first data node belongs to the updated second region and the second data node belongs to the updated first region, where the P gain values include the gain values of pairwise data nodes between the first region and the second region, and P is an integer greater than or equal to 1;

[0060] Until the algorithm termination condition is satisfied, obtain N regions.

[0061] In a possible design, in another implementation of another aspect of the embodiments of the present application,

[0062] A partitioning module, specifically configured to determine K edge betweennesses according to M data nodes and K edges in a target network, where the edge betweenness in the K edge betweennesses has a corresponding relationship with the edge in the K edges;

[0063] Select a target edge betweenness from the K edge betweennesses, where the target edge betweenness is the maximum value among the K edge betweennesses;

[0064] Delete the edge corresponding to the target edge betweenness;

[0065] Until the algorithm termination condition is satisfied, obtain N regions.

[0066] In a possible design, in another implementation of another aspect of the embodiments of the present application,

[0067] A partitioning module, specifically configured to obtain Q data nodes included in the area to be recognized from N areas, where Q is an integer greater than or equal to 1;

[0068] Determine the business domain corresponding to the area to be recognized according to the Q data tables corresponding to the Q data nodes, where there is a corresponding relationship between the data nodes among the Q data nodes and the data tables among the Q data tables.

[0069] In a possible design, in another implementation of another aspect of the embodiments of the present application,

[0070] A display module, specifically configured to display the business names and viewing interfaces corresponding to each business domain;

[0071] When an operation on the target viewing interface is detected, display at least one data table corresponding to the target business domain through the interface of the terminal device.

[0072] In a possible design, in another implementation of another aspect of the embodiments of the present application,

[0073] A display module, specifically configured to receive a viewing instruction sent by the terminal device when the terminal device detects an operation on the target viewing interface;

[0074] Send at least one data table corresponding to the target business domain to the terminal device according to the viewing instruction, so that the terminal device displays at least one data table corresponding to the target business domain.

[0075] Another aspect of the present application provides a computer device, including: a memory, a processor, and a bus system;

[0076] Wherein, the memory is used to store programs;

[0077] The processor is used to execute the programs in the memory, and the processor is used to execute the methods in the above aspects according to the instructions in the program code;

[0078] The bus system is used to connect the memory and the processor, so that the memory and the processor can communicate.

[0079] Another aspect of the present application provides a computer-readable storage medium, in which instructions are stored, and when it runs on a computer, it causes the computer to execute the methods in the above aspects.

[0080] Another aspect of the present application provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above aspects.

[0081] As can be seen from the above technical solutions, the embodiments of the present application have the following advantages:

[0082] In the embodiments of the present application, a method for displaying data tables based on business domains is provided. First, M metadata are obtained from M data tables, and there is a one-to-one correspondence between the data tables and the metadata. Then, the association relationships between the M data tables are determined according to the M metadata. Thus, a target network is constructed according to the association relationships between the M data tables. The target network includes M data nodes, and each data node also has a one-to-one correspondence with a data table. Each data node is used to store a data table. Finally, the target network is subjected to area division processing, and at least one business domain can be obtained. Each business domain includes at least one data table. When an operation for a target viewing interface is obtained, at least one data table corresponding to the target business domain is displayed through the interface of the terminal device. In the above manner, the relationships between the data tables can be determined by using the metadata corresponding to each data table. A target network is formed based on the relationships between the data tables. Then, a community division algorithm can be used to perform area division on the target network, so as to obtain at least one area. Each divided area can be regarded as a business domain, and each data node within the area is the data table included in the business domain. Thus, the automatic integration of data tables can be realized based on the business domain, which is convenient for developers to directly view the data tables of a certain business domain, thereby saving time costs and labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a schematic architecture diagram of a business processing system in the embodiments of the present application;

[0084] Figure 2 It is an environmental schematic diagram of a business processing system in the embodiments of the present application;

[0085] Figure 3 It is a schematic flowchart of a method for displaying data tables based on business domains in the embodiments of the present application;

[0086] Figure 4 It is a schematic diagram of converting a data table into a data node in the embodiments of the present application;

[0087] Figure 5 It is a schematic diagram of generating a business domain based on a target network in the embodiments of the present application;

[0088] Figure 6 It is a schematic diagram of the association relationship between data nodes in an embodiment of the present application;

[0089] Figure 7 It is another schematic diagram of the association relationship between data nodes in an embodiment of the present application;

[0090] Figure 8 It is a schematic diagram of determining the edge direction between data nodes based on data lineage in an embodiment of the present application;

[0091] Figure 9 It is a schematic diagram of dividing regions based on the fast unfolding algorithm in an embodiment of the present application;

[0092] Figure 10 It is a schematic diagram of dividing regions based on the Kernighan-Lin algorithm in an embodiment of the present application;

[0093] Figure 11 It is a schematic diagram of dividing regions based on the GN algorithm in an embodiment of the present application;

[0094] Figure 12 It is a schematic diagram of an interface showing the business domain in an embodiment of the present application;

[0095] Figure 13 It is a schematic diagram of an interface showing data tables under the target business domain in an embodiment of the present application;

[0096] Figure 14 It is a schematic diagram of an embodiment of a data table display device in an embodiment of the present application;

[0097] Figure 15 It is a schematic diagram of the structure of a server in an embodiment of the present application;

[0098] Figure 16 It is a schematic diagram of the structure of a terminal device in an embodiment of the present application. Detailed implementation manners

[0099] The embodiments of the present application provide a data table display method, related device, equipment and storage medium based on a business domain, which can realize the automatic integration of data tables based on the business domain, facilitating developers to directly extract the data tables corresponding to a certain business (or a certain business domain), thereby saving time costs and labor costs.

[0100] The terms "first", "second", "third", "fourth", etc. (if any) in the description, claims and the above drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "corresponding to" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0101] With the rapid development of industries such as social, e-commerce, finance, and the Internet of Things, a huge network of relationships has been formed. The relationships between the data that the big data industry needs to process increase exponentially with the amount of data. As an important carrier for enterprises to store important and sensitive information, the database carries more and more critical business systems and has gradually become an important asset of enterprises. How to carry out data asset inventory work in a timely and effective manner is crucial for the development and maintenance of applications. During the processes of application development, operation, and maintenance, relevant personnel usually need to search for corresponding data tables from the database for a specific business (or a specific business domain), and finally summarize and analyze these data tables. Based on this, the present application provides a method for displaying data tables based on business domains, which can systematically and automatically solve the problem of dividing data asset business domains. By collecting business metadata and technical metadata, and through a series of feature construction and transformation, the data assets are divided into business domains, so as to realize the effective operation and precipitation of data assets, which is beneficial for relevant personnel to clearly understand which data tables and valuable data assets exist in the current business domain, and thus better manage data assets.

[0102] It can be understood that the data assets involved in the present application include but are not limited to data assets of financial business, social business, search business, game business, video business, audio business, payment business, and subscription business. The relevant personnel involved in the present application can be internal personnel of the enterprise, such as operation personnel, data analysts, and product designers, or external personnel of the enterprise, such as partners, or personnel of the data development team, such as development engineers, programmers, business analysts, etc. There is no limitation here.

[0103] The method for displaying data tables provided by the present application can be applied to a business processing system. For the sake of easy understanding, please refer to Figure 1 , Figure 1This is a schematic diagram of the architecture of the business processing system in an embodiment of this application. As shown in the figure, the architecture of the business processing system can include five levels, which are, from bottom to top, the database, the data processing layer, the data management layer, the data service layer, and the application layer. The content of each level will be introduced separately below.

[0104] A database is a warehouse that organizes, stores, and manages data according to a data structure. It is a collection of a large amount of data that is stored in a computer device for a long time, organized, shareable, and uniformly managed. The data in the database can come from internal systems and external systems. Internal systems include, but are not limited to, enterprise internal management systems such as customer relationship management (CRM), enterprise resource planning (ERP), and software configuration management (SCM), point of sale (POS) systems in retail channels, enterprise-owned websites, applications (APPs), self-owned e-commerce platforms, offline retail outlets, and customer service center systems. External systems include, but are not limited to, third-party e-commerce platforms, search engines, email internet service provider (ISP) platforms, demand-side platform (DSP), third-party payment platforms, social media platforms, and third-party data providers.

[0105] The data processing layer includes data identification, data cleaning, and data fusion. Among them, data identification can identify different data types from the database. For example, it belongs to user identification or gender, etc. Data cleaning processes problems such as missing values, out-of-bounds values, inconsistent codes, and duplicate data from aspects such as the accuracy, integrity, consistency, uniqueness, timeliness, and effectiveness of the data. Data fusion can merge data of the same type.

[0106] The data management layer includes data asset planning, data asset processing, data asset quality, data operation and maintenance, data asset security, and metadata management. Among them, data asset planning specifically includes data architecture management, data standardization, dimension table standardization, indicator standardization, and data map planning, etc. Data asset processing includes data process design, data model design, data processing development, data application development, data testing, and going live, etc. Data operation and maintenance includes operation detection, alarm management, data evaluation, data optimization, storage optimization, and decommissioning management, etc. Data asset quality includes quality planning management, quality planning inspection, and quality problem management, etc. Data asset security includes security policy management, security vulnerability inspection, permission application and allocation, and security audit, etc. Metadata management includes metadata collection, metadata classification, metadata verification, data relationship analysis, field relationship analysis, and metadata services, etc.

[0107] The data service layer provides application programming interfaces (APIs). APIs are some predefined functions that can be called or configured by relevant personnel to implement processing such as permission control, service calls, and interface configuration.

[0108] The application layer mainly refers to the application of data assets, specifically including conducting operation diagnosis on business, implementing machine learning based on business data, analyzing and mining business data, and viewing data tables under a certain business (or a certain business domain).

[0109] To facilitate relevant personnel to quickly query data tables under a certain business domain, this application proposes a method for displaying data tables based on business domain, and this method is applied to Figure 2 the business processing system shown in Figure 2 As shown, the business processing system includes a database, a server, and a terminal device. The database in this application can be a relational database or a non-relational database. The server in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), as well as big data and artificial intelligence platforms. The terminal device in this application can be a smart phone, a tablet computer, a laptop computer, a handheld computer, a personal computer, a smart TV, a smart watch, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected through wired or wireless communication methods, and this application does not make any restrictions here. The number of servers and terminal devices is also not restricted.

[0110] In view of the fact that some professional terms are involved in the embodiments of this application, the following will introduce these professional terms.

[0111] 1. Metadata: It refers to the data that describes the relationships between data. In a data application system, metadata generally refers to the data that describes data concepts, relationships between data, and data processing rules. Domain semantics and knowledge also belong to the category of metadata.

[0112] 2. Technical metadata: Technical statistical indicators generated during the data development process. It refers to the data used by the designers and managers of a data warehouse for developing and daily managing the data warehouse. Technical metadata includes data source information, descriptions of data transformation, definitions of objects and data structures within the data warehouse, rules used for data cleaning and data updating, mappings from source data to destination data, user access permissions, data backup history records, data import history records, information release history records, etc. Common technical metadata includes data lineage, fan-in number, fan-out number, field name, field length, and database table structure, etc.

[0113] 3. Business metadata: It represents various attributes and concepts in the enterprise environment using information such as business names, definitions, and descriptions. To a certain extent, the business context behind all data can be regarded as business metadata, such as business names, business definitions, business descriptions, etc.

[0114] 4. Data lineage: It is a concept in data governance. It is to find the connections between relevant data during the process of data traceability and belongs to a logical concept. The lineage relationship of data also includes some specific characteristics, such as attribution, multi-source nature, traceability, and hierarchy.

[0115] Specific data belongs to a specific organization or individual, that is, it has attribution.

[0116] The same data can have multiple sources. A piece of data can be generated by processing multiple pieces of data, and this processing process can be multiple, that is, it has multi-source nature.

[0117] The lineage relationship of data reflects the life cycle of data and reflects the entire process from the generation to the disappearance of data, that is, it has traceability.

[0118] The lineage relationship of data is hierarchical. The classification, induction, and summary of data, and the information describing the data form new data, and the description information at different levels forms the hierarchy of data.

[0119] 5. Complex Network: It refers to a network with some or all of the properties of self-organization, self-similarity, attractor, small world, and scale-free. Its research directions include key node discovery, community discovery, and link prediction. Among them, key node discovery aims to discover the nodes that play a key role in the structure and function of the network. Community discovery aims to discover the community structure in the complex network so as to reasonably divide the composition of network nodes. Link prediction aims to predict the possibility of links existing between any nodes in the complex network.

[0120] 6. Business Domain: It refers to the business scope and field to which the data belongs and is one of the key metadata. The business domain generally refers to the subject to which the data table belongs. For example, for WeChat Pay, the business domains may include red envelopes, transfers, financial products, or marketing products, etc.

[0121] Combined with the above introduction, the following will introduce the data table display method based on the business domain in this application. Please refer to Figure 3 , an embodiment of the data table display method based on the business domain in the embodiment of this application includes:

[0122] 101. Obtain M metadata from M data tables, where the data tables in the M data tables and the metadata in the M metadata have a corresponding relationship, and M is an integer greater than or equal to 2;

[0123] In this embodiment, the data table display device can obtain M data tables from the data, and each data table has a corresponding metadata. It should be noted that the data table display device can be deployed on a computer device, which can be a server, or a terminal device, or a system composed of a server and a terminal device. This application does not make a limitation.

[0124] For easy understanding, please refer to Table 1, which is an example of a data table.

[0125] Table 1

[0126]

[0127] As can be seen from Table 1, the data table can include the table name, the fields in the table, and the records of the table. The table name should ensure its uniqueness, and the name of the table should match its use, be concise and intuitive. The length of the fields in the table is usually less than 64 characters, and the field names include letters, Chinese characters, numbers, spaces, and other characters. The records of the table are the specific parameters under the corresponding fields.

[0128] Combined with Table 1, please refer to Table 2, which is an example of the metadata corresponding to the data table.

[0129] Table 2

[0130]

[0131]

[0132] It should be noted that only part of the metadata is shown in Table 2. In actual situations, the metadata may also include the number of fan-ins, the number of fan-outs, and the database table structure, etc., which are not limited herein.

[0133] 102. Determine the association relationship between M data tables according to the M metadata;

[0134] In this embodiment, the data table display device uses the metadata of each data table as the feature of the data table, and based on these metadata, the association relationship between the data tables can be constructed. For example, based on the data lineage, the upstream and downstream relationships between the data tables can be determined. For ease of understanding, please refer to Figure 4 , Figure 4 which is a schematic diagram of converting a data table into a data node in the embodiment of the present application. As Figure 4 shown in Figure (A) in

[0135] 103. Construct a target network according to the association relationship between the M data tables, where the target network includes M data nodes, and the data nodes in the M data nodes have a corresponding relationship with the data tables in the M data tables, and each data node is used to store a data table;

[0136] In this embodiment, the data table display device can construct a target network based on the association relationship between the M data tables. Specifically, the target network can be a complex network. The target network includes M data nodes, each data node is used to store a data table, and the feature of each data node is the metadata of the data table.

[0137] For ease of understanding, please refer to Figure 4 again. It can be seen from Figure (A) in Figure 4 that assuming M is 10, that is, there are 10 data tables, a target network is constructed according to the association relationship between these 10 data tables, that is, as Figure 4The target network shown in Figure (B). The target network includes 10 data nodes. Among them, the 1st data node is used to store the "category specification table", the 2nd data node is used to store the "commodity category table", the 3rd data node is used to store the "discount information table", the 4th data node is used to store the "commodity details table", the 5th data node is used to store the "commodity specification table", the 6th data node is used to store the "commodity access information table", the 7th data node is used to store the "user information table", the 8th data node is used to store the "order approval table", the 9th data node is used to store the "order history table", and the 10th data node is used to store the "order commodity table".

[0138] It should be noted that Figure 4 The number of data nodes and data tables shown is only for illustration and should not be construed as a limitation to this application.

[0139] 104. Perform area division processing on the target network to obtain at least one business domain. Among them, each business domain includes at least one data table.

[0140] In this embodiment, the data table display device divides the data nodes in the target network to obtain at least two regions (or communities), and then determines whether the divided regions can be used as business domains. If a region can be used as a business domain, then the business domain is obtained. Since each region includes at least one data node, the data tables stored by these data nodes are used as the data tables included in the business domain.

[0141] For ease of explanation, please refer to Figure 5 , Figure 5 is a schematic diagram of generating a business domain based on the target network in an embodiment of this application. As shown in the figure, assume that three regions, namely Region A, Region B, and Region C, are obtained after dividing the target network. Among them, assume that Region A, Region B, and Region C all meet the business domain determination conditions. Then, business domains A, B, and C can be obtained. Since there are 10 data nodes in Region A, business domain A includes 10 data tables. Since there are 7 data nodes in Region B, business domain A includes 7 data tables. Since there are 14 data nodes in Region C, business domain C includes 14 data tables.

[0142] 105. When an operation for the target viewing interface is obtained, display at least one data table corresponding to the target business domain through the interface of the terminal device, where the target viewing interface is the viewing interface corresponding to the target business domain in at least one business domain.

[0143] In this embodiment, when the data table display device obtains an operation on the target viewing interface, the corresponding target business domain can be determined according to the target viewing interface, and the target business domain is one of at least one business domain. Therefore, the data table display device can display at least one data table corresponding to the target business domain through the interface of the terminal device.

[0144] In an embodiment of the present application, a method for displaying data tables based on business domains is provided. First, M metadata from M data tables are obtained. There is a one-to-one correspondence between the data tables and the metadata. Then, the association relationship between the M data tables is determined according to the M metadata. Thus, a target network is constructed based on the association relationship between the M data tables. The target network includes M data nodes, and each data node also has a one-to-one correspondence with a data table. Each data node is used to store a data table. Finally, the target network is subjected to area division processing to obtain at least one business domain. Each business domain includes at least one data table. When an operation on the target viewing interface is obtained, at least one data table corresponding to the target business domain is displayed through the interface of the terminal device. By the above method, the relationship between data tables can be determined using the metadata corresponding to each data table. A target network is formed based on the relationship between the data tables. Then, a community division algorithm can be used to divide the target network into areas, so as to obtain at least one area. Each divided area can be regarded as a business domain, and each data node in the area is the data table included in the business domain. Thus, the automatic integration of data tables can be realized based on business domains, which is convenient for developers to directly view the data tables of a certain business domain, thereby saving time and labor costs.

[0145] Optionally, based on the corresponding embodiment above, in another optional embodiment provided by the embodiment of the present application, the M data tables at least include a first data table and a second data table; Figure 3 The obtaining of M metadata from M data tables specifically includes the following steps:

[0146] Obtain the first metadata from the first data table;

[0147] Obtain the second metadata from the second data table;

[0148] Obtain the second metadata from the second data table;

[0149] The determining of the association relationship between the M data tables according to the M metadata specifically includes the following steps:

[0150] Determine the target association relationship between the first data table and the second data table according to the first metadata and the second metadata, where the target association relationship includes at least one of the edge direction and the edge weight between the first data node and the second data node, the first data node is used to store the first data table, and the second data node is used to store the second data table.

[0151] In this embodiment, a method for constructing the association relationship between data tables is introduced. For the sake of convenience of description, the first data table and the second data table in the M data tables are taken as examples. It can be understood that for the other data tables in the M data tables, a similar method can be used to determine the association relationship between the data tables, which will not be elaborated here.

[0152] Specifically, the data table display device first determines the first data table and the second data table, and then obtains the first metadata corresponding to the first data table and the second metadata of the second data table. If there is an association relationship between the first metadata and the second metadata, it is determined that there is also an association between the first data table and the second data table. Among them, the first data table is stored in the first data node, the second data table is stored in the second data node, and the target association relationship between the first data node and the second data node is the target association relationship between the first data table and the second data table.

[0153] It should be noted that the target association relationship may include the edge direction between the first data node and the second data node, or the target association relationship may include the edge weight between the first data node and the second data node, or the target association relationship includes both the edge direction between the first data node and the second data node and the edge weight between the first data node and the second data node.

[0154] Secondly, in the embodiments of the present application, a method for constructing the association relationship between data tables is provided. Through the above method, for two data tables, the association relationship between them can be determined by using their corresponding metadata, so as to obtain a more accurate association relationship, thereby improving the feasibility of the solution.

[0155] Optionally, on the basis of the above Figure 3 In another optional embodiment provided by the embodiments of the present application corresponding to the above, determining the target association relationship between the first data table and the second data table according to the first metadata and the second metadata specifically includes the following steps:

[0156] Determine the edge direction between the first data node and the second data node according to the technical metadata included in the first metadata and the technical metadata included in the second metadata;

[0157] Determine the edge weight between the first data node and the second data node according to the service metadata included in the first metadata and the service metadata included in the second metadata.

[0158] In this embodiment, a method for determining the target association relationship according to metadata is introduced. The first metadata includes technical metadata and service metadata, and the second metadata also includes technical metadata and service metadata. The technical metadata can reflect the source and composition of data, etc. Therefore, based on the technical metadata, the edge direction between two data nodes can be determined, where the edge direction represents the upstream and downstream relationship between the data nodes, and thus the upstream and downstream relationship between the data tables can also be known. Based on the service metadata, the edge weight between two data nodes can be determined, where the edge weight represents the degree of association between two data nodes. The greater the weight, the stronger the association, and thus the degree of association between the data tables can also be known.

[0159] Next, two examples will be combined to introduce the methods for constructing the edge direction and edge weight between data nodes respectively.

[0160] I. Construct the edge direction between data nodes;

[0161] Specifically, please refer to Figure 6 , Figure 6 which is a schematic diagram of the association relationship between data nodes in the embodiment of the present application. As shown in Figure (A) of Figure 6 , taking the target network including 7 data nodes as an example, the edges between the 7 data nodes represent the associations between the data nodes. Since each data node stores a data table and each data table corresponds to a metadata, therefore, based on the metadata of the 7 data nodes, the edge direction between the data nodes can also be constructed, that is, the edge as shown in Figure (B) of Figure 6 is obtained. Among them, there is an upstream and downstream relationship between the two data nodes connected by the arrow, and the arrow points to the downstream data node. For example, the 1st data node is the upstream node of the 3rd data node. Assuming that the 1st data node stores the 1st data table and the 3rd data node stores the 3rd data table, that is, the 1st data table is the upstream data table of the 3rd data table.

[0162] II. Construct the edge weight between data nodes;

[0163] Specifically, please refer to Figure 7 , Figure 7 which is another schematic diagram of the association relationship between data nodes in the embodiment of the present application. As shown in Figure 7As shown in Figure (A), taking the target network including 7 data nodes as an example, the connections between the 7 data nodes represent the associations between the data nodes, and the connection direction represents the upstream and downstream relationships between the data nodes. Since each data node stores a data table and each data table corresponds to a piece of metadata, therefore, based on the metadata of the 7 data nodes, the connection weights between the data nodes can also be constructed. Among them, the thicker the connection, the greater the weight; conversely, the thinner the connection, the smaller the weight. For example, the connection weight between data node No. 6 and data node No. 7 is relatively small. Assuming that data node No. 6 stores data table No. 6 and data node No. 7 stores data table No. 7, that is, the correlation between data table No. 6 and data table No. 7 is relatively low. Another example is that the connection weight between data node No. 2 and data node No. 5 is relatively large. Assuming that data node No. 2 stores data table No. 2 and data node No. 5 stores data table No. 5, that is, the correlation between data table No. 2 and data table No. 5 is relatively high.

[0164] Again, in the embodiments of the present application, a method for determining the target association relationship according to metadata is provided. Through the above method, it is possible to combine technical metadata and business metadata to construct the association relationship between two data nodes from different dimensions (i.e., connection direction and connection weight), and then determine the association relationship between data tables, thereby improving the feasibility and operability of the solution.

[0165] Optionally, on the basis of the above Figure 3 In another optional embodiment provided by the embodiments of the present application based on the corresponding embodiment, according to the technical metadata included in the first metadata and the technical metadata included in the second metadata, determine the connection direction between the first data node and the second data node, which specifically includes the following steps:

[0166] According to the technical metadata included in the first metadata, obtain the data lineage corresponding to the first data table;

[0167] According to the technical metadata included in the second metadata, obtain the data lineage corresponding to the second data table;

[0168] According to the data lineage corresponding to the first data table and the data lineage corresponding to the second data table, determine the upstream data table from the first data table and the second data table, where the data lineage belongs to technical metadata;

[0169] If the upstream data table is the first data table, construct a connection from the first data node to the second data node;

[0170] If the upstream data table is the second data table, construct a connection from the second data node to the first data node.

[0171] In this embodiment, a method for determining the connection edge direction between data nodes based on data lineage is introduced. The first metadata table includes technical metadata, and the second metadata also includes technical metadata. Technical metadata can reflect the source and composition of data, etc. Therefore, based on technical metadata, the connection edge direction between two data nodes can be determined.

[0172] Specifically, the data table display device can determine the data lineage of the first data table and the data lineage of the second data table according to the data lineage relationship table. If there is a data lineage relationship table, then the data table display device can directly extract the data lineage of the data table from the data lineage relationship table. The data lineage relationship table refers to a table used to store the data lineage relationship between data nodes. For example, each data table can include the table name of the downstream data table and the table name of the upstream data table, and can also record the way of processing the downstream data table from the upstream data table, etc. If there is no data lineage relationship table, then the data table display device can periodically obtain technical metadata from Structured Query Language (SQL) code information and log information, and then extract the data lineage from the technical metadata.

[0173] Since data tables can be stored in data nodes, the data lineage between data tables can directly affect the upstream and downstream relationships between data nodes. Taking the first data table and the second data table as an example, if the first data table is the upstream data table of the second data table, it means that the first data node is the upstream data node of the second data node. Therefore, the connection edge direction from the first data node to the second data node is from the first data node to the second data node. On the contrary, if the second data table is the upstream data table of the first data table, it means that the second data node is the upstream data node of the first data node. Therefore, the connection edge direction from the first data node to the second data node is from the second data node to the first data node.

[0174] For ease of understanding, please refer to Figure 8 , Figure 8This is a schematic diagram for determining the edge connection direction between data nodes based on data lineage in an embodiment of the present application. As shown in the figure, taking 5 data nodes as an example, each data node stores a data table. Among them, the No. 1 data table is the upstream data table of the other 4 data packets, that is, the No. 1 data node is the upstream data node of the other 4 data nodes. Among them, W_1,2 represents the weight from the No. 1 data node to the No. 2 data node, that is, it represents the correlation between the No. 1 data table and the No. 2 data table. W_1,3 represents the weight from the No. 1 data node to the No. 3 data node, that is, it represents the correlation between the No. 1 data table and the No. 3 data table. W_1,4 represents the weight from the No. 1 data node to the No. 4 data node, that is, it represents the correlation between the No. 1 data table and the No. 4 data table. W_1,5 represents the weight from the No. 1 data node to the No. 5 data node, that is, it represents the correlation between the No. 1 data table and the No. 5 data table.

[0175] Furthermore, in an embodiment of the present application, a method for determining the edge connection direction between data nodes based on data lineage is provided. Through the above method, the correlation relationship between data tables characterized by data lineage can be obtained, and then all data tables can be characterized as the relationship between points and edges. By introducing data mining methods for complex networks, community division and clustering of data assets can be carried out, so as to locate the business domain corresponding to the data assets and perform refined management of data assets, thereby improving the effective operation and precipitation of data assets.

[0176] Optionally, based on the above Figure 3 corresponding embodiment, in another optional embodiment provided by the embodiment of the present application, the edge connection weight between the first data node and the second data node is determined according to the business metadata included in the first metadata and the business metadata included in the second metadata. The specific steps are as follows:

[0177] Obtain the business name corresponding to the first data table according to the business metadata included in the first metadata;

[0178] Obtain the business name corresponding to the second data table according to the business metadata included in the second metadata;

[0179] Determine the correlation degree between the first data table and the second data table according to the business name corresponding to the first data table and the business name corresponding to the second data table;

[0180] Determine the edge connection weight between the first data node and the second data node according to the correlation degree between the first data table and the second data table.

[0181] In this embodiment, a method for determining the edge weight based on the business name is introduced. The first metadata table includes technical metadata, and the second metadata also includes technical metadata. The technical metadata can reflect the source and composition of the data, etc. Therefore, based on the technical metadata, the edge weight between two data nodes can be determined.

[0182] Specifically, taking the determination of the edge weight between the first data node and the second data node as an example, first obtain the business name of the first data table and the business name of the second data table, and then the similarity between the two business names can be determined based on Natural Language Processing (NLP) technology. There are mainly three ways to measure text similarity, which will be introduced separately below.

[0183] I. Keyword-based matching method;

[0184] (1) Define the similarity of the business name based on the N-gram language model. The calculation of the N-gram similarity means splitting the business name by length N to obtain word segments, that is, all substrings of length N in the business name. For two business names, the correlation between the two business names can be defined from the number of common substrings.

[0185] (2) Based on the Jaccard algorithm, the ratio of the intersection and union of the word sets between two business names can be calculated. The larger this value, the more similar the two business names are. When it comes to large-scale parallel operations, this method has certain advantages in terms of efficiency.

[0186] II. Vector space-based matching method;

[0187] Generate vectors for each business name based on word to vector (Word2vec), and then the correlation between the two business names can be calculated using Euclidean distance, Manhattan distance, cosine similarity distance, Hamming distance, or Pearson correlation coefficient, etc.

[0188] III. Deep learning-based matching method;

[0189] Use a pre-trained semantic matching model to predict the correlation between text names. The semantic matching model includes but is not limited to Deep Structured Semantic Models (DSSM), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM) networks, and tree-shaped LSTM networks, which are not limited here.

[0190] Furthermore, in the embodiments of the present application, a method for determining the edge weight based on the business name is provided. Through the above method, the association between data tables can be determined by the business name. Generally, the higher the degree of business name association, the closer the relationship between the two data tables, and the more accurate the edge weight constructed thereby.

[0191] Optionally, based on the corresponding embodiments above, in another optional embodiment provided by the embodiments of the present application, the edge weight between the first data node and the second data node is determined according to the business metadata included in the first metadata and the business metadata included in the second metadata, which specifically includes the following steps: Figure 3 Based on the business metadata included in the first metadata, obtain the business description corresponding to the first data table;

[0192] Based on the business metadata included in the second metadata, obtain the business description corresponding to the second data table;

[0193] Based on the business description corresponding to the first data table and the business description corresponding to the second data table, obtain the association degree between the first data table and the second data table through the semantic matching model;

[0194] Based on the association degree between the first data table and the second data table, determine the edge weight between the first data node and the second data node.

[0195] In this embodiment, a method for determining the edge weight based on the business description is introduced. The first metadata table includes technical metadata, and the second metadata also includes technical metadata. The technical metadata can reflect the source and composition of the data, etc. Therefore, based on the technical metadata, the edge weight between two data nodes can be determined.

[0196] Specifically, taking the determination of the edge weight between the first data node and the second data node as an example, first obtain the business descriptions of the first data table and the second data table, and then input the business descriptions into the semantic matching model. The semantic matching model outputs the association degree (i.e., the association degree score). The greater the association degree, the greater the edge weight.

[0197] It should be noted that in practical applications, the association degree between two business descriptions can also be determined based on the keyword matching method or the vector space matching method, which will not be elaborated here.

[0198]

[0199] ​Furthermore, in the embodiments of the present application, a method for determining the edge weight based on the service description is provided. Through the above method, since the service description often covers information related to the service, it is possible to determine the association between data tables through the service description. Generally, the higher the degree of association of the service names, the closer the relationship between the two data tables, and the more accurate the edge weight constructed in this way.

[0200] Optionally, based on the above Figure 3 corresponding embodiment, in another optional embodiment provided by the embodiments of the present application, the target network is subjected to regional division processing to obtain at least one service domain, which specifically includes the following steps:

[0201] The data nodes in the target network are divided to obtain N regions, where N is an integer greater than or equal to 1 and less than or equal to M;

[0202] At least one service domain is determined according to the N regions.

[0203] In this embodiment, a method for dividing the target network into regions is introduced. Using the community discovery algorithm, the data edge nodes in the target network can be divided, and thus N divided regions are obtained, and at least one service domain is determined from the N regions.

[0204] Specifically, there are various types of community discovery algorithms, including algorithms based on graph segmentation, methods based on hierarchical clustering, and methods based on modularity optimization. Among them, the algorithms based on graph segmentation may include the Kernighan-Lin (KL) algorithm and the spectral bisection method, etc. The methods based on hierarchical clustering may include the Girvan Newman (GN) algorithm and the Newman fast algorithm, etc. The methods based on modularity optimization may include the fast unfolding algorithm, the greedy algorithm, the simulated annealing algorithm, the Memetic algorithm, the Particle Swarm Optimization (PSO) algorithm, and the evolutionary multi-objective optimization algorithm, etc.

[0205] Furthermore, in the embodiments of the present application, a method for dividing the target network into regions is provided. Through the above method, the automatic division of regions is realized by using an algorithm, without manually delimiting different regions, thereby improving the flexibility and convenience of the solution.

[0206] Optionally, based on the above Figure 3 corresponding embodiment, in another optional embodiment provided by the embodiments of the present application, the data nodes in the target network are divided to obtain N regions, which specifically includes the following steps:

[0207] Obtain the data nodes to be partitioned from the target network;

[0208] Obtain the first data node and the second data node according to the data nodes to be partitioned, where both the first data node and the second data node are data nodes adjacent to the data nodes to be partitioned;

[0209] Determine the first modularity according to the data nodes to be partitioned and the first data node;

[0210] Determine the second modularity according to the data nodes to be partitioned and the second data node;

[0211] If both the first modularity and the second modularity are greater than 0, and the first modularity is greater than the second modularity, then determine that the data node to be partitioned and the first data node belong to the same region among the N regions;

[0212] If both the first modularity and the second modularity are greater than 0, and the first modularity is less than the second modularity, then determine that the data node to be partitioned and the second data node belong to the same region among the N regions;

[0213] Until the algorithm termination condition is satisfied, obtain the N regions.

[0214] In this embodiment, a method for partitioning regions by the fastunfolding algorithm is introduced. After constructing the data nodes and the connections, the fastunfolding algorithm based on the modularity Q can be used for region partitioning. The modularity becomes an important criterion for measuring the quality of community partitioning. The larger the modularity value of the network after partitioning, the better the effect of community partitioning. The fastunfolding algorithm is an algorithm for community partitioning based on modularity. The fastunfolding algorithm is an iterative algorithm, and its main goal is to continuously partition communities so that the modularity of the entire network after partitioning continuously increases. The modularity refers to the proportion of the edges connecting the vertices inside the regional structure in the network, minus the expected value of the proportion of randomly connecting these two data nodes under the same regional structure.

[0215] It can be understood that the definition of modularity is:

[0216]

[0217] Among them, Q represents the modularity, represents all the weights in the target network, A i,j represents the connection weight between data node i and data node j, k i =∑ j A i,j represents the connection weight with data node i, c i represents the region to which the vertex is assigned, δ(ci , c j ) indicates whether data node i and data node j are partitioned into the same region. If so, it is 1; otherwise, it is 0.

[0218] For ease of understanding, please refer to Figure 9 , Figure 9 which is a schematic diagram of partitioning regions based on the fastunfolding algorithm in the embodiments of this application. As shown in the figure, the fastunfolding algorithm includes two stages. The first stage is modularity optimization, mainly partitioning each data node into the region where its adjacent data nodes are located to continuously increase the value of modularity. The second stage is community aggregation, mainly aggregating the regions partitioned in the first step into one point, that is, reconstructing the network according to the region structure generated in the previous step. Repeat the above process until the algorithm termination condition is met, and then N regions can be obtained. Exemplarily, the algorithm termination condition can be to preset a partitioning threshold. When the number of partitions reaches the partitioning threshold, the algorithm termination condition is met. Exemplarily, the algorithm termination condition can also be until the structure in the network no longer changes.

[0219] Specifically, the process of the fast unfolding algorithm includes the following steps:

[0220] In step 1, initialize by partitioning each data node into different regions.

[0221] In step 2, for each data node, attempt to partition each data node into the region where its adjacent data nodes are located, calculate the modularity at this time, and judge the difference ΔQ of the modularity before and after partitioning. Judge whether ΔQ is positive. If it is positive, accept this partition; if it is not positive, abandon this partition.

[0222] In step 3, repeat the above process until the modularity can no longer be increased.

[0223] In step 4, construct a new graph. Each node in the new graph represents each region partitioned in step 3, and continue to execute steps 2 and 3 until the structure of the region no longer changes.

[0224] Furthermore, in the embodiments of this application, a method for partitioning regions by the fast unfolding algorithm is provided. Through the above method, region partitioning can be achieved without supervision. The whole process is easy to implement, the algorithm is fast, and it is easy to calculate the modularity gain. Since the first stage of the algorithm is designed to shift a single data node from one region to another, the algorithm has an inherent multi-level characteristic, so the problem of modular resolution limit can also be avoided.

[0225] Optionally, based on the above-mentioned Figure 3 corresponding embodiment, in another optional embodiment provided by the embodiments of the present application, the data nodes in the target network are partitioned to obtain N regions, which specifically include the following steps:

[0226] Obtain a first region and a second region from the target network;

[0227] Obtain a first gain value according to the first region and the second region, where the first gain value is the difference between the sum of the number of internal edges in the first region and the number of internal edges in the second region and the number of edges between the first region and the second region;

[0228] Obtain a first data node in the first region and a second data node in the second region;

[0229] Add the second data node to the first region to obtain an updated first region, and add the first data node to the second region to obtain an updated second region;

[0230] Obtain a second gain value according to the updated first region and the updated second region, where the second gain value is the difference between the sum of the number of internal edges in the updated first region and the number of internal edges in the updated second region and the number of edges between the updated first region and the updated second region;

[0231] Determine a target gain value according to the first gain value and the second gain value;

[0232] If the target gain value is the maximum value among P gain values, it is determined that the first data node belongs to the updated second region, and the second data node belongs to the updated first region, where the P gain values include the gain values of pairs of data nodes between the first region and the second region, and P is an integer greater than or equal to 1;

[0233] Until the algorithm termination condition is met, obtain N regions.

[0234] In this embodiment, a method for partitioning regions by the Kernighan-Lin algorithm is introduced. The Kernighan-Lin algorithm can arbitrarily partition M data nodes into two regions of a specified scale. For any pair of nodes (i, j) composed of data nodes i and data node j belonging to different regions, exchange the positions between data node i and data node j, and then calculate the gain value between the two regions before and after the exchange. Find the maximum gain value among all pairs of nodes, and exchange the pair of nodes corresponding to the maximum gain value. Repeat the above process until N regions are obtained when the algorithm termination condition is met.

[0235] Specifically, for the convenience of introduction, please refer to Figure 10 ,Figure 10 This is a schematic diagram of dividing regions based on the Kernighan-Lin algorithm in an embodiment of this application. As shown in Figure 10 (A) of the figure, data nodes 1, 2, 6, and 7 belong to the first region, and data nodes 3, 4, 5, and 8 belong to the second region. Based on this, calculate the sum of the number of edges within the first region and the number of edges within the second region. Taking Figure 10 (A) of the figure as an example, the number of edges within the first region is 1, the number of edges within the second region is 4, and the sum of the number of edges within the first region and the number of edges within the second region is 5. Calculate the sum of the number of edges between the first region and the second region. Taking Figure 10 (A) of the figure as an example, the number of edges between the first region and the second region is 7. Therefore, the first gain value is 5 - 7 = -2.

[0236] Then, obtain the first data node from the first region (for example, data node 7), obtain the second data node from the second region (for example, data node 4), and after exchanging the first data node and the second data node, obtain the updated first region and the updated second region. As shown in Figure 10 (B) of the figure, data nodes 1, 2, 4, and 6 belong to the updated first region, and data nodes 3, 5, 7, and 8 belong to the updated second region. Based on this, calculate the sum of the number of edges within the updated first region and the number of edges within the updated second region. Taking Figure 10 (B) of the figure as an example, the number of edges within the updated first region is 4, the number of edges within the updated second region is 4, and the sum of the number of edges within the updated first region and the number of edges within the updated second region is 8. Calculate the sum of the number of edges between the updated first region and the updated second region. Taking Figure 10 (B) of the figure as an example, the number of edges between the updated first region and the updated second region is 4. Therefore, the second gain value is 8 - 4 = 4.

[0237] After subtracting the second gain value from the first gain value and taking the absolute value, the target gain value can be obtained. Combining the above example, |-2 - 4| = 6. After pairwise exchanging the data nodes within the first region and the second region, P gain values can be obtained. If the target gain value is the maximum among these P gain values, then take the first data node as the data node within the second region and take the second data node as the data node within the first region. And so on, until the algorithm termination condition is met, N regions are obtained.

[0238] Exemplarily, the algorithm termination condition can be to preset a swap threshold. When the number of swaps reaches the iteration threshold, the algorithm termination condition is satisfied. Exemplarily, the algorithm termination condition can also be to calculate the evaluation parameter of each region. When the evaluation parameter is greater than the parameter threshold, it indicates that the algorithm termination condition is satisfied. The calculation method of the evaluation parameter is as follows:

[0239]

[0240] Among them, U represents the evaluation parameter, and e ii represents the proportion of the number of edges connecting each data node inside a certain region in the total number of all edges, and a i represents the proportion of the edges connected to the data nodes in the i-th region in the total number of all edges.

[0241] It should be noted that other methods can also be used to calculate the evaluation parameter. This is only an illustration here and should not be construed as a limitation to this application.

[0242] Furthermore, in the embodiments of this application, a method for partitioning regions by the Kernighan-Lin algorithm is provided. Through the above method, considering each pair of data nodes in the target network and analyzing them accordingly, the analysis difficulty is relatively low, and it can ensure that all data nodes go through swaps, thereby obtaining a more accurate region partitioning result.

[0243] Optionally, based on the above Figure 3 corresponding embodiment, in another optional embodiment provided by the embodiments of this application, the data nodes in the target network are partitioned to obtain N regions, which specifically include the following steps:

[0244] According to the M data nodes and K edges in the target network, determine K edge betweennesses, where the edge betweenness in the K edge betweennesses has a corresponding relationship with the edges in the K edges;

[0245] Select a target edge betweenness from the K edge betweennesses, where the target edge betweenness is the maximum value among the K edge betweennesses;

[0246] Delete the edge corresponding to the target edge betweenness;

[0247] Until the algorithm termination condition is satisfied, obtain N regions.

[0248] In this embodiment, a method for partitioning regions based on the GN algorithm is introduced. The GN algorithm is a community discovery algorithm and belongs to a splitting hierarchical clustering algorithm. Its basic idea is to continuously delete the edge with the maximum betweenness relative to all source nodes in the network, and then recalculate the betweenness of the remaining edges in the network relative to all source nodes, repeating this process until the algorithm termination condition is satisfied.

[0249] Specifically, for the convenience of introduction, please refer to Figure 11 , Figure 11 which is a schematic diagram of dividing regions based on the GN algorithm in the embodiment of the present application. As shown in Figure 11 (A) of the figure, it is assumed that the target network includes 7 data nodes and has 6 connections. The betweenness centrality of each connection can be calculated. For example, the betweenness centrality of the connection between data node 3 and data node 4 is 12, that is, a total of 12 pairs of data nodes pass through the connection between data node 3 and data node 4. Based on this, the betweenness centrality shown in Figure 11 (B) of the figure is obtained. Among them, the betweenness centrality of the connection between data node 1 and data node 3 is 6, the betweenness centrality of the connection between data node 2 and data node 3 is 6, the betweenness centrality of the connection between data node 3 and data node 4 is 12, the betweenness centrality of the connection between data node 4 and data node 5 is 12, the betweenness centrality of the connection between data node 5 and data node 6 is 6, and the betweenness centrality of the connection between data node 5 and data node 7 is 6.

[0250] Thus, the betweenness centrality of the connection between data node 3 and data node 4, and the betweenness centrality of the connection between data node 4 and data node 5 are determined to be the maximum values. Then, one of the betweenness centralities is selected as the target betweenness centrality, and the connection corresponding to the target betweenness centrality is deleted, that is, the two regions shown in Figure 11 (C) of the figure are obtained. Similarly, continue to calculate the betweenness centrality between data nodes in each region, and then continue to delete the connection corresponding to the maximum betweenness centrality, that is, the three regions shown in Figure 11 (D) of the figure are obtained. And so on, until the algorithm termination condition is met, N regions are obtained.

[0251] Exemplarily, the algorithm termination condition can be to preset an iteration threshold. When the number of divisions reaches the iteration threshold, the algorithm termination condition is met. Exemplarily, the algorithm termination condition can also be to calculate the evaluation parameter of each region. When the evaluation parameter is greater than the parameter threshold, it means that the algorithm termination condition is met.

[0252] Furthermore, in the embodiment of the present application, a method for dividing regions based on the GN algorithm is provided. Through the above method, the global situation of the target network can be considered, and the divided regions have high accuracy. And considering the end point of region division, an algorithm termination condition can also be defined. Once the algorithm termination condition is reached, the continuous division of regions is stopped, so as to improve the processing efficiency while taking into account the effect of region division.

[0253] Optionally, on the basis of the above Figure 3 corresponding embodiment, in another optional embodiment provided by the embodiment of the present application, at least one service domain is determined according to the N regions, which specifically includes the following steps:

[0254] Obtain Q data nodes included in the area to be recognized from N areas, where Q is an integer greater than or equal to 1;

[0255] Determine the business domain corresponding to the area to be recognized according to the Q data tables corresponding to the Q data nodes, where there is a corresponding relationship between the data nodes in the Q data nodes and the data tables in the Q data tables.

[0256] In this embodiment, a method for interpreting the divided areas is introduced. As can be seen from the foregoing embodiments, after the target network is divided into N areas, the area to which each data node belongs can be obtained. For each area, it is necessary to determine whether the area meets the business domain determination condition. If it meets this condition, the corresponding business domain can be obtained.

[0257] Specifically, taking the area to be recognized as an example, the area to be recognized is one of the N areas. Assume that the area to be recognized includes Q data nodes, and thus Q data tables are obtained. Next, it is necessary to determine the type of the area to be recognized by combining business information and the form of manual sampling discrimination. For example, if Q is 100 and 70 of the Q data tables belong to the data tables of the "red envelope business", that is, the proportion of the "red envelope business" is 70%. If the business proportion is greater than or equal to the business proportion threshold, it is determined that the area to be recognized meets the business domain determination condition; otherwise, it does not meet the business domain determination condition.

[0258] For the convenience of introduction, please refer to Table 3, which is a schematic diagram of the relationship between the area and the business domain.

[0259] Table 3

[0260] Region Number of data nodes Business type Business proportion Name of business domain Region A 100 Red envelope business 70％ Red envelope business domain Region B 50 Online car-hailing business 40％ None Region C 60 Transfer business 75％ Transfer business domain Region D 150 E-commerce business 80％ E-commerce business domain

[0261] Taking the business proportion threshold of 60% as an example, in actual applications, the business proportion threshold can also be other ratios, which are not limited here. As can be seen from Table 3, the business proportions of the data tables in Area A, Area C, and Area D are all greater than the business proportion threshold. Therefore, the business domain can be interpreted as the business type corresponding to the majority of the data tables. Based on Area B, it can be seen that the proportion of the data tables belonging to the online car-hailing business is less than the business proportion threshold. Therefore, this area does not belong to any business domain, that is, it is interpreted as having no business domain.

[0262] Furthermore, in the embodiments of the present application, a method for interpreting the divided areas is provided. Through the above method, each already divided area can be further interpreted to determine the business domain to which the area belongs. Thus, a more reasonable business domain allocation result can be obtained.

[0263] Optionally, in the above Figure 3Based on the corresponding embodiments, in another alternative embodiment provided by the embodiments of the present application, when an operation on a target viewing interface is obtained, at least one data table corresponding to the target business domain is displayed through the interface of the terminal device, specifically including the following steps:

[0264] Display the business name and viewing interface corresponding to each business domain;

[0265] When an operation on the target viewing interface is detected, at least one data table corresponding to the target business domain is displayed through the interface of the terminal device;

[0266] Or,

[0267] When an operation on the target viewing interface is obtained, at least one data table corresponding to the target business domain is displayed through the interface of the terminal device, specifically including the following steps:

[0268] When the terminal device detects an operation on the target viewing interface, receive the viewing instruction sent by the terminal device;

[0269] Send at least one data table corresponding to the target business domain to the terminal device according to the viewing instruction, so that the terminal device displays at least one data table corresponding to the target business domain.

[0270] In this embodiment, a method for presenting business domains to relevant personnel is introduced. As can be seen from the foregoing embodiments, the data table display device can be deployed on the server or on the terminal device. Based on this, the following will be described in combination with the cases where the data table display device is deployed on different devices.

[0271] I. The data table display device is deployed on the terminal device;

[0272] After the terminal device obtains the business domains, it can directly display the business names and corresponding viewing interfaces of these business domains. For ease of understanding, please refer to Figure 12 , Figure 12 which is a schematic diagram of an interface for presenting business domains in the embodiments of the present application. As shown in the figure, different business domains are displayed on the interface of the data platform, such as "red envelope business", "transfer business", "online car-hailing business", and "e-commerce business", etc. When the user needs to view the data tables under the "red envelope business", the user can click the viewing interface corresponding to the "red envelope business", and this viewing interface is the target viewing interface.

[0273] In response to the user's operation on the target viewing interface, the terminal device can jump to the interface as shown in Figure 13 Please refer to Figure 13 , Figure 13This is a schematic diagram of an interface for displaying data tables in the target business domain in an embodiment of the present application. As shown in the figure, taking the target business domain as "red envelope business" as an example, there are 13 data tables under this target business domain, that is, the identifiers of these 13 data tables are displayed. If the user needs to view one or more of the data tables, they can directly select the options of the data tables. For example, select to view "Data Table 0156", "Data Table 3594", "Data Table 1072", "Data Table 4235", "Data Table 6569", and "Data Table 7711". After the selection is completed, click the "View" button to display the specific content within the data table.

[0274] Second, the data table display device is deployed on the server;

[0275] After the server obtains the business domain, it sends the business name corresponding to the business domain to the terminal device, so that the terminal device can display the business names of these business domains and the corresponding viewing interfaces. When the terminal device detects an operation on the target viewing interface, it can send a viewing instruction to the data table display device. The viewing instruction carries the identifier of the target business domain. Thus, the server sends at least one data table corresponding to the target business domain to the terminal device according to the viewing instruction, so that the terminal device can display at least one data table corresponding to the target business domain.

[0276] It should be noted that the interface for the terminal device to display the business names and viewing interfaces is similar to the Figure 12 interface shown, and the interface for the terminal device to display at least one data table under the target business domain is similar to the Figure 13 interface shown, so it will not be elaborated here.

[0277] Secondly, in the embodiment of the present application, a method for displaying the business domain to relevant personnel is provided. Through the above method, the divided business domains can be visually displayed, enabling relevant personnel to more intuitively find the data tables under a certain business domain without manually searching for the data tables under a certain business domain from a large number of data tables, thereby improving the data search efficiency and increasing the flexibility and operability of the solution.

[0278] The following will describe the data table display device in the present application in detail. Please refer to Figure 14 , Figure 14 This is a schematic diagram of an embodiment of the data table display device in an embodiment of the present application. The data table display device 20 includes:

[0279] An acquisition module 201, configured to acquire M metadata from M data tables, where the data tables in the M data tables have a corresponding relationship with the metadata in the M metadata, and M is an integer greater than or equal to 2;

[0280] A determination module 202, configured to determine the association relationship between M data tables according to M metadata;

[0281] A construction module 203, configured to construct a target network according to the association relationship between M data tables, where the target network includes M data nodes, and the data nodes in the M data nodes have a corresponding relationship with the data tables in the M data tables, and each data node is used to store a data table;

[0282] A partitioning module 204, configured to perform area partitioning processing on the target network to obtain at least one business domain, where each business domain includes at least one data table;

[0283] A display module 205, configured to, when an operation on a target viewing interface is obtained, display at least one data table corresponding to the target business domain through the interface of the terminal device, where the target viewing interface is the viewing interface corresponding to the target business domain in the at least one business domain.

[0284] In an embodiment of the present application, a data table display device is provided. By using the above device, the relationship between data tables can be determined by using the metadata corresponding to each data table, a target network is formed based on the relationship between the data tables, and then a community partitioning algorithm can be used to perform area partitioning on the target network, so as to obtain at least one area. Each partitioned area can be regarded as a business domain, and each data node in the area is the data table included in the business domain. Thus, the automatic integration of data tables can be realized based on the business domain, which is convenient for developers to directly view the data tables of a certain business domain, thereby saving time cost and labor cost.

[0285] Optionally, based on the corresponding embodiment above Figure 14 In another embodiment of the data table display device 20 provided in the embodiment of the present application, the M data tables at least include a first data table and a second data table;

[0286] An acquisition module 201 is specifically configured to acquire first metadata from the first data table;

[0287] Acquire second metadata from the second data table;

[0288] A determination module 202 is specifically configured to determine the target association relationship between the first data table and the second data table according to the first metadata and the second metadata, where the target association relationship includes at least one of the connection direction and the connection weight between the first data node and the second data node, the first data node is used to store the first data table, and the second data node is used to store the second data table.

[0289] In an embodiment of the present application, a data table display device is provided. By using the above device, for two data tables, the association relationship between them can be determined by using their corresponding metadata, so as to obtain a more accurate association relationship, thereby improving the feasibility of the solution.

[0290] Optionally, on the basis of the corresponding embodiment above, Figure 14 in another embodiment of the data table display device 20 provided in the embodiment of the present application,

[0291] The determination module 202 is specifically configured to determine the edge direction between the first data node and the second data node according to the technical metadata included in the first metadata and the technical metadata included in the second metadata;

[0292] Determine the edge weight between the first data node and the second data node according to the business metadata included in the first metadata and the business metadata included in the second metadata.

[0293] In an embodiment of the present application, a data table display device is provided. By using the above device, the association relationship between two data nodes can be constructed from different dimensions (i.e., edge direction and edge weight) by combining technical metadata and business metadata, and then the association relationship between data tables can be determined, thereby improving the feasibility and operability of the solution.

[0294] Optionally, on the basis of the corresponding embodiment above, Figure 14 in another embodiment of the data table display device 20 provided in the embodiment of the present application,

[0295] The determination module 202 is specifically configured to obtain the data lineage corresponding to the first data table according to the technical metadata included in the first metadata;

[0296] Obtain the data lineage corresponding to the second data table according to the technical metadata included in the second metadata;

[0297] Determine the upstream data table from the first data table and the second data table according to the data lineage corresponding to the first data table and the data lineage corresponding to the second data table, where the data lineage belongs to the technical metadata;

[0298] If the upstream data table is the first data table, then construct an edge from the first data node to the second data node;

[0299] If the upstream data table is the second data table, then construct an edge from the second data node to the first data node.

[0300] In an embodiment of the present application, a data table display device is provided. By using the above device, based on the association relationship between data tables depicted by data lineage, it is possible to depict all data tables as a relationship between points and edges. Then, by introducing a data mining method for complex networks, community partitioning and clustering of data assets are performed, thereby locating the business domain corresponding to the data assets and performing refined management of the data assets, so as to improve the effective operation and precipitation of the data assets.

[0301] Optionally, based on the corresponding embodiment above, in another embodiment of the data table display device 20 provided in the embodiment of the present application, Figure 14 The determination module 202 is specifically configured to obtain the business name corresponding to the first data table according to the business metadata included in the first metadata;

[0302] Obtain the business name corresponding to the second data table according to the business metadata included in the second metadata;

[0303] Determine the association degree between the first data table and the second data table according to the business name corresponding to the first data table and the business name corresponding to the second data table;

[0304] Determine the edge weight between the first data node and the second data node according to the association degree between the first data table and the second data table.

[0305]

[0306] In an embodiment of the present application, a data table display device is provided. By using the above device, it is possible to determine the association between data tables through the business name. Generally, the higher the association degree of the business name, the closer the relationship between the two data tables, and the more accurate the edge weight constructed thereby.

[0307] Optionally, based on the corresponding embodiment above, in another embodiment of the data table display device 20 provided in the embodiment of the present application, Figure 14 The determination module 202 is specifically configured to obtain the business description corresponding to the first data table according to the business metadata included in the first metadata;

[0308] Obtain the business description corresponding to the second data table according to the business metadata included in the second metadata;

[0309] Based on the business description corresponding to the first data table and the business description corresponding to the second data table, obtain the association degree between the first data table and the second data table through a semantic matching model;

[0310]

[0311] Determine the edge weight between the first data node and the second data node according to the association degree between the first data table and the second data table.​​

[0312] In an embodiment of the present application, a data table display device is provided. By using the above device, since business descriptions often cover information related to the business, it is possible to determine the association between data tables through business descriptions. Generally, the higher the degree of association of business names, the closer the relationship between two data tables, and the more accurate the edge weights constructed in this way.

[0313] Optionally, based on the corresponding embodiment above, in another embodiment of the data table display device 20 provided by the embodiment of the present application, Figure 14 The partitioning module 204 is specifically configured to perform a partitioning process on data nodes in the target network to obtain N regions, where N is an integer greater than or equal to 1 and less than or equal to M;

[0314] Determine at least one business domain according to the N regions.

[0315] In an embodiment of the present application, a data table display device is provided. By using the above device, the automatic partitioning of regions is realized by using an algorithm, and there is no need for manual delineation of different regions, thereby improving the flexibility and convenience of the solution.

[0316] Optionally, based on the corresponding embodiment above, in another embodiment of the data table display device 20 provided by the embodiment of the present application,

[0317] The partitioning module 204 is specifically configured to obtain data nodes to be partitioned from the target network; Figure 14 Based on the corresponding embodiment above, in another embodiment of the data table display device 20 provided by the embodiment of the present application,

[0318] Obtain a first data node and a second data node according to the data nodes to be partitioned, where both the first data node and the second data node are data nodes adjacent to the data nodes to be partitioned;

[0319] Determine a first modularity according to the data nodes to be partitioned and the first data node;

[0320] Determine a second modularity according to the data nodes to be partitioned and the second data node;

[0321] If both the first modularity and the second modularity are greater than 0, and the first modularity is greater than the second modularity, determine that the data node to be partitioned and the first data node belong to the same region among the N regions;

[0322] If both the first modularity and the second modularity are greater than 0, and the first modularity is less than the second modularity, determine that the data node to be partitioned and the second data node belong to the same region among the N regions;

[0323] If both the first modularity and the second modularity are greater than 0, and the first modularity is less than the second modularity, determine that the data node to be partitioned and the second data node belong to the same region among the N regions;

[0324] Until the algorithm termination condition is satisfied, obtain N regions.

[0325] In the embodiments of the present application, a data table display device is provided. By using the above device, regional division can be achieved without supervision. The whole process is easy to implement, the algorithm is fast, and the modular gain is easy to calculate. Since the first stage of the algorithm is designed to shift a single data node from one region to another region, the algorithm has an inherent multi-level characteristic, so the problem of modular resolution limitation can also be avoided.

[0326] Optionally, based on the corresponding embodiments described above, in another embodiment of the data table display device 20 provided in the embodiments of the present application, Figure 14 the partitioning module 204 is specifically configured to obtain a first region and a second region from the target network;

[0327] Based on the first region and the second region, obtain a first gain value, where the first gain value is the difference between the sum of the number of internal edges in the first region and the number of internal edges in the second region, and the number of edges between the first region and the second region;

[0328] Obtain a first data node in the first region and a second data node in the second region;

[0329] Add the second data node to the first region to obtain an updated first region, and add the first data node to the second region to obtain an updated second region;

[0330] Based on the updated first region and the updated second region, obtain a second gain value, where the second gain value is the difference between the sum of the number of internal edges in the updated first region and the number of internal edges in the updated second region, and the number of edges between the updated first region and the updated second region;

[0331] Based on the first gain value and the second gain value, determine a target gain value;

[0332] If the target gain value is the maximum value among P gain values, it is determined that the first data node belongs to the updated second region and the second data node belongs to the updated first region, where the P gain values include the gain values of pairwise data nodes between the first region and the second region, and P is an integer greater than or equal to 1;

[0333] Until the algorithm termination condition is met, obtain N regions.

[0334] Until the algorithm termination condition is satisfied, obtain N regions.

[0335] In the embodiments of the present application, a data table display device is provided. By using the above device, considering each pair of data nodes in the target network and analyzing them, it is possible to exchange all data nodes, thereby obtaining a more accurate regional division result.

[0336] Optionally, based on the corresponding embodiments described above, in another embodiment of the data table display device 20 provided by the embodiments of the present application, Figure 14 partitioning module 204 is specifically configured to determine K edge betweennesses according to M data nodes and K links in the target network, where the edge betweenness in the K edge betweennesses has a corresponding relationship with the link in the K links;

[0337] select a target edge betweenness from the K edge betweennesses, where the target edge betweenness is the maximum value among the K edge betweennesses;

[0338] delete the link corresponding to the target edge betweenness;

[0339] until the algorithm termination condition is satisfied, and N regions are obtained.

[0340] In the embodiments of the present application, a data table display device is provided. By using the above device, the global situation of the target network can be considered, the divided regions have high accuracy, and considering the end point of region division, an algorithm termination condition can also be defined. Once the algorithm termination condition is reached, the region division is stopped, so that while taking into account the region division effect, the processing efficiency can be improved.

[0341] Optionally, based on the corresponding embodiments described above, in another embodiment of the data table display device 20 provided by the embodiments of the present application,

[0342] partitioning module 204 is specifically configured to obtain Q data nodes included in the region to be recognized from the N regions, where Q is an integer greater than or equal to 1; Figure 14 based on the Q data tables corresponding to the Q data nodes, determine the service domain corresponding to the region to be recognized, where the data node in the Q data nodes has a corresponding relationship with the data table in the Q data tables.

[0343] In the embodiments of the present application, a data table display device is provided. By using the above device, each already divided region can be further interpreted to determine the service domain to which the region belongs. Thus, a more reasonable service domain allocation result can be obtained.

[0344] Optionally, based on the corresponding embodiments described above, in another embodiment of the data table display device 20 provided by the embodiments of the present application,

[0345] display module 205 is specifically configured to display the service name and viewing interface corresponding to each service domain;

[0346] Figure 14

[0347]

[0347] display module 205 is specifically configured to display the service name and viewing interface corresponding to each service domain;

[0348] When an operation on a target viewing interface is detected, at least one data table corresponding to the target business domain is displayed through the interface of the terminal device.

[0349] In an embodiment of the present application, a data table display device is provided. By using the above device, the divided business domains can be visually displayed, enabling relevant personnel to more intuitively find the data tables under a certain business domain without manually searching for the data tables under a certain business domain from a large number of data tables, thereby improving the data search efficiency and increasing the flexibility and operability of the solution.

[0350] Optionally, based on the corresponding embodiment above, in another embodiment of the data table display device 20 provided in the embodiment of the present application, Figure 14

[0351] The display module 205 is specifically configured to receive a viewing instruction sent by the terminal device when the terminal device detects an operation on the target viewing interface;

[0352] Send at least one data table corresponding to the target business domain to the terminal device according to the viewing instruction, so that the terminal device displays at least one data table corresponding to the target business domain.

[0353] In an embodiment of the present application, a data table display device is provided. By using the above device, the divided business domains can be visually displayed, enabling relevant personnel to more intuitively find the data tables under a certain business domain without manually searching for the data tables under a certain business domain from a large number of data tables, thereby improving the data search efficiency and increasing the flexibility and operability of the solution.

[0354] Figure 15 It is a schematic structural diagram of a server provided in an embodiment of the present application. The server 300 may vary greatly due to different configurations or performances, and may include one or more central processing units (CPUs) 322 (for example, one or more processors) and a memory 332, and one or more storage media 330 (for example, one or more mass storage devices) for storing application programs 342 or data 344. Among them, the memory 332 and the storage media 330 can be short-term storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server. Further, the central processing unit 322 may be configured to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the server 300.

[0355] ​The server 300 may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input / output interfaces 358, and / or one or more operating systems 341, such as Windows Server TM , Mac OS X TM , Unix TM , Linux TM , FreeBSD TM and so on.

[0356] The steps performed by the server in the above embodiments may be based on the Figure 15 server structure shown.

[0357] The embodiment of the present application also provides another image display control device. As Figure 16 shown, for ease of illustration, only the parts related to the embodiment of the present application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The terminal device may be any terminal device including a mobile phone, a tablet computer, a personal digital assistant (PDA), a point of sales (POS) device, an in-vehicle computer, etc. Taking the terminal device as a mobile phone as an example:

[0358] Figure 16 Shown is a block diagram of a part of the structure of a mobile phone related to the terminal device provided by the embodiment of the present application. Referring to Figure 16 , the mobile phone includes: a radio frequency (RF) circuit 410, a memory 420, an input unit 430, a display unit 440, a sensor 450, an audio circuit 460, a wireless fidelity (WiFi) module 470, a processor 480, and a power supply 490 and other components. Those skilled in the art can understand that Figure 16 the mobile phone structure shown in

[0359] does not limit the mobile phone, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Figure 16 The following specifically introduces each component of the mobile phone in combination with

[0360] The RF circuit 410 can be used for receiving and transmitting information or signals during communication. Specifically, after receiving the downlink information from the base station, it is sent to the processor 480 for processing. Additionally, the uplink data is sent to the base station. Generally, the RF circuit 410 includes but is not limited to antennas, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. Moreover, the RF circuit 410 can also communicate with the network and other devices via wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to the Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0361] The memory 420 can be used to store software programs and modules. The processor 480 executes various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory 420. The memory 420 mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the sound playback function, image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 420 can include high-speed random access memory and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0362] The input unit 430 can be used to receive input numeric or character information and generate key signal inputs related to the user settings and function controls of the mobile phone. Specifically, the input unit 430 may include a touch panel 431 and other input devices 432. The touch panel 431, also known as a touch screen, can collect touch operations of the user thereon or nearby (such as operations of the user using any suitable object or accessory like a finger, a stylus, etc. on or near the touch panel 431), and drive corresponding connection devices according to a pre-set program. Optionally, the touch panel 431 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch orientation of the user, detects the signals brought by the touch operation, and transmits the signals to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, then sends it to the processor 480, and can receive and execute commands sent by the processor 480. In addition, various types such as resistive, capacitive, infrared, and surface acoustic wave can be used to implement the touch panel 431. In addition to the touch panel 431, the input unit 430 may further include other input devices 432. Specifically, the other input devices 432 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, power on / off keys, etc.), a trackball, a mouse, a joystick, etc.

[0363] The display unit 440 can be used to display information input by the user or information provided to the user as well as various menus of the mobile phone. The display unit 440 may include a display panel 441. Optionally, the display panel 441 can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, the touch panel 431 can cover the display panel 441. When the touch panel 431 detects a touch operation thereon or nearby, it transmits it to the processor 480 to determine the type of touch event. Subsequently, the processor 480 provides corresponding visual output on the display panel 441 according to the type of touch event. Although in Figure 16 the touch panel 431 and the display panel 441 are implemented as two independent components to realize the input and output functions of the mobile phone, in some embodiments, the touch panel 431 and the display panel 441 can be integrated to realize the input and output functions of the mobile phone.

[0364] The mobile phone may further include at least one sensor 450, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. Among them, the ambient light sensor can adjust the brightness of the display panel 441 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 441 and / or the backlight when the mobile phone is moved to the ear. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary, and can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that the mobile phone can also be configured with, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be elaborated here.

[0365] The audio circuit 460, the speaker 461, and the microphone 462 can provide an audio interface between the user and the mobile phone. The audio circuit 460 can transmit the electrical signal converted from the received audio data to the speaker 461, and the speaker 461 converts it into a sound signal for output; on the other hand, the microphone 462 converts the collected sound signal into an electrical signal, which is received by the audio circuit 460 and then converted into audio data. After the audio data is output to the processor 480 for processing, it is sent through the RF circuit 410 to, for example, another mobile phone, or the audio data is output to the memory 420 for further processing.

[0366] WiFi belongs to short - range wireless transmission technology. The mobile phone can help users send and receive emails, browse the web, and access streaming media through the WiFi module 470, which provides users with wireless broadband Internet access. Although Figure 16 the WiFi module 470 is shown, it can be understood that it does not belong to the essential components of the mobile phone and can be omitted completely within the scope of not changing the essence of the invention according to needs.

[0367] The processor 480 is the control center of the mobile phone, connecting various parts of the entire mobile phone through various interfaces and lines. By running or executing the software programs and / or modules stored in the memory 420, and calling the data stored in the memory 420, it executes various functions of the mobile phone and processes data. Optionally, the processor 480 may include one or more processing units; optionally, the processor 480 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above - mentioned modem processor may not be integrated into the processor 480 either.

[0368] The mobile phone further includes a power supply 490 (such as a battery) for powering each component. Optionally, the power supply can be logically connected to the processor 480 through a power management system, so as to manage functions such as charging, discharging, and power consumption management through the power management system.

[0369] Although not shown, the mobile phone may further include a camera, a Bluetooth module, etc., which will not be elaborated here.

[0370] In the above embodiments, the steps performed by the terminal device can be based on the Figure 16 shown terminal device structure.

[0371] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. When it runs on a computer, it causes the computer to execute the methods described in the foregoing embodiments.

[0372] An embodiment of the present application also provides a computer program product including a program. When it runs on a computer, it causes the computer to execute the methods described in the foregoing embodiments.

[0373] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0374] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0375] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0376] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0377] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0378] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for displaying data tables based on business domains, characterized in that, Including: Obtain M metadata from M data tables, where there is a corresponding relationship between the data tables in the M data tables and the metadata in the M metadata, and M is an integer greater than or equal to 2; Determine the association relationship between the M data tables according to the M metadata; Construct a target network according to the association relationship between the M data tables, where the target network includes M data nodes, and there is a corresponding relationship between the data nodes in the M data nodes and the data tables in the M data tables, and each data node is used to store a data table; Perform a partitioning process on the data nodes in the target network to obtain N regions, where N is an integer greater than or equal to 1 and less than or equal to M; Determine at least one business domain according to the N regions, where each business domain includes at least one data table; When an operation for a target viewing interface is obtained, display at least one data table corresponding to the target business domain through the interface of the terminal device, where the target viewing interface is the viewing interface corresponding to the target business domain in the at least one business domain; Among them, the performing a partitioning process on the data nodes in the target network to obtain N regions includes: Determine K edge betweennesses according to the M data nodes and K edges in the target network, where there is a corresponding relationship between the edge betweennesses in the K edge betweennesses and the edges in the K edges; Select a target edge betweenness from the K edge betweennesses, where the target edge betweenness is the maximum value among the K edge betweennesses; Delete the edge corresponding to the target edge betweenness; Until the algorithm termination condition is satisfied, obtain the N regions.

2. The method for displaying a data table according to claim 1, wherein The M data tables at least include a first data table and a second data table; The obtaining M metadata from M data tables includes: Obtain first metadata from the first data table; Obtain second metadata from the second data table; The determining the association relationship between the M data tables according to the M metadata includes: Determine the target association relationship between the first data table and the second data table according to the first metadata and the second metadata, where the target association relationship includes at least one of the connection direction and the connection weight between the first data node and the second data node, the first data node is used to store the first data table, and the second data node is used to store the second data table.

3. The method for displaying a data table according to claim 2, wherein The determining the target association relationship between the first data table and the second data table according to the first metadata and the second metadata includes: Determine the connection direction between the first data node and the second data node according to the technical metadata included in the first metadata and the technical metadata included in the second metadata; Determine the connection weight between the first data node and the second data node according to the business metadata included in the first metadata and the business metadata included in the second metadata.

4. The method for displaying a data table according to claim 3, wherein Determining the edge direction between the first data node and the second data node according to the technical metadata included in the first metadata and the technical metadata included in the second metadata includes: Obtaining the data lineage corresponding to the first data table according to the technical metadata included in the first metadata; Obtaining the data lineage corresponding to the second data table according to the technical metadata included in the second metadata; Determining the upstream data table from the first data table and the second data table according to the data lineage corresponding to the first data table and the data lineage corresponding to the second data table, where the data lineage belongs to the technical metadata; If the upstream data table is the first data table, constructing an edge between the first data node and the second data node; If the upstream data table is the second data table, constructing an edge between the second data node and the first data node.

5. The method for displaying a data table according to claim 3, characterized in that Determining the edge weight between the first data node and the second data node according to the business metadata included in the first metadata and the business metadata included in the second metadata includes: Obtaining the business name corresponding to the first data table according to the business metadata included in the first metadata; Obtaining the business name corresponding to the second data table according to the business metadata included in the second metadata; Determining the correlation degree between the first data table and the second data table according to the business name corresponding to the first data table and the business name corresponding to the second data table; Determining the edge weight between the first data node and the second data node according to the correlation degree between the first data table and the second data table.

6. The data table display method according to claim 3, wherein Determining the edge weight between the first data node and the second data node according to the business metadata included in the first metadata and the business metadata included in the second metadata includes: Obtaining the business description corresponding to the first data table according to the business metadata included in the first metadata; Obtaining the business description corresponding to the second data table according to the business metadata included in the second metadata; Obtaining the correlation degree between the first data table and the second data table through a semantic matching model based on the business description corresponding to the first data table and the business description corresponding to the second data table; Determining the edge weight between the first data node and the second data node according to the correlation degree between the first data table and the second data table.

7. The method for displaying a data table according to claim 1, wherein The partitioning process of the data nodes in the target network to obtain N regions further includes: Obtaining the data node to be partitioned from the target network; Obtaining a first data node and a second data node according to the data node to be partitioned, where both the first data node and the second data node are data nodes adjacent to the data node to be partitioned; Determining a first modularity according to the data node to be partitioned and the first data node; Determine a second modularity according to the data node to be partitioned and the second data node; If both the first modularity and the second modularity are greater than 0, and the first modularity is greater than the second modularity, determine that the data node to be partitioned and the first data node belong to the same region among the N regions; If both the first modularity and the second modularity are greater than 0, and the first modularity is less than the second modularity, determine that the data node to be partitioned and the second data node belong to the same region among the N regions; Until the algorithm termination condition is met, obtain the N regions.

8. The method for displaying a data table according to claim 1, characterized in that The partitioning process of the data nodes in the target network to obtain N regions includes: Obtain a first region and a second region from the target network; Obtain a first gain value according to the first region and the second region, where the first gain value is the difference between the sum of the number of internal edges in the first region and the number of internal edges in the second region, and the number of edges between the first region and the second region; Obtain a first data node in the first region and a second data node in the second region; Add the second data node to the first region to obtain an updated first region, and add the first data node to the second region to obtain an updated second region; Obtain a second gain value according to the updated first region and the updated second region, where the second gain value is the difference between the sum of the number of internal edges in the updated first region and the number of internal edges in the updated second region, and the number of edges between the updated first region and the updated second region; Determine a target gain value according to the first gain value and the second gain value; If the target gain value is the maximum value among P gain values, determine that the first data node belongs to the updated second region and the second data node belongs to the updated first region, where the P gain values include the gain values of pairwise data nodes between the first region and the second region, and P is an integer greater than or equal to 1; Until the algorithm termination condition is met, obtain the N regions.

9. The method for displaying a data table according to claim 1, wherein The determining of at least one business domain according to the N regions includes: Obtain Q data nodes included in the region to be identified from the N regions, where Q is an integer greater than or equal to 1; Determine the business domain corresponding to the region to be identified according to the Q data tables corresponding to the Q data nodes, where there is a corresponding relationship between the data nodes in the Q data nodes and the data tables in the Q data tables.

10. The method for displaying a data table according to claim 1, wherein When an operation on a target viewing interface is obtained, display at least one data table corresponding to the target business domain through the interface of the terminal device, including: Display the business name and viewing interface corresponding to each business domain; When it is detected that an operation is performed on the target viewing interface, display at least one data table corresponding to the target business domain through the interface of the terminal device; Or, When an operation on a target viewing interface is obtained, at least one data table corresponding to a target service domain is displayed through the interface of the terminal device, including: When the terminal device detects an operation on the target viewing interface, receive a viewing instruction sent by the terminal device; Send at least one data table corresponding to the target service domain to the terminal device according to the viewing instruction, so that the terminal device displays at least one data table corresponding to the target service domain.

11. A data table display device, characterized in that, Including: An acquisition module, configured to acquire M metadata from M data tables, where there is a corresponding relationship between the data tables in the M data tables and the metadata in the M metadata, and M is an integer greater than or equal to 2; A determination module, configured to determine the association relationship between the M data tables according to the M metadata; A construction module, configured to construct a target network according to the association relationship between the M data tables, where the target network includes M data nodes, and there is a corresponding relationship between the data nodes in the M data nodes and the data tables in the M data tables, and each data node is used to store a data table; A division module, configured to perform division processing on the data nodes in the target network to obtain N regions, where N is an integer greater than or equal to 1 and less than or equal to M; determine at least one service domain according to the N regions, where each service domain includes at least one data table; A display module, configured to, when an operation on a target viewing interface is obtained, display at least one data table corresponding to a target service domain through the interface of the terminal device, where the target viewing interface is the viewing interface corresponding to the target service domain in the at least one service domain; Wherein, the division module performs division processing on the data nodes in the target network to obtain N regions, specifically for: Determine K edge betweennesses according to the M data nodes and K edges in the target network, where there is a corresponding relationship between the edge betweennesses in the K edge betweennesses and the edges in the K edges; Select a target edge betweenness from the K edge betweennesses, where the target edge betweenness is the maximum value among the K edge betweennesses; Delete the edge corresponding to the target edge betweenness; Until the algorithm termination condition is met, obtain the N regions.

12. The device according to claim 11, characterized in that, The M data tables at least include a first data table and a second data table. The acquisition module is specifically configured to acquire first metadata from the first data table; acquire second metadata from the second data table; The determination module is specifically configured to: Determine a target association relationship between the first data table and the second data table according to the first metadata and the second metadata, where the target association relationship includes at least one of the connection direction and connection weight between a first data node and a second data node, the first data node is used to store the first data table, and the second data node is used to store the second data table.

13. The device according to claim 12, wherein The determination module is specifically configured to: Determine the edge direction between the first data node and the second data node according to the technical metadata included in the first metadata and the technical metadata included in the second metadata; Determine the edge weight between the first data node and the second data node according to the service metadata included in the first metadata and the service metadata included in the second metadata.

14. The device according to claim 13, characterized in that, The determining module is specifically configured to: Obtain the data lineage corresponding to the first data table according to the technical metadata included in the first metadata; Obtain the data lineage corresponding to the second data table according to the technical metadata included in the second metadata; Determine the upstream data table from the first data table and the second data table according to the data lineage corresponding to the first data table and the data lineage corresponding to the second data table, where the data lineage belongs to the technical metadata; If the upstream data table is the first data table, construct an edge from the first data node to the second data node; If the upstream data table is the second data table, construct an edge from the second data node to the first data node.

15. The device according to claim 13, characterized in that, The determining module is specifically configured to: Obtain the service name corresponding to the first data table according to the service metadata included in the first metadata; Obtain the service name corresponding to the second data table according to the service metadata included in the second metadata; Determine the association degree between the first data table and the second data table according to the service name corresponding to the first data table and the service name corresponding to the second data table; Determine the edge weight between the first data node and the second data node according to the association degree between the first data table and the second data table.

16. The device according to claim 13, characterized in that, The determining module is specifically configured to: Obtain the service description corresponding to the first data table according to the service metadata included in the first metadata; Obtain the service description corresponding to the second data table according to the service metadata included in the second metadata; Obtain the association degree between the first data table and the second data table through a semantic matching model based on the service description corresponding to the first data table and the service description corresponding to the second data table; Determine the edge weight between the first data node and the second data node according to the association degree between the first data table and the second data table.

17. The device according to claim 11, characterized in that, The partitioning module performs partitioning processing on the data nodes in the target network to obtain N regions, and is further specifically configured to: Obtain the data node to be partitioned from the target network; Obtain a first data node and a second data node according to the data node to be partitioned, where both the first data node and the second data node are data nodes adjacent to the data node to be partitioned; Determine a first modularity according to the data node to be partitioned and the first data node; Determine a second modularity according to the data node to be partitioned and the second data node; If both the first modularity and the second modularity are greater than 0, and the first modularity is greater than the second modularity, determine that the data node to be partitioned and the first data node belong to the same region among the N regions; If both the first modularity and the second modularity are greater than 0, and the first modularity is less than the second modularity, determine that the data node to be partitioned and the second data node belong to the same region among the N regions; Until the algorithm termination condition is satisfied, obtain the N regions.

18. The device according to claim 11, wherein The partitioning module performs partitioning processing on the data nodes in the target network to obtain N regions, specifically: Obtain a first region and a second region from the target network; According to the first region and the second region, obtain a first gain value, where the first gain value is the difference between the sum of the number of internal edges in the first region and the number of internal edges in the second region, and the number of edges between the first region and the second region; Obtain a first data node in the first region and a second data node in the second region; Add the second data node to the first region to obtain an updated first region, and add the first data node to the second region to obtain an updated second region; According to the updated first region and the updated second region, obtain a second gain value, where the second gain value is the difference between the sum of the number of internal edges in the updated first region and the number of internal edges in the updated second region, and the number of edges between the updated first region and the updated second region; Determine a target gain value according to the first gain value and the second gain value; If the target gain value is the maximum value among P gain values, determine that the first data node belongs to the updated second region, and the second data node belongs to the updated first region, where the P gain values include the gain values of pairwise data nodes between the first region and the second region, and P is an integer greater than or equal to 1; Until the algorithm termination condition is satisfied, obtain the N regions.

19. The device according to claim 11, wherein, The partitioning module is specifically used for: Obtain Q data nodes included in the region to be identified from the N regions, where Q is an integer greater than or equal to 1; Determine the service domain corresponding to the region to be identified according to the Q data tables corresponding to the Q data nodes, where there is a corresponding relationship between the data nodes in the Q data nodes and the data tables in the Q data tables.

20. The device according to claim 11, characterized in that The display module is specifically used for: Display the service name and view interface corresponding to each service domain; When an operation on the target view interface is detected, display at least one data table corresponding to the target service domain through the interface of the terminal device; Or, The display module is specifically used for: When the terminal device detects an operation on the target view interface, receive the view instruction sent by the terminal device; Sending at least one data table corresponding to the target service domain to the terminal device according to the viewing instruction, so that the terminal device displays at least one data table corresponding to the target service domain.

21. A computer device, characterized in that, Including: A memory, a processor, and a bus system; Wherein, the memory is used for storing programs; The processor is used for executing the programs in the memory, and the processor is used for executing the data table display method according to any one of claims 1 to 10 according to the instructions in the program code; The bus system is used for connecting the memory and the processor, so that the memory and the processor communicate with each other.

22. A computer-readable storage medium, including instructions, when running on a computer, enabling the computer to execute the data table display method according to any one of claims 1 to 10.

23. A computer program product, characterized in that, The computer program product includes computer instructions, and the processor of the computer device executes the computer instructions, enabling the computer to execute the data table display method according to any one of claims 1 to 10.

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