Data management method and device based on public cloud technology

Abstract

The invention discloses a data management method and device based on a public cloud technology, and belongs to the technical field of cloud services. The method comprises the following steps: receiving a data governance instruction sent by a tenant; based on the data governance instruction, obtaining a plurality of first entities in a to-be-governed data set and feature information of each first entity; acquiring a plurality of second entities in a reference data set of a specified service field and feature information of each second entity; retrieving the plurality of second entities based on the target first entity to obtain one or more target second entities associated with the target first entity; based on the one or more target second entities and the feature information of each target second entity, executing data governance operation on the target first entity to obtain a data set subjected to data governance; a response is sent to the tenant indicating that the data governance operation has been completed. According to the invention, the accuracy of data management is improved.

Description

Technical Field

[0001] This application relates to the field of cloud service technology, and in particular to a data governance method and apparatus based on public cloud technology. Background Technology

[0002] Data governance refers to the management of data assets, including developing relevant policies, processes, and standards, and applying technical means to ensure the quality, security, compliance, and effective use of data. Data governance helps guide the execution of other data management functions to guarantee data availability, quality, and security. For example, data governance includes standardizing field names in database tables, completing information for database tables, and identifying relationships between tables in the database.

[0003] Currently, the common methods for standardizing data names in database tables are as follows: First, complete the field names (usually Chinese names) by querying field notes or asking the client for the exact meaning of each field; then, break down the Chinese names into appropriate root words; next, translate the root words into English terms that fit the domain and business meanings; finally, concatenate the translated Chinese names into English fields. The methods for completing information in database tables are also typically as follows: complete the field names (usually Chinese names) by querying field notes or asking the client for the exact meaning of each field. The methods for obtaining relationships between data tables in a database are typically as follows: determine the relationships by communicating with the database administrator and development team, consulting database design documents or entity-relationship diagrams (ER diagrams), manually reviewing foreign key constraints, and manually checking the data in the tables.

[0004] However, the accuracy of these data governance methods needs to be improved. Summary of the Invention

[0005] This application provides a data governance method and apparatus based on public cloud technology. This application improves the accuracy of data governance. The technical solution provided by this application is as follows:

[0006] Firstly, this application provides a data governance method based on public cloud technology. This method is applied to a cloud platform. The method includes: receiving a data governance instruction sent by a tenant, the data governance instruction being used to instruct the execution of data governance operations on a dataset to be governed belonging to a specified business domain; based on the data governance instruction, obtaining multiple first entities in the dataset to be governed and characteristic information of each first entity, the characteristic information of the first entity including one or more of the following: the type of the first entity, attributes, or the relationship between the first entity and other first entities, the first entity being used to instruct transactions in the specified business domain, the transactions instructed by the first entity including one or more of the following: people, locations, and business concepts in the specified business domain; obtaining multiple second entities in a reference dataset of the specified business domain and characteristic information of each second entity, the characteristic information of the second entities including one or more of the following Type: The type, attributes, or relationship between the second entity and other second entities of the second entity. The second entity is used to indicate transactions in a specified business domain. The transactions indicated by the second entity include one or more of the following: people, places, and business concepts in the specified business domain; Retrieve multiple second entities based on the target first entity to obtain one or more target second entities that are associated with the target first entity. The target first entity is any one of the multiple first entities, and the target second entity is one of the multiple second entities; Perform data governance operations on the target first entity based on one or more target second entities and the feature information of each target second entity to obtain a data-governed dataset; Send a response to the tenant indicating that the data governance operation has been completed.

[0007] This process involves retrieving multiple second entities from the target first entity in the dataset to be governed, obtaining one or more target second entities that are related to the target first entity. This is equivalent to querying one or more target second entities that are related to the target first entity from among the multiple second entities. After querying the target second entities that are related to the target first entity from among the multiple second entities, data governance operations are performed on the target first entity based on one or more target second entities and the feature information of each target second entity. This is equivalent to performing data governance operations on the target first entity with reference to one or more target second entities that are related to the target first entity and their feature information. Since the target second entities and their feature information are obtained from the reference dataset, and the dataset to be governed and the reference dataset belong to the same specified business domain, the target second entities and their feature information can be considered to have high reference value and higher accuracy. Therefore, performing data governance operations on the target first entity based on the target second entities and their feature information can effectively improve the accuracy of data governance.

[0008] There are various data governance operations. The following examples illustrate how to implement them.

[0009] In the first implementation, the data governance operation includes field standardization of data names in the database tables. Then, based on one or more target second entities and the characteristic information of each target second entity, the data governance operation is performed on the target first entity, including: obtaining a target third entity from among the one or more target second entities that indicates the same thing as the target first entity; and modifying the name of the target first entity to the name of the target third entity if the names of the target first entity and the target third entity are different.

[0010] By standardizing the data names in the database tables, the names of the target first entity and the target third entity can be unified. This standardization of the names of the target first entity ensures the readability, consistency, and ease of understanding of the data.

[0011] In the second implementation, the data governance operation includes the operation of completing information for data tables in the database. Based on one or more target second entities and the feature information of each target second entity, the data governance operation is performed on the target first entity, including: obtaining a target third entity from among the one or more target second entities that indicates the same thing as the target first entity; and, if the target first entity lacks feature information compared to the target third entity, completing the missing feature information for the target first entity based on the feature information of the target third entity.

[0012] By supplementing the information in the data tables of the database, the information recorded in the data tables becomes more comprehensive, enabling a more accurate description and mapping of the primary entity. This helps reduce the difficulty of understanding the business meaning behind the data, improves the business value of the data, and enhances the effectiveness of data-driven decision-making.

[0013] In the third implementation, data governance operations include obtaining the relationships between data tables in the database. The reference dataset includes multiple reference data tables, and the dataset to be governed includes multiple data tables to be governed. Then, based on one or more target second entities and the feature information of each target second entity, data governance operations are performed on the target first entity, including: obtaining target third entities from among the one or more target second entities that indicate the same thing as the target first entity; obtaining multiple target reference data tables related to the target third entity from among the multiple reference data tables, and obtaining the target relationships between the multiple target reference data tables; retrieving multiple data tables to be governed based on the multiple target reference data tables to obtain multiple target data tables to be governed that have relationships with the multiple target reference tables; and configuring the relationships between the multiple target data tables to be governed based on the target relationships between the multiple target reference tables.

[0014] By obtaining the relationships between data tables in the database, connections are established between different data tables in the database, enabling data to be accessed across data tables during database queries, thereby achieving data integration and analysis.

[0015] In one possible implementation, the dataset to be governed includes multiple data tables to be governed. Obtaining multiple first entities and feature information of each first entity from the dataset to be governed includes: extracting metadata of each data table to be governed from the dataset to be governed, wherein the metadata of the data table to be governed is data that describes and defines the data table to be governed; and extracting multiple first entities and feature information of each first entity from the metadata of each data table to be governed.

[0016] In one possible implementation, the target first entity is associated with one or more target second entities, including: one or more target second entities include a target third entity that indicates the same thing as the target first entity, and other target second entities besides the target third entity are related to the target third entity. In this way, when performing data governance on the target first entity based on the target second entities and their characteristic information, the target second entities and their characteristic information can provide more valuable reference information, helping to improve the quality of data governance for the target first entity.

[0017] In one possible implementation, the method further includes: receiving a display instruction sent by a tenant, the display instruction being used to instruct the visualization of the data-governed dataset; based on the display instruction, visualizing the data-governed dataset, the displayed content including one or more of the following: multiple first entities in the data-governed dataset after name modification, the names of the multiple first entities being obtained by modifying the names of multiple second entities in the reference dataset; multiple first entities in the data-governed dataset after supplementing feature information, the feature information of the multiple first entities being obtained by supplementing the feature information of the multiple second entities in the reference dataset; or, multiple data tables configured with relationships in the data-governed dataset, the relationships between the multiple data tables being configured based on the relationships between multiple reference data tables in the reference dataset.

[0018] By visualizing data-governed datasets, users can more intuitively understand them, discover patterns and trends, and support decision-making based on these datasets.

[0019] In one possible implementation, the method further includes: receiving feedback instructions from a tenant regarding a displayed data-governed dataset, the feedback instructions indicating: consent to use the data-governed dataset, instructions to modify the data-governed dataset, or switching the displayed data content; and responding according to the feedback instructions. By receiving feedback instructions from tenants regarding the data-governed dataset and responding to those instructions, tenants are provided with the ability to interact with computing devices based on the data-governed dataset, enabling them to perform specified operations on the data-governed dataset as needed, thereby improving the tenant's user experience.

[0020] In one possible implementation, multiple second entities and the feature information of each second entity are represented by a knowledge graph, which is a graph including multiple vertices and edges between different vertices. The multiple vertices are used to indicate multiple second entities respectively. The information carried by any vertex is used to indicate the type and / or attributes of the second entity indicated by any vertex. The edge between any two vertices is used to indicate the relationship between the second entities indicated by any two vertices respectively.

[0021] Secondly, this application provides a data governance device based on public cloud technology. The device is deployed on a cloud platform. The device includes: an interaction module for receiving data governance instructions sent by a tenant, the data governance instructions instructing the execution of data governance operations on a dataset to be governed belonging to a specified business domain; a processing module for obtaining, based on the data governance instructions, multiple first entities in the dataset to be governed and characteristic information of each first entity, the characteristic information of the first entity including one or more of the following: the type of the first entity, attributes, or the relationship between the first entity and other first entities, the first entity being used to instruct transactions in the specified business domain, the transactions instructed by the first entity including one or more of the following: people, locations, and business concepts in the specified business domain; the processing module is also used to obtain multiple second entities in a reference dataset of the specified business domain and characteristic information of each second entity, the characteristic information of the second entities including one or more of the following The second entity is defined as its type, attributes, or relationship with other second entities. The second entity is used to indicate transactions within a specified business domain. The transactions indicated by the second entity include one or more of the following: people, locations, and business concepts within the specified business domain. The processing module is further configured to retrieve multiple second entities based on the target first entity to obtain one or more target second entities associated with the target first entity. The target first entity is any one of the multiple first entities, and the target second entity is one of the multiple second entities. The processing module is also configured to perform data governance operations on the target first entity based on one or more target second entities and the characteristic information of each target second entity, to obtain a data-governed dataset. The interaction module is further configured to send a response to the tenant indicating that the data governance operation has been completed.

[0022] In one possible implementation, the processing module is specifically used to: obtain a target third entity among the target second entities that indicates the same thing as the target first entity, based on one or more target second entities and the feature information of each target second entity; and modify the name of the target first entity to the name of the target third entity if the name of the target first entity is different from the name of the target third entity.

[0023] In one possible implementation, the processing module is specifically used to: obtain a target third entity among the target second entities that indicates the same thing as the target first entity, based on one or more target second entities and the feature information of each target second entity; and, if the target first entity lacks feature information compared to the target third entity, supplement the target first entity with the missing feature information based on the feature information of the target third entity.

[0024] In one possible implementation, the reference dataset includes multiple reference data tables, and the dataset to be governed includes multiple data tables to be governed. The processing module is specifically used for: obtaining a target third entity from one or more target second entities and the feature information of each target second entity, which indicates the same thing as the target first entity; retrieving multiple target reference data tables related to the target third entity from the multiple reference data tables, and obtaining the target association relationships between the multiple target reference data tables; retrieving multiple data tables to be governed based on the multiple target reference data tables, and obtaining multiple target data tables to be governed that are associated with the multiple target reference tables; and configuring the association relationships between the multiple target data tables to be governed based on the target association relationships between the multiple target reference tables.

[0025] In one possible implementation, the dataset to be governed includes multiple tables to be governed. The processing module is specifically used to: extract metadata for each table to be governed from the dataset, where the metadata describes and defines the table; and extract multiple first entities and characteristic information for each first entity from the metadata of each table to be governed.

[0026] In one possible implementation, the target first entity is associated with one or more target second entities, including: one or more target second entities include a target third entity that indicates the same thing as the target first entity, and other target second entities besides the target third entity are associated with the target third entity.

[0027] In one possible implementation, the interaction module is also used to receive display instructions sent by the tenant, which instruct the visualization of the data-governed dataset.

[0028] Correspondingly, the interaction module is also used to visualize the data-governed dataset based on display commands. The displayed content includes one or more of the following: multiple first entities in the data-governed dataset after name modification, the names of which are obtained by modifying the names of multiple second entities in the reference dataset; multiple first entities in the data-governed dataset after feature information is supplemented, the feature information of which is obtained by supplementing the feature information of multiple second entities in the reference dataset; or, multiple data tables configured with relationships in the data-governed dataset, the relationships between which are configured based on the relationships between multiple reference data tables in the reference dataset.

[0029] In one possible implementation, the interaction module is also used to receive feedback instructions from the tenant regarding the displayed data-governed dataset, the feedback instructions being used to indicate: agreeing to use the data-governed dataset, indicating modifications to the data-governed dataset, or switching the displayed data content.

[0030] Correspondingly, the processing module is also used to respond according to the instructions of the feedback commands.

[0031] In one possible implementation, multiple second entities and the feature information of each second entity are represented by a knowledge graph, which is a graph including multiple vertices and edges between different vertices. The multiple vertices are used to indicate multiple second entities respectively. The information carried by any vertex is used to indicate the type and / or attributes of the second entity indicated by any vertex. The edge between any two vertices is used to indicate the relationship between the second entities indicated by any two vertices respectively.

[0032] Thirdly, this application provides a computing device including a memory and a processor, the memory storing program instructions, and the processor executing the program instructions to perform the methods provided in the first aspect of this application and any possible implementation thereof.

[0033] Fourthly, this application provides a computing device cluster, including multiple computing devices, each computing device including multiple processors and multiple memories, the multiple memories storing program instructions, and the multiple processors executing the program instructions, causing the computing device cluster to perform the methods provided in the first aspect of this application and any possible implementation thereof.

[0034] Fifthly, this application provides a computer-readable storage medium that is a non-volatile computer-readable storage medium, which includes program instructions that, when executed on a computing device, cause the computing device to perform the methods provided in the first aspect of this application and any of its possible implementations.

[0035] Sixthly, this application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods provided in the first aspect of this application and any possible implementation thereof. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the implementation scenario of a data governance method based on public cloud technology provided in this application embodiment;

[0037] Figure 2 This is a schematic diagram of the implementation scenario of another data governance method based on public cloud technology provided in this application embodiment;

[0038] Figure 3 This is a schematic diagram illustrating the deployment of basic resources in a data center, provided in an embodiment of this application.

[0039] Figure 4 This is a flowchart of a data governance method based on public cloud technology provided in an embodiment of this application;

[0040] Figure 5 This is a flowchart illustrating how to obtain multiple first entities and feature information of each first entity in a dataset to be governed, as provided in an embodiment of this application.

[0041] Figure 6 This is a schematic diagram illustrating a process of constructing a knowledge graph based on a reference dataset, as provided in an embodiment of this application.

[0042] Figure 7 This is a schematic diagram of a data governance process provided in an embodiment of this application;

[0043] Figure 8 This is a flowchart illustrating the field standardization process for data names in a database table, as provided in an embodiment of this application.

[0044] Figure 9 This is a flowchart provided in an embodiment of the present application for completing information in a data table in a database;

[0045] Figure 10 This is a flowchart illustrating how to obtain the relationships between data tables in a database, as provided in an embodiment of this application.

[0046] Figure 11 This is a schematic diagram illustrating the process of visualizing a data-governed dataset, as provided in an embodiment of this application.

[0047] Figure 12 This is a visual illustration of a data table completion method provided in an embodiment of this application for an airline's database.

[0048] Figure 13 This is a visual diagram illustrating the configuration of association relationships between data tables in an airline's database, as provided in an embodiment of this application.

[0049] Figure 14 This is a schematic diagram of a data governance device based on public cloud technology provided in an embodiment of this application;

[0050] Figure 15 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application;

[0051] Figure 16 This is a schematic diagram of the structure of a computing device cluster provided in an embodiment of this application;

[0052] Figure 17 This is a schematic diagram of another computing device cluster structure provided in an embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0054] To facilitate understanding, the technologies and background involved in the embodiments of this application will be introduced below.

[0055] A knowledge graph is a structured method of knowledge representation that stores and represents complex information and knowledge in a specific domain (such as medicine, law, finance, etc.) in the form of a graph. It typically uses a graph representation including vertices and edges. Vertices in a knowledge graph represent entities within the domain. Entities in a knowledge graph for a specific business domain are used to indicate transactions within that domain. Transactions indicated by entities include one or more of the following: people, places, and concepts. For example, vertices in a medical knowledge graph represent transactions such as patients, doctors, diseases, and hospitals. Edges in a knowledge graph represent relationships between entities; for example, edges represent relationships such as "belongs to," "located in," and "has." When two vertices represent entities with an association relationship, the two vertices are connected by an edge. When two vertices represent entities without an association relationship, the two vertices are not connected. For example, in a medical knowledge graph, an edge between the vertex representing a patient and the vertex representing a disease indicates that the patient has the disease. In one implementation, the association between one entity and another entity includes one or more of the following: one entity actively associates with another entity, and another entity actively associates with one entity. Both vertices and edges carry information. The information carried by a vertex can include the name (also known as an identifier or label) of the entity it represents and its property information. For example, the vertex representing a patient carries the patient's name. The information carried by an edge can include property information indicating the relationship between the two entities represented by the two vertices connected by the edge. For example, the edge between the vertex representing a patient and the vertex representing a disease carries the information indicating a disease relationship.

[0056] Data governance refers to the management of data assets, including the development of relevant policies, processes, and standards, and the application of technical means to ensure the quality, security, compliance, and effective use of data. Data governance helps guide the execution of other data management functions to ensure data availability, data quality, and data security. The data governance in this application mainly includes standardizing the field names of data tables in the database, completing information for data tables in the database, and obtaining the relationships between data tables in the database.

[0057] Field standardization, also known as data name standardization, is the process of unifying and standardizing fields in a database table during database design and data management. The purpose of this process is to ensure data consistency, accuracy, and maintainability. Field standardization typically includes: naming constraints, data type constraints, format standardization, length and precision constraints, NOT NULL constraints, and uniqueness constraints. In this application, field standardization primarily refers to naming constraints, also known as data name standardization.

[0058] Data naming standardization is a crucial data management process that involves converting column or field names in a dataset into a uniform, standardized format to ensure readability, consistency, and ease of understanding. This process typically includes removing special characters from column names, standardizing case, using underscores or camelCase to separate words, ensuring consistency in abbreviations, ensuring the uniqueness of column names, and providing clear semantic descriptions for column names. CamelCase is a naming convention used in computer programming that allows the use of mixed uppercase and lowercase letters in variable or function names to improve readability. Data naming standardization reduces the difficulty of data integration, analysis, and sharing, minimizes confusion and errors caused by inconsistent naming, increases the automation level of data processing, and supports data governance and compliance. Furthermore, data naming standardization helps improve data discoverability, making it easier for data users to identify and access the data they need, thereby promoting the effective use of data throughout the organization and supporting the need for data-driven decision-making.

[0059] Currently, data naming standardization mainly faces two problems: standardization and metadata complexity. The standardization problem refers to the lack of unified standards for database metadata. This leads to differences in naming, formatting, and descriptions across different data sources, increasing the difficulty of data integration and affecting data consistency and understandability. The metadata complexity problem arises as database metadata becomes increasingly complex with the growth of data volume. This makes data model analysis and understanding complex and time-consuming, necessitating more efficient tools and methods to simplify and abstract metadata.

[0060] In data-enabled solution delivery scenarios, the following method is typically used for database data name standardization. First, by querying field notes or asking the customer for the precise meaning of each field, the field notes (usually Chinese names) are completed. Then, the Chinese names are broken down into appropriate root words; these root words are then translated into English meanings appropriate for the domain, as well as English abbreviations appropriate for the business context; finally, the Chinese field names are concatenated into English fields based on the translations. To improve processing efficiency, the above process is usually automated using tools or scripts, and natural language processing techniques are employed for string processing such as pattern matching, word segmentation, and character cleaning.

[0061] However, the data naming standardization technique has the following drawbacks: When field notes are incomplete, it's necessary to consult with clients about the meaning of each field. This process is very time-consuming and may involve communication with multiple business departments. Furthermore, translating root terms typically requires implementers to have a deep understanding of the business to accurately translate Chinese fields into the relevant English. Additionally, over-reliance on automation tools can lead to insufficient understanding of the context, resulting in inaccuracies when processing domain-specific data, sometimes necessitating manual intervention to ensure accuracy and applicability.

[0062] Completing missing information in a database table refers to supplementing the table with its missing information. Missing information in a data table is usually due to a lack of metadata. The lack of accurate descriptions and mappings of entities in metadata makes it difficult to understand the business meaning behind the data, thus affecting the business value of the data and the effectiveness of data-driven decision-making. Missing information in data tables is also caused by the complexity of metadata. As data volume grows, database metadata becomes increasingly complex, making the analysis and understanding of data models complicated and time-consuming. More efficient tools and methods are needed to simplify and abstract metadata, posing a significant challenge to completing the missing information.

[0063] Currently, the common method for completing information in database tables is to query field comments or ask the customer for the information the table needs, then compare the required information with the information already recorded in the table to identify and complete any missing information. The drawbacks of this method are discussed in the section on data name standardization, and will not be elaborated upon here.

[0064] The relationships between data tables are also called table associations. Table associations refer to the connections established between different data tables in a database. These connections allow the database to access data across tables during queries, enabling data integration and analysis. The main types of table associations are one-to-one, one-to-many, and many-to-many relationships. Databases typically implement these relationships using foreign keys or joins.

[0065] Identifying table relationships through metadata is an advanced data governance approach. It relies on in-depth analysis and understanding of the metadata contained within data tables. The core of this method lies in identifying potential connections between different data tables, whether through explicit foreign key relationships or implicit pattern matching or semantic similarity. In this way, complex networks between data tables can be built, revealing the inherent connections between data, thereby promoting cross-table data integration, enhancing data consistency and accuracy, and providing strong support for data warehouses, data lakes, or any system requiring data integration. Furthermore, obtaining these relationships provides data scientists and analysts with richer perspectives on data exploration, helping them build accurate data models, conduct deeper data analysis and mining, and ultimately achieve data-driven decision-making and business optimization. Table metadata describes and defines the data table. Examples of table metadata include column names, data types, keys, indexes, descriptive text, source information, and business rules.

[0066] Automating the discovery of table relationships presents challenges. In large databases, quickly and accurately identifying relationships between different tables is a challenge, especially without explicit foreign keys or business rules. Furthermore, the complexity of data models makes it difficult for data analysts to quickly grasp the overall structure and relationships, impacting the efficiency and quality of data analysis. Currently, the main methods for obtaining table relationships include: 1. Communicating with database administrators and development teams to understand their understanding of the database structure and relationships; 2. Understanding the relationships between tables by querying database design documents or entity-relationship diagrams (ER diagrams); 3. Manually examining foreign key constraints in the database to determine which tables are related by foreign keys, or using Structured Query Language (SQL) to find foreign key and primary key information, or automatically detecting foreign key relationships by writing automated scripts; 4. Manually examining data in tables, especially fields that appear to match primary or unique keys, to obtain potential relationships. A primary key is a field or combination of fields used to uniquely identify each row of data in a table. The purpose of a primary key is to ensure that each row of data in a table can be uniquely identified and accessed. A foreign key is a field used to establish relationships between data tables. A foreign key is a primary key that links to another data table, thus creating a relationship between the tables. The purpose of a foreign key is to implement the association between data tables, allowing data queries and operations to be performed between the related tables.

[0067] However, techniques for obtaining table relationships have the following drawbacks: When database developers lack sufficient knowledge, have an inadequate overall understanding of the database, or when database knowledge is not effectively communicated, it becomes impossible to fully understand the meaning of each table and field, as well as the relationships between tables, during communication with them. Similarly, if the database design documents or ER diagrams are insufficient or incomplete, it's impossible to directly obtain all the necessary information from the documents. Manually or using scripts to examine primary and foreign key relationships between tables relies on the requirement that primary and foreign key relationships exist between tables. However, in some cases, primary and foreign keys may not be set during database design. For example, in some high-performance read / write scenarios, primary and foreign key constraints may introduce additional checking and maintenance costs, and in high-concurrency, high-volume transaction scenarios, using foreign keys can easily lead to deadlocks; therefore, primary and foreign keys are not set. Manually examining data in tables to obtain potential relationships is extremely labor-intensive and may result in misunderstandings leading to incorrect inferences about table relationships.

[0068] In view of this, embodiments of this application provide a data governance method based on public cloud technology. The method includes: receiving a data governance instruction sent by a tenant; based on the data governance instruction, obtaining multiple first entities and feature information of each first entity in the dataset to be governed indicated by the data governance instruction; obtaining multiple second entities and feature information of each second entity in a reference dataset belonging to a specified business domain to which the dataset to be governed belongs; then, retrieving multiple second entities based on a target first entity to obtain one or more target second entities associated with the target first entity; performing data governance operations on the target first entities based on the one or more target second entities and the feature information of each target second entity to obtain a data-governed dataset; and sending a response to the tenant indicating that the data governance operation has been completed. The data governance instruction is used to instruct the execution of data governance operations on the dataset to be governed belonging to a specified business domain. The feature information of the first entity includes one or more of the following: the type, attributes, or relationship between the first entity and other first entities. The first entity is used to indicate transactions in the specified business domain. The transactions indicated by the first entity include one or more of the following: people, locations, and business concepts in the specified business domain. The feature information of the second entity includes one or more of the following: the type, attributes, or relationship between the second entity and other second entities. The second entity is used to indicate transactions in the specified business domain. The transaction indicated by the second entity includes one or more of the following: people, places, and business concepts in a specified business area. The target first entity is one of a plurality of first entities, and the target second entity is one of a plurality of second entities.

[0069] In the data governance method based on public cloud technology provided in this application, multiple second entities are retrieved based on the target first entity in the dataset to be governed, resulting in one or more target second entities that are related to the target first entity. This is equivalent to querying one or more target second entities that are related to the target first entity from among the multiple second entities. After querying the target second entities that are related to the target first entity from among the multiple second entities, data governance operations are then performed on the target first entity based on one or more target second entities and the feature information of each target second entity. This is equivalent to performing data governance operations on the target first entity with reference to one or more target second entities that are related to the target first entity and their feature information. Since the target second entities and their feature information are obtained from the reference dataset, and the dataset to be governed and the reference dataset belong to the same specified business domain, the target second entities and their feature information can be considered to have high reference value and higher accuracy. Therefore, by performing data governance operations on the target first entity based on the target second entities and their feature information, the accuracy of data governance can be effectively improved.

[0070] This article provides a detailed introduction to the technical solution of this application from multiple perspectives, including implementation scenarios, methods and processes, hardware devices, and software devices.

[0071] The following are examples illustrating the implementation scenarios of the embodiments of this application.

[0072] Figure 1 This is a structural diagram illustrating an implementation scenario of a data governance method based on public cloud technology provided in this application embodiment. For example... Figure 1 As shown, the implementation scenario includes one or more computing devices 10 and a client 20. The one or more computing devices 10 are used to implement the data governance method based on public cloud technology provided in this embodiment. The client 20 is capable of establishing a communication connection with one or more computing devices 10. For example, the client 20 and one or more computing devices 10 can establish a communication connection via a network. Optionally, the network can be a local area network, the Internet, or other networks; this embodiment does not limit the scope of the application.

[0073] Client 20 is used for users to interact with one or more computing devices 20. In one implementation, client 20 is used to send a data governance instruction to computing device 10 according to the user's instructions, to instruct computing device 10 to perform data governance operations on the dataset to be governed. One or more computing devices 20 are used to perform data governance operations on the dataset to be governed based on the data governance instruction, according to the data governance method based on public cloud technology provided in the embodiments of this application, and to feed back the data governance results to client 20.

[0074] In one possible implementation, the client 20 can be a desktop computer, laptop computer, mobile phone, smartphone, tablet computer, multimedia player, smart home appliance, artificial intelligence device, smart wearable device, e-reader, smart in-vehicle device, or IoT device, etc. The computing device 10 can be a server (such as a cloud server). When the implementation scenario includes multiple computing devices 10, these multiple computing devices 10 can be referred to as a computing device cluster. In this case, the computing device cluster is a server cluster composed of several servers, or it can be implemented as a cloud computing service center. The cloud computing service center deploys a large amount of basic resources owned by the cloud service provider. For example, the cloud computing service center deploys computing resources, storage resources, and network resources. The cloud computing service center can utilize these large amounts of basic resources to implement the data governance method based on public cloud technology provided in this application embodiment.

[0075] When a computing device cluster is implemented through a cloud computing service center, the data governance function implemented by the computing device cluster can be abstracted into a data governance cloud service by the cloud service provider on the cloud platform. In this case, users can access the cloud management platform through client 20, purchase the data governance cloud service on the cloud management platform, and then use the data governance cloud service provided by the computing device cluster to perform data governance operations on the dataset specified by the user. Optionally, the cloud management platform can be a central cloud cloud management platform, an edge cloud cloud management platform, or a cloud management platform including both a central cloud and an edge cloud; this embodiment does not specifically limit its scope. Furthermore, the data governance cloud service can optionally be provided by the cloud management platform as a separate cloud service. Alternatively, the data governance cloud service can be provided as an add-on cloud service to other cloud services. For example, the cloud management platform provides the data governance cloud service as an add-on cloud service to the database service, in which case the data governance cloud service is used to perform data governance operations on the database managed by the database service.

[0076] When computing device clusters are implemented through a cloud computing service center, such as Figure 2 As shown in the illustration, the implementation scenario of the data governance method based on public cloud technology provided in this application includes: a data center 1 and a client 20. The data center 1 and the client 20 can establish a communication connection via a network. Optionally, this network can be the Internet or other networks; this application embodiment does not specifically limit its use. Tenants can interact with the data center 1 through the client 20. For example, a tenant can send data governance instructions and other information to the data center 1 through the client 20. The data center 1 responds based on the information sent by the client 20.

[0077] Data center 1 houses a large amount of infrastructure owned by the cloud service provider, such as computing resources, storage resources, and network resources. For example, computing resources could be computing devices 10 (such as servers) capable of providing computing power. In this case, computing device 10 is deployed in data center 1. Figure 2 As shown, data center 1 includes a cloud management platform and infrastructure ( Figure 2 (Not shown in the image). The cloud management platform and the infrastructure are connected via an internal data center network. The cloud management platform is used to manage the infrastructure. The infrastructure is used to provide public cloud services, such as data governance cloud services. The infrastructure includes multiple servers. Cloud services are optionally deployed on the servers. Cloud services are implemented by running virtual instances, and are therefore also referred to as virtual instances deployed on servers to implement tenant business. Tenants can send cloud service requests and related information to the server through their client 20. The server can process the cloud service requests and related information and provide cloud services to the tenant based on the processed cloud service requests and related information. For example, the server can implement data governance of the dataset using the public cloud technology-based data governance method provided in the embodiments of this application.

[0078] The cloud management platform can be logically divided into: tenant console, compute management service, network management service, storage management service, authentication service, and image management service. The tenant console provides a user interface or application programming interface (API) for interaction with tenants. The compute management service manages servers running virtual instances and bare metal servers. The network management service manages network services (such as gateways and firewalls). The storage management service manages storage services (such as data bucket services). The authentication service manages tenant accounts and passwords. The image management service manages virtual instance images.

[0079] exist Figure 2 In the illustrated implementation scenario, a data center contains multiple servers. The servers consist of a hardware layer and a software layer. The hardware layer comprises the standard server configuration, including processors, memory, network interface cards (NICs), disks, and buses. The software layer includes the operating system installed and running on the server. This operating system, relative to the virtual machine, can be called the host operating system. The host operating system runs a virtual machine manager (also known as a hypervisor). The hypervisor's role is to implement compute virtualization, network virtualization, and storage virtualization, and to manage the virtual machines.

[0080] The virtual machine manager runs a cloud management platform client. This client receives control plane commands from the cloud management platform, creates virtual instances on the server based on these commands, and manages the virtual instances throughout their lifecycle. For example, the client can monitor the hardware resource usage of the server in real time and report it to the cloud management platform. When the cloud management platform confirms that a virtual instance needs to be created on a specific server, it sends a virtual instance creation command to the client on that server. Upon receiving the command, the client creates the virtual instance on that server. In this way, tenants can create, manage, log in to, and operate virtual instances within the data center through the cloud management platform.

[0081] Servers can run virtual machines of different specifications. Virtual machine specifications are categorized as: general-purpose computing, memory-optimized, ultra-large memory, etc., with specific specifications under each type. After a tenant selects a virtual machine specification, the cloud management platform selects a server in the data center that supports that specification and ensures sufficient idle hardware resources on that server. Then, it creates and configures the virtual machine with that specification on that server. Configuring servers through the cloud management platform allows for the analysis and planning of server hardware resources. Based on the server's hardware performance, it plans the corresponding computing products for the physical hardware, such as planning virtual machines of different specifications, to meet the diverse needs of different tenants. Furthermore, differentiated pricing strategies can be implemented based on the performance differences of virtual machines of different specifications. For example, high-performance virtual instances can be sold at a higher price, while ordinary performance virtual instances can be sold at a lower price, allowing tenants to purchase virtual instances as needed.

[0082] In one implementation, such as Figure 3As shown, the location of basic resources in a data center can be described using cloud resource deployment regions (regions) and availability zones (AZs). Tenants can choose to deploy cloud services based on resources within a specific region or AZ. A region is defined by geographical location and network latency. Using the same resource pool within the same region can be understood as sharing common services such as elastic computing, block storage, object storage, virtual private cloud (VPC) networks, elastic internet protocol (EIP) addresses, and images. Regions are divided into general-purpose regions and dedicated regions. A general-purpose region refers to a region that provides general cloud services to public tenants. A dedicated region refers to a region that hosts the same type of business or provides business services to specific tenants. A region typically includes multiple AZs. Multiple AZs within a region are connected via high-speed fiber optic cables to meet the needs of tenants building high-availability systems across AZs. An AZ is one or more... Figure 3 The data center shown is a collection of data centers. Within an Availability Zone (AZ), computing, networking, and storage resources are logically divided into multiple clusters.

[0083] Tenants can send instructions to the cloud management platform through their client 20 to create, manage, log in to, and operate virtual instances on the server, and use the cloud services provided by these virtual instances. For example, the cloud management platform can provide an access interface. This interface can be provided either as a user interface or an API. Tenants can operate their client to remotely access the access interface to register a cloud account and password on the cloud management platform, and then log in using these accounts and passwords. The cloud management platform can also authenticate the cloud account and password. After successful authentication, the tenant can further select and purchase virtual instances of specific specifications (processor, memory, disk) on the cloud management platform. After the tenant successfully purchases a virtual instance, the cloud management platform provides the tenant with a remote login account and password for the purchased virtual instance. The tenant can use the remote login account and password to remotely log in to the virtual instance on their client, install and run their application within the virtual instance, and use the application to implement their business operations.

[0084] In one implementation, the data governance method based on public cloud technology provided in this application embodiment can be implemented by running an executable program on one or more computing devices 10. For example, the executable program for the data governance method based on public cloud technology may be presented in the form of an application installation package. After the application installation package is installed on one or more computing devices 10, the data governance method based on public cloud technology can be implemented by running the executable program. When the computing device cluster is implemented through a cloud computing service center, the data governance method based on public cloud technology provided in this application embodiment can be implemented by running an executable program on computing devices in data center 1.

[0085] This application is applicable to various scenarios requiring data governance. For example, it can be used in the delivery of data-enabled solutions. During the delivery of such solutions, it is typically necessary to analyze the current state of the user's database and perform data governance based on the user's needs. This data governance process involves integrating data tables in the database and migrating them to a cloud-based data lake or data warehouse. Before migration, the relationships between data tables need to be clarified, and the tables to be migrated to the cloud must be selected. Furthermore, to enhance the readability of the data model, ensure data quality, and promote the implementation of data governance policies, field standardization is required during data migration. Fields that are difficult to read, non-standardized, or inconsistent need to be standardized to ensure all field names follow the same pattern. Additionally, information needs to be supplemented to improve the usability of the data tables in the database. Therefore, this application can be applied to the data governance process in this scenario.

[0086] It should be understood that the above content is an exemplary description of the implementation scenarios of the data governance method based on public cloud technology provided in the embodiments of this application, and does not constitute a limitation on the implementation scenarios of the data governance method based on public cloud technology. Those skilled in the art will know that, as business needs change, the implementation scenarios can be adjusted according to application requirements, and the embodiments of this application do not specifically limit them. Furthermore, when the data governance method based on public cloud technology provided in the embodiments of this application is applied to other scenarios, the executable program of the method can also be presented in the form of an application installation package or in other ways, and the embodiments of this application do not list them all.

[0087] The following describes the implementation process of the data governance method based on public cloud technology provided in the embodiments of this application, applied to a computing device managed by a cloud management platform. Figure 4 This is a flowchart illustrating a data governance method based on public cloud technology provided in an embodiment of this application. For example... Figure 4As shown, this data governance method based on public cloud technology includes the following steps:

[0088] Step 401: Receive data governance instructions sent by the tenant. Data governance instructions are used to instruct the execution of data governance operations on the dataset to be governed that belongs to the specified business domain.

[0089] When a tenant needs to use the data governance cloud service provided by the cloud management platform, they can execute a specified operation on their client to trigger a data governance instruction. This instruction then allows the cloud management platform to perform data governance operations on the tenant's specified dataset. It should be noted that the data governance operation on the dataset can be performed by computing devices managed by the cloud management platform. Therefore, after obtaining the data governance instruction, the cloud management platform can select the appropriate computing device from among its managed devices to perform the data governance operation based on that instruction and forward the instruction to the selected device.

[0090] To facilitate the cloud management platform in identifying the objects to be subject to data governance operations, the data governance instruction needs to specify the dataset to be governed. For example, the data governance instruction may carry identification information for the dataset. Optionally, the data governance instruction may also indicate the technical field to which the dataset belongs, i.e., specify the designated business domain to which the dataset belongs. Alternatively, when the data governance instruction does not specify the designated business domain to which the dataset belongs, the computing device may determine the designated business domain to which the dataset belongs based on the content of the data recorded in the dataset. For example, when the content of the data recorded in the dataset is mostly related to a certain business domain, the computing device determines that the dataset belongs to that business domain.

[0091] In one possible implementation, the cloud management platform can provide an interaction interface to tenants, allowing tenants to trigger data governance instructions based on this interface. After a tenant triggers a data governance instruction, the cloud management platform can obtain the instruction through the interaction interface. Optionally, the interaction interface can include one or more of the following implementations: an application programming interface (API) and a user interface (UI). When the interaction interface is implemented through a user interface, the tenant can operate within the user interface to instruct on the functions they need to perform. The tenant's actions can trigger the user interface to send corresponding instructions to the cloud management platform. For example, if a tenant selects a dataset to be governed in the user interface and instructs the user to perform data governance operations on that dataset, the tenant's action will trigger the user interface to send data governance instructions to the cloud management platform. The cloud management platform can then obtain the instructions triggered by the tenant's actions from the user interface and determine the functions the tenant needs to perform based on those instructions.

[0092] Step 402: Based on the data governance instructions, obtain multiple first entities in the dataset to be governed and the feature information of each first entity. The feature information of the first entity includes one or more of the following: the type of the first entity, its attributes, or the relationship between the first entity and other first entities. The first entity is used to indicate transactions in a specified business domain. The transactions indicated by the first entity include one or more of the following: people, places, and business concepts in the specified business domain.

[0093] After receiving a data governance instruction, the computing device can determine the dataset to be governed based on the instruction's guidance. Once the computing device obtains the dataset, it can acquire multiple first entities and their feature information. In one possible implementation, the computing device uses a machine learning model, such as a deep learning model, to extract multiple first entities and their feature information from the dataset. Optionally, this machine learning model can be an information extraction model built based on a bidirectional encoder representations from transformers (BERT) model. The BERT model has powerful language understanding capabilities, which enable the information extraction model to extract multiple first entities and their feature information from the dataset.

[0094] Multiple first entities within a dataset to be governed, belonging to a specified business domain, are used to indicate transactions within that domain. A transaction includes one or more of the following: people, locations, and business concepts within the specified business domain. The characteristic information of a first entity includes one or more of the following: the type of the first entity, its attributes, or the relationship between the first entity and other first entities. For example, assuming the specified business domain is the medical domain, first entities are used to indicate transactions within the medical domain. When a transaction indicates a person, it might indicate a patient, doctor, nurse, or patient's family member. When a transaction indicates a location, it might indicate a hospital, ward, consultation room, treatment room, operating room, pharmacy, or hospital cafeteria. When a first entity indicates a patient, its type is "patient," and its attributes include the patient's age, gender, and medical history. When a first entity indicates a doctor, its type is "therapist," and its attributes include the doctor's age, gender, medical department, and areas of expertise. The relationship between the first entity used to indicate a patient and the first entity used to indicate a doctor is that of a patient and a therapist. When a transaction refers to a business concept in the medical field, it might refer to diseases, drugs, treatments, or medical devices. For example, assuming the designated business field is aviation, the first entity is used to refer to aviation-related transactions. When a transaction refers to a person, it might refer to pilots, flight attendants, police officers, passengers, aircraft maintenance personnel, or flight management personnel. When a transaction refers to a location, it might refer to the departure point, arrival point, and location of a flight. When a transaction refers to a business concept in the aviation field, it might refer to passenger information, departure time, arrival time, delay rate, gate information, and airport runway information. When the first entity refers to a flight attendant, its type is a service provider during the flight, and its attributes include the flight attendant's age, gender, employee number, and customer satisfaction rating. When the first entity refers to a passenger, its type is a recipient of service during the flight, and its attributes include the passenger's age and gender. The relationship between the first entity referring to a flight attendant and the first entity referring to a passenger is that of a service provider and a recipient of service.

[0095] It should be noted that when a dataset includes multiple tables, retrieving entity and feature information from the dataset typically involves retrieving the entity and feature information from the table's metadata. For example, ... Figure 5 As shown, when the dataset to be governed includes multiple tables, the system obtains multiple first entities and the feature information of each first entity in the dataset to be governed, including:

[0096] Step 4021: Extract the metadata of each data table to be governed from the dataset to be governed. The metadata of the data table to be governed is the data that describes and defines the data table to be governed.

[0097] In one possible implementation, the computing device can run a data probing script for each table in the dataset to be governed, to extract metadata such as table data and field data from the tables. The metadata of the tables to be governed describes and defines the tables. For example, the metadata of a table includes column names, data types, keys, indexes, descriptive text, source information, and business rules. The data probing script is used to extract information with specified characteristics, which here refer to the characteristics of the metadata of the tables to be governed. It should be noted that this example of using a data probing script to extract metadata from tables to be governed is just one implementation method; other methods can also be used to extract metadata from tables to be governed, but these will not be listed here.

[0098] Step 4022: Extract multiple first entities and the feature information of each first entity from the metadata of each data table to be governed.

[0099] like Figure 7 As shown, after the computing device obtains the metadata of the data tables to be governed in the dataset to be governed, it can extract multiple first entities and the feature information of each first entity from the metadata. In one possible implementation, the computing device uses machine learning models such as deep learning models to extract multiple first entities and the feature information of each first entity from the metadata. For example, an information extraction model based on the BERT model can be used to extract multiple first entities and the feature information of each first entity from the metadata of each data table to be governed.

[0100] Step 403: Obtain multiple second entities and the feature information of each second entity from the reference dataset of the specified business domain. The feature information of the second entity includes one or more of the following: the type of the second entity, its attributes, or the relationship between the second entity and other second entities. The second entity is used to indicate transactions in the specified business domain. The transactions indicated by the second entity include one or more of the following: people, places, and business concepts in the specified business domain.

[0101] The computing device typically performs data governance operations on the dataset to be governed based on the tenant's data governance instructions. When sending data governance instructions, the tenant usually also specifies a reference dataset that needs to be referenced when performing data governance operations on the dataset to be governed. In this case, the computing device can determine the reference dataset based on the data governance instructions. Alternatively, if the user does not specify a reference dataset, the computing device automatically determines the reference dataset based on the dataset to be governed. For example, the computing device sets up a reference dataset for each business domain. After determining the dataset to be governed and its corresponding specified business domain, the computing device can use the reference dataset set for that specified business domain as the reference dataset for data governance of the dataset to be governed. After obtaining the reference dataset, the computing device can obtain multiple second entities in the reference dataset and the feature information of each second entity. The definitions of the second entities and their feature information are similar to the description of the first entity and its feature information in step 402, and will not be repeated here.

[0102] In one possible implementation, the computing device uses a machine learning model, such as a deep learning model, to extract multiple second entities and feature information of each second entity from a reference dataset. Optionally, this machine learning model can be an information extraction model built based on the BERT model. The BERT model has powerful language understanding capabilities, which enable the information extraction model to extract multiple second entities and feature information of each second entity from the reference dataset. Alternatively, the multiple second entities and feature information of each entity in the reference dataset can be pre-obtained by the computing device. When performing step 403, the computing device only needs to read the pre-obtained feature information of the multiple second entities and each entity in the reference dataset.

[0103] The reference dataset can be pre-constructed by a computing device, and after obtaining the reference dataset, multiple second entities and the characteristic information of each second entity are determined. In one possible implementation, the computing device collects relevant data in a specified business domain, such as structured data in the specified business domain. The collected data is then provided to experts in the specified business domain, who, based on the characteristics of the specified business domain, define second entities and their characteristic information within the collected data, thereby obtaining multiple second entities and their characteristic information in the reference dataset. For example, assuming the collected data is medical data from the medical field, the computing device provides the collected medical data to experts in the medical field, including doctors, medical researchers, and medical information experts. These experts then define entities, attributes, and relationships between different entities in the collected medical data based on the characteristics of the medical field. For example, patients, diseases, drugs, and treatment methods are defined as entities; the patient's age, gender, and disease severity are defined as attributes of the entities; and the relationship between patients and diseases, and between drugs and treatment methods, are defined as relationships between different entities. After obtaining multiple second entities and their feature information from the reference dataset, these can be stored in a specified database (such as Neo4j) to create a knowledge base for data governance of the dataset. The knowledge base obtained based on the expert-defined first entities and their feature information is also called an ontology.

[0104] In another possible implementation, after collecting relevant data from a specified business domain, the computing device uses an information extraction model to extract multiple second entities and their feature information from the collected data, thereby obtaining multiple second entities and their feature information for a reference dataset. For example, assuming the collected data is from the medical field, the computing device inputs the collected medical data into an information extraction model built on the BERT model. Leveraging its powerful language understanding capabilities, it extracts entities, attributes, and relationships from the collected medical data. After obtaining the multiple second entities and their feature information for the reference dataset, a specified database (such as Neo4j) can be used to store them, resulting in a knowledge base for data governance of the dataset.

[0105] It should be noted that other implementation methods can also be used to obtain multiple second entities and their feature information from the reference dataset; the two implementation methods mentioned above are merely examples. Furthermore, this application can use multiple acquisition methods to obtain multiple second entities and their feature information from the reference dataset. When the second entities and their feature information are obtained through multiple methods, the multiple second entities and their feature information in the reference dataset can also be obtained by data fusion of the second entities and their feature information obtained through multiple methods. For example, after a computing device obtains multiple second entities and their feature information through medical experts and an information extraction model, it can also perform knowledge fusion on the second entities and their feature information defined by the medical experts and the second entities and their feature information output by the information extraction model to form a unified knowledge base, which is then stored in a specified database (such as the Neo4j database). Knowledge fusion involves identifying entities and their feature information that indicate the same transaction in different datasets, and unifying the description methods when the related descriptions of entities indicating the same transaction have the same meaning but are described in different ways. For example, unifying the names of entities indicating the same transaction when their names are different is also called entity disambiguation. For example, when the descriptions of the same characteristic of entities that refer to the same transaction are different, the descriptions are unified. This process is also called reference resolution or coreference resolution.

[0106] The data collected in the reference dataset can be selected from structured data, semi-structured data, and unstructured data in the medical field; this application does not specifically limit the types of data. Structured data refers to data that can be represented and stored using a relational database and is presented in a two-dimensional form. Its characteristics are: data is organized by rows, with each row representing the information of one entity, and all rows having the same attributes. When the reference dataset belongs to the medical field, the structured data used in this application may be, for example, electronic health records (EHRs) or open-source datasets. Semi-structured data is a data form between structured and unstructured data. It does not conform to the data model structure associated with relational databases or other data tables, but it contains relevant tags used to separate semantic elements and to hierarchically organize records and fields. Therefore, semi-structured data is also called self-describing structure. In semi-structured data, entities belonging to the same category can have different attributes, and even when they are combined, the order of these attributes is not important. When the reference dataset belongs to the medical field, the semi-structured data used in this application may be, for example, data from medical reports and medical information websites. Unstructured data refers to data with irregular or incomplete structures, lacking a predefined data model, and inconvenient to represent using two-dimensional logical tables in a database. Various documents, images, videos / audio, etc., all fall under the category of unstructured data. This type of data is generally stored directly as a whole, typically in binary format. When the reference dataset pertains to the medical field, the unstructured data used in this application includes, for example, medical literature and medical books.

[0107] In this application, multiple second entities and the feature information of each second entity may be represented using a knowledge graph. A knowledge graph is a graph including multiple vertices and edges between different vertices. Multiple vertices are used to indicate multiple second entities. Information carried by any vertex indicates the type and / or attributes of the second entity indicated by that vertex. An edge between any two vertices indicates the relationship between the second entities indicated by those two vertices. The information carried by the edge between any two vertices may include attribute information representing the relationship between the two entities represented by the two vertices connected by the edge. In this case, the implementation process of step 403 is actually the process of constructing a knowledge graph based on a reference dataset. Figure 6As shown, after obtaining the ontology library through expert definition and extracting information from structured, semi-structured, and unstructured data, knowledge fusion can be performed on the ontology library and the second entities and their feature information extracted from the structured, semi-structured, and unstructured data. Then, ontology extraction is performed based on the knowledge fusion result, and the extraction result is quality-assessed before a knowledge graph is constructed. Furthermore, after obtaining the knowledge graph, knowledge reasoning can be performed based on the existing knowledge graph. After obtaining the knowledge reasoning result, the quality of the knowledge reasoning result can be assessed, and the knowledge graph can be optimized based on the quality-assessed knowledge reasoning result. Quality assessment and knowledge reasoning are used to improve the quality of the obtained knowledge graph, and other operations that can improve the quality of the knowledge graph can also be performed during the construction process; this application embodiment does not specifically limit these operations. In addition, before the computing device constructs a knowledge graph based on multiple second entities and the feature information of each second entity, it needs to select a suitable knowledge representation model and use the selected knowledge representation model as the basic architecture of the knowledge graph. Among them, knowledge representation models include resource description frameworks (RDF), Web ontology web language (OWL), or attribute graph models.

[0108] Step 404: Based on the target first entity, search for multiple second entities to obtain one or more target second entities that are related to the target first entity. The target first entity is any one of the multiple first entities, and the target second entity is one of the multiple second entities.

[0109] After obtaining multiple first entities and feature information of each first entity in the dataset to be governed, and multiple second entities and feature information of each second entity in the reference dataset, the computing device searches the multiple second entities based on the target first entity to obtain one or more target second entities that are related to the target first entity. This process is equivalent to the computing device traversing each first entity in all first entities of the dataset to be governed, and the traversal of the target first entity involves searching for that target first entity in all second entities of the reference dataset to obtain one or more target second entities that are related to the target first entity. Obtaining one or more target second entities that are related to the target first entity is for the purpose of performing data governance operations on the target first entity based on these one or more target second entities. Figure 7As shown, after obtaining multiple first entities and their feature information of the dataset to be governed, and multiple second entities and their feature information of the reference dataset, it is necessary to obtain the relationship between the first entities and the second entities so as to perform data governance on the first entities based on the second entities that are related to the first entities.

[0110] The second target entity and the first target entity are related, which can be that the transaction indicated by the second target entity and the transaction indicated by the second target entity are related. Optionally, the second target entity and the first target entity indicate the same transaction. For example, when the first target entity is named "Potato" and the second entity is named "Vegetable," it is common sense that they indicate the same transaction, so the second entity named "Vegetable" is considered to be a related target entity to the first target entity named "Potato." Alternatively, the transaction indicated by the second target entity and the transaction indicated by the first target entity are not the same transaction, but the transactions indicated by them have a designated relationship. For example, when the first target entity is named "Tomato" and the second entity is named "Cherry Tomato," it is common sense that they do not indicate the same transaction, but they have many similar characteristics, so the second entity named "Cherry Tomato" is considered to be a related target entity to the first target entity named "Tomato." In this way, when performing data governance on the first target entity based on the second target entity and its characteristic information, the second target entity and its characteristic information can provide more valuable reference information, which helps to improve the governance quality of the data governance of the first target entity.

[0111] For example, one or more target second entities are associated with a target first entity, including: one or more target second entities containing a target third entity that indicates the same thing as the target first entity, and other target second entities besides the target third entity being related to the target third entity. In this case, since the target third entity and the target first entity indicate the same thing, the target third entity can be directly used to perform data governance on the target first entity. Furthermore, since other target second entities besides the target third entity are all related to the target third entity, these other target second entities can enrich and refine the characteristic information of the target third entity, providing some auxiliary assistance for data governance of the target first entity. In this way, the target second entities and the target first entity have a strong correlation, and the other target second entities besides the target third entity can provide auxiliary assistance to the first entity and its characteristic information, making it easier to provide greater reference value for the first entity and its characteristic information, and helping to further improve the governance quality of the dataset to be governed.

[0112] In one possible implementation, during the retrieval of multiple second entities based on a target first entity, the computing device can obtain first content represented by the target first entity and second content represented by multiple second entities. Then, it determines whether the second content represented by each second entity is related to the first content represented by the target first entity. When the second content represented by any second entity is related to the first content represented by the target first entity, that second entity is determined to be a target second entity with a relationship to the target first entity. Here, the relationship between the second content and the first content includes one or more of the following: the second content and the first content are the same content; the second content and the first content have a high degree of similarity; or the second content can make a significant contribution to inferring the first content.

[0113] Step 405: Based on one or more target second entities and the feature information of each target second entity, perform data governance operations on the target first entity to obtain a data-governed dataset, and send a response to the tenant indicating that the data governance operation has been completed.

[0114] After obtaining one or more target second entities that are associated with the target first entity, the computing device can analyze the target first entity based on the one or more target second entities and the feature information of each target second entity, and perform data governance operations on the target first entity based on the analysis results, thereby obtaining a data-governed dataset. There are various data governance operations; the following examples illustrate their implementation.

[0115] In the first implementation, data governance operations include standardizing the field names of data tables in the database. For example... Figure 8 As shown, based on one or more target second entities and the feature information of each target second entity, data governance operations are performed on the target first entity, including:

[0116] Step 405a1: Based on one or more target second entities and the feature information of each target second entity, obtain a target third entity among the one or more target second entities that indicates the same thing as the target first entity.

[0117] When performing step 405a1, the computing device can acquire the first content represented by the target first entity and acquire the second content represented by one or more target second entities. It then determines whether the second content represented by each target second entity is the same as the first content represented by the target first entity. When the second content represented by a target second entity is the same as the first content represented by the target first entity, it is determined that the target second entity and the target first entity indicate the same thing, and thus the target second entity is the target third entity corresponding to the target first entity. For example, if the target first entity represents a potato and the target second entity represents a potato, common sense tells us that they indicate the same content, i.e., they both indicate the same thing. Therefore, the target second entity is the target third entity that indicates the same thing as the target first entity.

[0118] Step 405a2: If the name of the first target entity is different from the name of the third target entity, change the name of the first target entity to the name of the third target entity.

[0119] Since the target third entity and the target first entity refer to the same transaction, when standardizing the data name of the target first entity, if the name of the target first entity is different from the name of the target third entity, the name of the target first entity can be modified to match the name of the target third entity. For example, if the target first entity represents "potato" and the target third entity represents "potato," and both essentially refer to the same thing but have different names, the name of the target first entity will be modified to "potato," thus aligning it with the name of the target third entity.

[0120] By standardizing the data names in the database tables, the names of the target first entity and the target third entity can be unified. This standardization of the names of the target first entity ensures the readability, consistency, and ease of understanding of the data.

[0121] In the second implementation, data governance operations include completing information for data tables in the database. For example... Figure 9 As shown, based on one or more target second entities and the feature information of each target second entity, data governance operations are performed on the target first entity, including:

[0122] Step 405b1: Based on one or more target second entities and the feature information of each target second entity, obtain a target third entity among the one or more target second entities that indicates the same thing as the target first entity.

[0123] Please refer to the relevant description in step 405a1 for the implementation process of step 405b1; it will not be repeated here.

[0124] Step 405b2: If the target first entity lacks feature information compared to the target third entity, supplement the target first entity with the missing feature information based on the feature information of the target third entity.

[0125] Since the target third entity and the target first entity refer to the same transaction, when completing the information in the data table containing the target first entity, it is necessary to compare the feature information possessed by the target third entity with that possessed by the target first entity. If the comparison result indicates that the target first entity lacks feature information compared to the target third entity, then the missing feature information of the target first entity is completed based on the feature information of the target third entity. Specifically, for a certain feature information, if the target third entity possesses that feature information, but the target first entity does not possess that feature information, then it is determined that the target first entity lacks that feature information compared to the target third entity.

[0126] For example, suppose Table 1 contains the feature information of the target third entity, indicating "potato," and Table 2 contains the feature information of the target first entity, indicating "sweet potato." Comparing the feature information in Table 1 and Table 2, we can find that the feature information in Table 2 lacks medicinal value and special uses compared to that in Table 1. Therefore, based on the feature information in Table 1, we can complete the missing feature information for the target first entity: Medicinal value: stomach-strengthening, detoxifying, swelling-reducing, and cancer-preventing; Special uses: raw material for extracting solanine, and skin care.

[0127] Table 1

[0128] feature feature Chinese name Potato soil Sandy loam soil alternative name Potatoes, steamed buns with eggs, etc. Fruit shape Tubers are round, oval, or oblong. Ecological habits Terrestrial plants Nutritional value Juice, simmer, stir-fry, boil or steam, or make into a cold dish. sunshine Like light Industrial value The main raw materials of industrial starch Moisture Fear of drought and fear of flood Medicinal value It strengthens the stomach, detoxifies, reduces swelling, and prevents cancer. temperature Not resistant to high temperatures Special Purpose Raw materials for extracting solanine, used for skin care.

[0129] Table 2

[0130]

[0131]

[0132] By supplementing the information in the data tables of the database, the information recorded in the data tables becomes more comprehensive, enabling a more accurate description and mapping of the primary entity. This helps reduce the difficulty of understanding the business meaning behind the data, improves the business value of the data, and enhances the effectiveness of data-driven decision-making.

[0133] In the third implementation, data governance operations include retrieving the relationships between data tables in the database. For example... Figure 10 As shown, the reference dataset includes multiple reference data tables, and the dataset to be governed includes multiple data tables to be governed. Based on one or more target second entities and the feature information of each target second entity, data governance operations are performed on the target first entity, including:

[0134] Step 405c1: Based on one or more target second entities and the feature information of each target second entity, obtain a target third entity among the one or more target second entities that indicates the same thing as the target first entity.

[0135] Please refer to the relevant description in step 405a1 for the implementation process of step 405c1; it will not be repeated here.

[0136] Step 405c2: In multiple reference data tables, obtain multiple target reference data tables related to the target third entity, and obtain the target association relationship between the multiple target reference data tables.

[0137] After determining a target third entity that indicates the same thing as the target first entity, the computing device needs to obtain multiple target reference data tables related to the target third entity. For example, the target reference data tables related to the target third entity may be data tables containing relevant content about the target third entity. Then, the computing device obtains the target association relationships between the multiple target reference data tables. Since multiple target reference data tables are all related to the target third entity, and the target third entity and the target first entity indicate the same thing, it is necessary to obtain the target association relationships between the multiple target reference data tables. These target association relationships include: whether different target reference data tables are related, and, when different target reference data tables are related, the specific manifestation of that relationship. In one possible implementation, the computing device determines whether the content represented by different target reference data tables is related to each other based on the content represented by the multiple target reference data tables. If the content represented by two target reference data tables is related to each other, it is determined that these two target reference data tables are related. If the content represented by two target reference data tables is not related to each other, it is determined that these two target reference data tables are not related. When there is a relationship between the two target reference data tables, the specific nature of the relationship is analyzed based on the content represented by these two target reference data tables. For example, there may be a primary key / foreign key relationship between the two target reference data tables, or the target reference data table may be accessed via the primary key and the target reference data table may be accessed via the foreign key.

[0138] Step 405c3: Based on multiple target reference data tables, retrieve multiple data tables to be governed to obtain multiple target data tables to be governed that are related to the multiple target reference data tables.

[0139] After acquiring multiple target reference data tables related to the target third entity, the computing device needs to retrieve multiple data tables to be governed based on these target reference data tables to obtain multiple target data tables to be governed that are associated with the target reference data tables. In one possible implementation, the computing device acquires the content represented by the multiple target reference data tables and the content represented by the multiple data tables to be governed. Then, it compares the content represented by each target reference data table with the content represented by the multiple data tables to be governed. When the similarity between the content represented by a target reference data table and the content represented by a data table to be governed is greater than a specified threshold, the data table to be governed is determined to be a target data table to be governed that is associated with the target reference data table. By performing this process on all data tables to be governed in the dataset to be governed according to this principle, multiple target data tables to be governed that are associated with the multiple target reference data tables can be obtained. The value of the specified threshold can be determined according to application requirements, and this embodiment does not specifically limit it.

[0140] Step 405c4: Based on the target association relationship between multiple target reference data tables, configure the association relationship between multiple target data tables to be governed.

[0141] After obtaining multiple target data tables to be governed that are associated with multiple target reference data tables, the computing device needs to configure the association relationships between these target data tables based on these association relationships. In one possible implementation, the computing device first obtains multiple pairs of related target reference data tables based on the target association relationships between them. Then, for any pair of target reference data tables, based on the association relationships between the target reference data tables and the target data tables to be governed, it obtains the corresponding target data tables to be governed that are associated with each target reference data table in that pair. Finally, according to the association relationships between the target reference data tables in that pair, the corresponding target data tables to be governed are configured to also have those association relationships, thereby configuring the association relationships between the multiple target data tables to be governed.

[0142] For example, multiple target reference data tables include: Reference Data Table 1, Reference Data Table 2, and Reference Data Table 3. The target relationships among Reference Data Table 1, Reference Data Table 2, and Reference Data Table 3 are as follows: Reference Data Table 2 can be accessed through a foreign key in Reference Data Table 1, and Reference Data Table 3 can be accessed through a foreign key in Reference Data Table 2. Multiple target data tables to be governed that are related to the multiple target reference data tables include: Data Table to be governed 1, Data Table to be governed 2, and Data Table to be governed 3. Data Table to be governed 1 is related to Reference Data Table 1, Data Table to be governed 2 is related to Reference Data Table 2, and Data Table to be governed 3 is related to Reference Data Table 3. Therefore, the target relationships among Data Table to be governed 1, Data Table to be governed 2, and Data Table to be governed 3 can be accessed through a foreign key in Data Table to be governed 1. Therefore, the relationship between the data table to be governed 1, the data table to be governed 2, and the data table to be governed 3 can be configured based on this relationship.

[0143] By obtaining the relationships between data tables in the database, connections are established between different data tables in the database, enabling data to be accessed across data tables during database queries, thereby achieving data integration and analysis.

[0144] Step 406: Receive the display instruction sent by the tenant. The display instruction is used to instruct the visualization of the data-governed dataset.

[0145] When a tenant needs to view a data-governed dataset, they can execute a specified operation on their client to trigger a display command. This command then allows the cloud management platform to visualize the data-governed dataset. In one possible implementation, the cloud management platform can provide an interactive interface to the tenant, allowing the tenant to trigger the display command. After the tenant triggers the command, the cloud management platform can obtain the command through this interface. Optionally, the interactive interface can include one or more of the following implementations: API and UI.

[0146] Step 407: Based on the display instructions, visualize the dataset that has undergone data governance.

[0147] After receiving a display instruction from a tenant, the computing device can present the data-governed dataset to the tenant's client for viewing. For example, the computing device can send the data information of the data-governed dataset to the tenant's client, allowing the client to visualize the data. Visualizing the data-governed dataset helps users understand it more intuitively, discovering patterns and trends, and providing support for decision-making based on the dataset.

[0148] In some possible implementations, the content visualized in this application includes one or more of the following: multiple first entities renamed in a data-governed dataset, the names of which are obtained by modifying the names of multiple second entities in a reference dataset; multiple first entities with supplemented feature information in a data-governed dataset, the feature information of which is obtained by supplementing the feature information of multiple second entities in a reference dataset; or, multiple data tables configured with relationships in a data-governed dataset, the relationships between which are configured based on the relationships between multiple reference data tables in a reference dataset.

[0149] In one possible implementation, when a computing device visualizes a data-governed dataset, it first needs to determine the visualization tool required for the visualization. Then, it converts the data-governed dataset into a format recognizable by the visualization tool, imports the converted dataset into the visualization tool, and controls the visualization tool to visualize the converted dataset. When visualizing the dataset on a tenant's client, the computing device can obtain the projection data used by the visualization tool to visualize the converted dataset and provide this projection data to the tenant's client, enabling the client to perform visualization based on this projection data. For example, such as... Figure 11As shown, the visualization tool used in this application can be the InnostageWorkbench visualization tool. When the computing device visualizes the data-governed dataset, it needs to first convert the data-governed dataset into a format that the InnostageWorkbench visualization tool can recognize. Formats that the visualization tool can recognize include, for example, comma-separated values ​​(CSV) format and JavaScript object notation (JSON) format. Then, the converted dataset is imported into the visualization tool, and the visualization tool is controlled to visualize the converted dataset. In another implementation, when visualizing the data-governed dataset, the data-governed dataset can be displayed directly. For example, the data-governed data can be displayed through a data table. When visualizing on a tenant's client, the computing device can obtain the projection data of the data-governed data displayed in the data table and provide this projection data to the tenant's client so that the client can perform visualization based on this projection data.

[0150] Step 408: Receive feedback instructions from the tenant regarding the displayed data-governed dataset. The feedback instructions are used to indicate: agree to use the data-governed dataset, indicate modifications to the data-governed dataset, or switch the displayed data content.

[0151] After the computing device presents the data-governed dataset to the tenant, the tenant can provide feedback. For example, the tenant can determine whether the data-governed dataset meets the expected governance results and provide feedback to the computing device. For instance, if the tenant determines that the data-governed dataset meets the expected governance results, the tenant sends a feedback instruction to the computing device through their client, indicating their agreement to use the data-governed dataset. If the tenant determines that the data-governed dataset does not meet the expected governance results, the tenant sends a feedback instruction to the computing device through their client, indicating that the data-governed dataset should be modified. Modifications to the data-governed dataset may include one or more of the following: modifying the name, feature information, and relationships of the first entity in the data-governed dataset. It should be noted that the modifications described here are merely examples; users may also instruct modifications to other aspects of the data-governed dataset, which are not listed here.

[0152] In addition, tenant feedback instructions can also instruct on switching the displayed data content. For example, a tenant may instruct to view detailed information about the data in the dataset. This could involve viewing the content recorded in related tables based on the foreign key of the currently viewed table. It should be noted that this example of switching the display is merely illustrative; users can also instruct on other ways to switch the display of data-governed datasets, which will not be listed here.

[0153] Step 409: Respond according to the instructions in the feedback command.

[0154] Once the computing device receives a feedback instruction from the tenant, it can respond accordingly. By receiving and responding to feedback instructions from the tenant regarding the data-governed dataset, the device provides the tenant with the ability to interact with the computing device based on the data-governed dataset. This allows the tenant to perform specific operations on the data-governed dataset as needed, thereby improving the user experience.

[0155] The following two specific examples illustrate the implementation process of data governance.

[0156] Example 1: Processing an airline's dataset by standardizing its existing data names. The implementation process mainly includes the following steps:

[0157] (1) Training a knowledge extraction model for the aviation field. By deeply analyzing professional knowledge in the aviation field, a knowledge extraction model is constructed using natural language processing technology, specifically designed to identify and classify specific entities and relationships in aviation data. This model, trained through machine learning algorithms, can efficiently extract key information from large amounts of text, laying a solid foundation for building a knowledge graph in the aviation field.

[0158] (2) Extract multiple primary entities and their characteristic information from the airline's database. Develop an automated data sourcing script to quickly and accurately extract metadata from the airline's database, including flight details, airport information, etc. Then, use a knowledge extraction model to extract multiple primary entities and their characteristic information from the extracted metadata, obtaining aviation entities and relationships, such as runway attributes.

[0159] (3) Obtain a knowledge graph in the aviation field, which includes multiple second entities in the aviation field and their feature information.

[0160] (4) Using the target first entity extracted from the airline's dataset as the query basis, one or more target second entities with a relationship to the first entity are retrieved from the knowledge graph. Then, based on the one or more target second entities and the feature information of each target second entity, a target third entity that indicates the same thing as the target first entity is obtained from the one or more target second entities. Then, based on the feature information of the target third entity, the missing feature information of the target first entity is supplemented, thereby supplementing the missing information in the data table of the airline's dataset. That is, the data governance method provided in this application is used to supplement the missing information in the airline's data table.

[0161] Table 3 is an example of a data table in the airline dataset, and Table 4 is an example of the data table after completion. A comparison of Table 3 and Table 4 shows that by completing the missing information in the airline data table, the Chinese names and standardized English names of the fields in Table 3 were filled in.

[0162] Table 3

[0163]

[0164]

[0165] Table 4

[0166]

[0167] (5) Visualize the dataset after information completion. Import the dataset after information completion into visualization analysis tools such as InnostageWorkbench for data visualization. Figure 12 The dataset with the information completed is shown, according to which... Figure 12 It can be seen that the dataset after completing the information now includes both Chinese and standardized alphabetic English field names. The standardized alphabetic English names are displayed when clicking... Figure 12 The STRING field is displayed. For example, the Chinese names of the fields are: Runway Magnetic Direction, Runway Number, Landing Threshold Elevation, Runway Length, and Airport ICAO Identification.

[0168] Example 2: Process an airline's dataset and predict the relationships between the data tables within it.

[0169] (1) Same as (1) in Example 1.

[0170] (2) Same as (2) in Example 1.

[0171] (3) Same as (3) in Example 1.

[0172] (4) Using the target first entity extracted from the airline's dataset as the query basis, one or more target second entities with a relationship with the first entity are retrieved from the knowledge graph. Then, based on the one or more target second entities and the feature information of each target second entity, a target third entity that indicates the same thing as the target first entity is obtained from the one or more target second entities. Then, in multiple reference data tables, multiple target reference data tables related to the target third entity are obtained, and the target relationship between the multiple target reference data tables is obtained. Then, based on the multiple target reference data tables, multiple data tables to be governed are retrieved to obtain multiple target data tables to be governed that have a relationship with the multiple target reference data tables. Then, based on the target relationship between the multiple target reference tables, the relationship between the multiple target data tables to be governed is configured, thereby predicting the relationship between the data tables in the airline's dataset. That is, the data governance method provided in this application is used to predict the relationship between the data tables of the airline.

[0173] (5) Visualize datasets with configured relationships between tables. Import datasets with configured relationships between tables into visualization and analysis tools such as InnostageWorkbench for data visualization. The visualization result of the relationships between two tables in a dataset with configured relationships is as follows: Figure 13 As shown, according to this Figure 13 It can be seen that there is a relationship between the Airport Runway Information Table (ADM) and the runway table in the database, and the relationship is that the Airport Runway Information Table (ADM) corresponds one-to-one with the runway table in the database.

[0174] In summary, the data governance method based on public cloud technology provided in this application involves retrieving multiple second entities from the target first entity in the dataset to be governed, obtaining one or more target second entities that are related to the target first entity. This is equivalent to querying one or more target second entities that are related to the target first entity from among the multiple second entities. After querying the target second entities that are related to the target first entity from among the multiple second entities, data governance operations are then performed on the target first entity based on one or more target second entities and the feature information of each target second entity. This is equivalent to performing data governance operations on the target first entity with reference to one or more target second entities that are related to the target first entity and their feature information. Since the target second entities and their feature information are obtained from the reference dataset, and the dataset to be governed and the reference dataset belong to the same specified business domain, the target second entities and their feature information can be considered to have high reference value and higher accuracy. Therefore, by performing data governance operations on the target first entity based on the target second entities and their feature information, the accuracy of data governance can be effectively improved.

[0175] It should be noted that the order of steps in the data governance method based on public cloud technology provided in this application embodiment can be appropriately adjusted, and steps can also be added or removed as appropriate. Any variations that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application, and therefore will not be elaborated further.

[0176] The following describes an example of a virtual device in an embodiment of this application.

[0177] The above describes the data governance method based on public cloud technology in the embodiments of this application. Corresponding to the above method, the embodiments of this application also provide a data governance device based on public cloud technology. Figure 14 This is a schematic diagram of the structure of a data governance device based on public cloud technology provided in an embodiment of this application. Based on... Figure 14 The following are several components shown, which Figure 14 The data governance device based on public cloud technology shown is capable of performing the above-mentioned tasks. Figure 4 All or part of the operations shown. It should be understood that the device may include more additional components than those shown, or may omit some of the shown components; this application embodiment does not impose limitations in this regard. Optionally, the data governance device based on public cloud technology can be deployed on a cloud platform. For example... Figure 14 As shown, the data governance device 140 based on public cloud technology may include:

[0178] The interaction module 1401 is used to receive data governance instructions sent by the tenant. The data governance instructions are used to instruct the execution of data governance operations on the dataset to be governed that belongs to a specified business domain.

[0179] The processing module 1402 is used to obtain multiple first entities and the characteristic information of each first entity in the dataset to be governed based on data governance instructions. The characteristic information of the first entity includes one or more of the following: the type of the first entity, its attributes, or the relationship between the first entity and other first entities. The first entity is used to indicate transactions in a specified business domain. The transactions indicated by the first entity include one or more of the following: people, places, and business concepts in the specified business domain.

[0180] The processing module 1402 is also used to obtain multiple second entities and the feature information of each second entity in the reference dataset of the specified business domain. The feature information of the second entity includes one or more of the following: the type of the second entity, its attributes, or the relationship between the second entity and other second entities. The second entity is used to indicate transactions in the specified business domain. The transactions indicated by the second entity include one or more of the following: people, places, and business concepts in the specified business domain.

[0181] The processing module 1402 is also used to retrieve multiple second entities based on the target first entity to obtain one or more target second entities that are associated with the target first entity, wherein the target first entity is any one of the multiple first entities and the target second entity is one of the multiple second entities.

[0182] The processing module 1402 is also used to perform data governance operations on the target first entity based on one or more target second entities and the feature information of each target second entity, to obtain a data-governed dataset.

[0183] The interaction module 1401 is also used to send a response to the tenant indicating that the data governance operation has been completed.

[0184] In one possible implementation, the processing module 1402 is specifically used to: obtain a target third entity among the one or more target second entities that indicates the same thing as the target first entity, based on one or more target second entities and the feature information of each target second entity; and modify the name of the target first entity to the name of the target third entity if the name of the target first entity is different from the name of the target third entity.

[0185] In one possible implementation, the processing module 1402 is specifically used to: obtain a target third entity among the one or more target second entities that indicates the same thing as the target first entity, based on one or more target second entities and the feature information of each target second entity; and, in the case where the target first entity lacks feature information compared to the target third entity, supplement the target first entity with the missing feature information of the target first entity based on the feature information of the target third entity.

[0186] In one possible implementation, the reference dataset includes multiple reference data tables, and the dataset to be governed includes multiple data tables to be governed. The processing module 1402 is specifically configured to: based on one or more target second entities and the feature information of each target second entity, obtain a target third entity from the one or more target second entities that indicates the same thing as the target first entity; in the multiple reference data tables, obtain multiple target reference data tables related to the target third entity, and obtain the target association relationships between the multiple target reference data tables; search the multiple data tables to be governed based on the multiple target reference data tables to obtain multiple target data tables to be governed that have association relationships with the multiple target reference data tables; and configure the association relationships between the multiple target data tables to be governed based on the target association relationships between the multiple target reference tables.

[0187] In one possible implementation, the dataset to be governed includes multiple tables to be governed. Processing module 1402 is specifically used to: extract metadata for each table to be governed from the dataset, the metadata of which describes and defines the table; and extract multiple first entities and feature information for each first entity from the metadata of each table to be governed.

[0188] In one possible implementation, the target first entity is associated with one or more target second entities, including: one or more target second entities include a target third entity that indicates the same thing as the target first entity, and other target second entities besides the target third entity are associated with the target third entity.

[0189] In one possible implementation, the interaction module 1401 is also used to receive a display instruction sent by the tenant, which is used to instruct the visualization of the data-governed dataset.

[0190] Correspondingly, the interaction module 1401 is also used to visualize the data-governed dataset based on display instructions. The displayed content includes one or more of the following: multiple first entities in the data-governed dataset after name modification, the names of which are obtained by modifying the names of multiple second entities in the reference dataset; multiple first entities in the data-governed dataset after feature information is supplemented, the feature information of which is obtained by supplementing the feature information of multiple second entities in the reference dataset; or, multiple data tables configured with relationships in the data-governed dataset, the relationships between which are configured based on the relationships between multiple reference data tables in the reference dataset.

[0191] In one possible implementation, the interaction module 1401 is further configured to receive feedback instructions sent by the tenant for the displayed data-governed dataset, the feedback instructions being used to indicate: agreeing to use the data-governed dataset, indicating modification of the data-governed dataset, or switching the displayed data content.

[0192] Correspondingly, the processing module 1402 is also used to respond according to the instructions of the feedback command.

[0193] In one possible implementation, multiple second entities and the feature information of each second entity are represented by a knowledge graph, which is a graph including multiple vertices and edges between different vertices. The multiple vertices are used to indicate multiple second entities respectively. The information carried by any vertex is used to indicate the type and / or attributes of the second entity indicated by any vertex. The edge between any two vertices is used to indicate the relationship between the second entities indicated by any two vertices respectively.

[0194] Here, the detailed working process of the interaction module 1401 and the processing module 1402 is described in the preceding method embodiments. For example, the interaction module 1401 uses the aforementioned step 401 to receive the data governance instruction sent by the tenant, uses the aforementioned step 405 to send a response to the tenant indicating that the data governance operation has been completed, uses the aforementioned step 406 to receive the display instruction sent by the tenant, uses the aforementioned step 407 to visualize the data-governed dataset based on the display instruction, and uses the aforementioned step 408 to receive the feedback instruction sent by the tenant for the displayed data-governed dataset. The processing module 1402 uses the aforementioned step 402 to obtain multiple first entities and the feature information of each first entity in the dataset to be governed, uses the aforementioned step 403 to obtain multiple second entities and the feature information of each second entity in the reference dataset of the specified business domain, uses the aforementioned step 404 to search for multiple second entities based on the target first entity to obtain one or more target second entities that are associated with the target first entity, uses the aforementioned step 405 to perform data governance operations on the target first entities, and uses the aforementioned step 409 to respond according to the instructions of the feedback instruction. The embodiments of this application will not be described again here.

[0195] Both the interaction module 1401 and the processing module 1402 can be implemented in software or in hardware. For example, the implementation of the interaction module 1401 will be described below. Similarly, the implementation of the processing module 1402 can refer to the implementation of the interaction module 1401.

[0196] As an example of a software functional unit, the interaction module 1401 may include code running on a computing instance. The computing instance may include at least one of a physical host (computing device), a virtual machine, or a container. Further, the aforementioned computing instance may be one or more. For example, the interaction module 1401 may include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers used to run the code may be distributed in the same region or in different regions. Further, the multiple hosts / virtual machines / containers used to run the code may be distributed in the same availability zone (AZ) or in different AZs, each AZ including one cloud data center or multiple geographically proximate cloud data centers. Typically, a region may include multiple AZs.

[0197] Similarly, multiple hosts / virtual machines / containers used to run this code can be distributed within the same Virtual Private Cloud (VPC) or across multiple VPCs. Typically, a VPC is set up within a region. Communication between two VPCs within the same region, as well as between VPCs in different regions, requires a communication gateway to be set up within each VPC to enable interconnection between VPCs.

[0198] As an example of a hardware functional unit, the interaction module 1401 may include at least one computing device, such as a server. Alternatively, the interaction module 1401 may also be a device implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD). The PLD may be implemented using a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), generic array logic (GAL), or any combination thereof.

[0199] The multiple computing devices included in the interaction module 1401 can be distributed in the same region or in different regions. Similarly, the multiple computing devices included in the interaction module 1401 can be distributed in the same Availability Zone (AZ) or in different AZs. Likewise, the multiple computing devices included in the interaction module 1401 can be distributed in the same Virtual Private Cloud (VPC) or in multiple VPCs. These multiple computing devices can be any combination of computing devices such as servers, ASICs, PLDs, CPLDs, FPGAs, and GALs.

[0200] It should be noted that, in other embodiments, either the interaction module 1401 or the processing module 1402 can be used to execute any step in the data governance method based on public cloud technology. The steps implemented by the interaction module 1401 and the processing module 1402 can be specified as needed. By implementing different steps in the data governance method based on public cloud technology through the interaction module 1401 and the processing module 1402, all functions of the data governance device based on public cloud technology can be realized.

[0201] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of each component described above can be referred to the corresponding content in the foregoing method embodiments, and will not be repeated here.

[0202] The following provides examples illustrating the basic hardware structures involved in the embodiments of this application.

[0203] This application also provides a computing device 1500. For example... Figure 15 As shown, the computing device 1500 includes a bus 1502, a processor 1504, a memory 1506, and a communication interface 1508. The processor 1504, the memory 1506, and the communication interface 1508 communicate with each other via the bus 1502. The computing device 1500 can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memories in the computing device 1500.

[0204] Bus 1502 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 15 The bus 1502 may be represented by a single line, but this does not mean that there is only one bus or one type of bus. The bus 1502 may include a path for transmitting information between various components of the computing device 1500 (e.g., memory 1506, processor 1504, communication interface 1508).

[0205] Processor 1504 may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0206] The memory 1506 may include volatile memory, such as random access memory (RAM). The processor 1504 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0207] The memory 1506 stores executable program code, which the processor 1504 executes to implement the functions of the aforementioned interaction module 1401 and processing module 1402, thereby realizing a data governance method based on public cloud technology. In other words, the memory 1506 stores instructions for executing the data governance method based on public cloud technology.

[0208] The communication interface 1508 uses transceiver modules, such as, but not limited to, network interface cards and transceivers, to enable communication between the computing device 1500 and other devices or communication networks.

[0209] This application also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.

[0210] like Figure 16 As shown, the computing device cluster includes at least one computing device 1500. The memory 1506 of one or more computing devices 1500 in the computing device cluster may store the same instructions for executing data governance methods based on public cloud technology.

[0211] In some possible implementations, the memory 1506 of one or more computing devices 1500 in the computing device cluster may also store partial instructions for executing data governance methods based on public cloud technology. In other words, a combination of one or more computing devices 1500 can jointly execute instructions for executing data governance methods based on public cloud technology.

[0212] It should be noted that the memory 1506 in different computing devices 1500 within the computing device cluster can store different instructions, which are used to execute certain functions of the data governance device based on public cloud technology. That is, the instructions stored in the memory 1506 of different computing devices 1500 can implement the functions of one or more modules in the interaction module 1401 and the processing module 1402.

[0213] In some possible implementations, one or more computing devices in a computing device cluster can be connected via a network. This network can be a wide area network (WAN), a local area network (LAN), or similar. Figure 17 One possible implementation is shown. For example... Figure 17As shown, the two computing devices 1500A and 1500B are connected via a network. Specifically, they are connected to the network through the communication interfaces in each computing device. In this possible implementation, the memory 1506 in computing device 1500A stores instructions for executing the functions of the interaction module 1401. Simultaneously, the memory 1506 in computing device 1500B stores instructions for executing the functions of the processing module 1402.

[0214] It should be understood that Figure 17 The functions of computing device 1500A shown can also be performed by multiple computing devices 1500. Similarly, the functions of computing device 1500B can also be performed by multiple computing devices 1500.

[0215] This application also provides another computing device cluster. The connection relationships between the computing devices in this computing device cluster can be similarly referred to... Figure 16 and Figure 17 The connection method of the computing device cluster. The difference is that the memory 1506 of one or more computing devices 1500 in this computing device cluster can store the same instructions for executing data governance methods based on public cloud technology.

[0216] In some possible implementations, the memory 1506 of one or more computing devices 1500 in the computing device cluster may also store partial instructions for executing data governance methods based on public cloud technology. In other words, a combination of one or more computing devices 1500 can jointly execute instructions for executing data governance methods based on public cloud technology.

[0217] This application also provides a computer program product containing instructions. The computer program product may be software or program products containing instructions capable of running on a computing device or stored on any usable medium. When the computer program product runs on at least one computing device, it causes the at least one computing device to perform a data governance method based on public cloud technology.

[0218] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center that includes one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to perform a data governance method based on public cloud technology, or instruct the computing device to perform a data governance method based on public cloud technology.

[0219] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0220] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the raw data and executable code involved in this application were obtained with full authorization.

[0221] In the embodiments of this application, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The term "at least one" refers to one or more, and the term "multiple" refers to two or more, unless otherwise expressly defined.

[0222] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0223] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data governance method based on public cloud technology, characterized in that, The method is applied to a cloud platform, and the method includes: Receive data governance instructions sent by tenants, the data governance instructions being used to instruct the execution of data governance operations on the dataset to be governed that belongs to a specified business domain; Based on the data governance instructions, multiple first entities and feature information of each first entity in the dataset to be governed are obtained. The feature information of the first entity includes one or more of the following: the type of the first entity, its attributes, or the relationship between the first entity and other first entities. The first entity is used to indicate transactions in the specified business domain. The transactions indicated by the first entity include one or more of the following: people, places, and business concepts in the specified business domain. Obtain multiple second entities and feature information of each second entity from the reference dataset of the specified business domain. The feature information of the second entity includes one or more of the following: the type of the second entity, attributes, or the relationship between the second entity and other second entities. The second entity is used to indicate transactions in the specified business domain. The transactions indicated by the second entity include one or more of the following: people, places, and business concepts in the specified business domain. Based on the target first entity, the plurality of second entities are retrieved to obtain one or more target second entities that are associated with the target first entity, wherein the target first entity is any one of the plurality of first entities, and the target second entity is one of the plurality of second entities; Based on the one or more target second entities and the feature information of each target second entity, data governance operations are performed on the target first entity to obtain a data-governed dataset; Send a response to the tenant indicating that the data governance operation has been completed.

2. The method as described in claim 1, characterized in that, The step of performing data governance operations on the target first entity based on the one or more target second entities and the feature information of each target second entity includes: Based on the one or more target second entities and the feature information of each target second entity, a target third entity among the one or more target second entities that indicates the same thing as the target first entity is obtained; If the name of the first target entity is different from the name of the third target entity, the name of the first target entity shall be changed to the name of the third target entity.

3. The method as described in claim 1 or 2, characterized in that, The step of performing data governance operations on the target first entity based on the one or more target second entities and the feature information of each target second entity includes: Based on the one or more target second entities and the feature information of each target second entity, a target third entity among the one or more target second entities that indicates the same thing as the target first entity is obtained; If the target first entity lacks feature information compared to the target third entity, the target first entity is supplemented with the missing feature information based on the feature information of the target third entity.

4. The method according to any one of claims 1 to 3, characterized in that, The reference dataset includes multiple reference data tables, and the dataset to be governed includes multiple data tables to be governed. The step of performing data governance operations on the target first entity based on the one or more target second entities and the feature information of each target second entity includes: Based on the one or more target second entities and the feature information of each target second entity, a target third entity among the one or more target second entities that indicates the same thing as the target first entity is obtained; From the plurality of reference data tables, obtain a plurality of target reference data tables related to the target third entity, and obtain the target association relationships between the plurality of target reference data tables; Based on the multiple target reference data tables, the multiple data tables to be governed are retrieved to obtain multiple target data tables to be governed that are associated with the multiple target reference data tables; Based on the target association relationships among the multiple target reference data tables, configure the association relationships among the multiple target data tables to be governed.

5. The method according to any one of claims 1 to 4, characterized in that, The dataset to be governed includes multiple tables of data to be governed. Obtaining multiple first entities and feature information of each first entity from the dataset to be governed includes: Extract the metadata of each data table to be governed from the dataset to be governed. The metadata of the data table to be governed is the data that describes and defines the data table to be governed. Extract multiple first entities and the characteristic information of each first entity from the metadata of each data table to be governed.

6. The method according to any one of claims 1 to 5, characterized in that, The first target entity is associated with one or more second target entities, including: there is a third target entity among the one or more second target entities that indicates the same thing as the first target entity, and the other second target entities among the multiple second target entities, excluding the third target entity, are related to the third target entity.

7. The method as described in any one of claims 1 to 6, characterized in that, The method further includes: Receive a display instruction sent by the tenant, the display instruction being used to instruct the data-governed dataset to be visualized; Based on the display instructions, the data-governed dataset is visualized, and the displayed content includes one or more of the following: The names of the plurality of first entities after being renamed in the data-governed dataset are obtained by modifying the names of the plurality of second entities in the reference dataset. The feature information of the plurality of first entities after supplementing feature information in the data-governed dataset is obtained by supplementing feature information of the plurality of second entities in the reference dataset. Alternatively, the data-governed dataset may be configured with multiple data tables that are related, and the relationships between these multiple data tables are configured based on the relationships between multiple reference data tables in the reference dataset.

8. The method as described in claim 7, characterized in that, The method further includes: Receive feedback instructions from the tenant regarding the displayed data-governed dataset, the feedback instructions being used to indicate: agree to use the data-governed dataset, indicate modification of the data-governed dataset, or switch the displayed data content; Respond according to the instructions given in the feedback command.

9. The method according to any one of claims 1 to 8, characterized in that, The plurality of second entities and the feature information of each second entity are represented by a knowledge graph, which is a graph including multiple vertices and edges between different vertices. The multiple vertices are used to indicate the plurality of second entities respectively. The information carried by any vertex is used to indicate the type and / or attributes of the second entity indicated by the vertex. The edge between any two vertices is used to indicate the relationship between the second entities indicated by the two vertices respectively.

10. A data governance device based on public cloud technology, characterized in that, The device is deployed on a cloud platform and includes: The interaction module is used to receive data governance instructions sent by tenants, which are used to instruct data governance operations to be performed on the dataset to be governed that belongs to a specified business domain. The processing module is used to obtain multiple first entities and feature information of each first entity in the dataset to be governed based on the data governance instructions. The feature information of the first entity includes one or more of the following: the type of the first entity, its attributes, or the relationship between the first entity and other first entities. The first entity is used to indicate transactions in the specified business domain. The transactions indicated by the first entity include one or more of the following: people, places, and business concepts in the specified business domain. The processing module is further configured to acquire multiple second entities and feature information of each second entity from the reference dataset of the specified business domain. The feature information of the second entity includes one or more of the following: the type of the second entity, its attributes, or the relationship between the second entity and other second entities. The second entity is used to indicate transactions in the specified business domain. The transactions indicated by the second entity include one or more of the following: people, places, and business concepts in the specified business domain. The processing module is further configured to retrieve the plurality of second entities based on the target first entity to obtain one or more target second entities that are associated with the target first entity, wherein the target first entity is any one of the plurality of first entities and the target second entity is one of the plurality of second entities; The processing module is further configured to perform data governance operations on the target first entity based on the one or more target second entities and the feature information of each target second entity, to obtain a data-governed dataset; The interaction module is also used to send a response to the tenant indicating that the data governance operation has been completed.

11. The apparatus as claimed in claim 10, characterized in that, The processing module is specifically used for: Based on the one or more target second entities and the feature information of each target second entity, a target third entity among the one or more target second entities that indicates the same thing as the target first entity is obtained; If the name of the first target entity is different from the name of the third target entity, the name of the first target entity shall be changed to the name of the third target entity.

12. The apparatus as claimed in claim 10 or 11, characterized in that, The processing module is specifically used for: Based on the one or more target second entities and the feature information of each target second entity, a target third entity among the one or more target second entities that indicates the same thing as the target first entity is obtained; If the target first entity lacks feature information compared to the target third entity, the target first entity is supplemented with the missing feature information based on the feature information of the target third entity.

13. The apparatus according to any one of claims 10 to 12, characterized in that, The reference dataset includes multiple reference data tables, the dataset to be governed includes multiple data tables to be governed, and the processing module is specifically used for: Based on the one or more target second entities and the feature information of each target second entity, a target third entity among the one or more target second entities that indicates the same thing as the target first entity is obtained; From the plurality of reference data tables, obtain a plurality of target reference data tables related to the target third entity, and obtain the target association relationships between the plurality of target reference data tables; Based on the multiple target reference data tables, the multiple data tables to be governed are retrieved to obtain multiple target data tables to be governed that are associated with the multiple target reference data tables; Based on the target association relationships among the multiple target reference data tables, configure the association relationships among the multiple target data tables to be governed.

14. The apparatus according to any one of claims 10 to 13, characterized in that, The dataset to be governed includes multiple tables of data to be governed. The processing module is specifically used for: Extract the metadata of each data table to be governed from the dataset to be governed. The metadata of the data table to be governed is the data that describes and defines the data table to be governed. Extract multiple first entities and the characteristic information of each first entity from the metadata of each data table to be governed.

15. The apparatus as claimed in any one of claims 10 to 14, characterized in that, The first target entity is associated with one or more second target entities, including: there is a third target entity among the one or more second target entities that indicates the same thing as the first target entity, and the other second target entities among the multiple second target entities, excluding the third target entity, are related to the third target entity.

16. The apparatus as claimed in any one of claims 10 to 15, characterized in that, The interaction module is also used to receive a display instruction sent by the tenant, the display instruction being used to instruct the data-governed dataset to be visualized; The interaction module is also used to visualize the data-governed dataset based on the display command, and the displayed content includes one or more of the following: The names of the plurality of first entities after being renamed in the data-governed dataset are obtained by modifying the names of the plurality of second entities in the reference dataset. The feature information of the plurality of first entities after supplementing feature information in the data-governed dataset is obtained by supplementing feature information of the plurality of second entities in the reference dataset. Alternatively, the data-governed dataset may be configured with multiple data tables that are related, and the relationships between these multiple data tables are configured based on the relationships between multiple reference data tables in the reference dataset.

17. The apparatus as claimed in claim 16, characterized in that, The interaction module is also used to receive feedback instructions sent by the tenant regarding the displayed data-governed dataset. The feedback instructions are used to indicate: agree to use the data-governed dataset, indicate modification of the data-governed dataset, or switch the displayed data content. The processing module is also configured to respond according to the instructions of the feedback command.

18. The apparatus according to any one of claims 10 to 17, characterized in that, The plurality of second entities and the feature information of each second entity are represented by a knowledge graph, which is a graph including multiple vertices and edges between different vertices. The multiple vertices are used to indicate the plurality of second entities respectively. The information carried by any vertex is used to indicate the type and / or attributes of the second entity indicated by the vertex. The edge between any two vertices is used to indicate the relationship between the second entities indicated by the two vertices respectively.

19. A computing device cluster, characterized in that, The system includes multiple computing devices, each comprising multiple processors and multiple memories, the multiple memories storing program instructions, and the multiple processors executing the program instructions to cause the cluster of computing devices to perform the method as described in any one of claims 1 to 9.

20. A computer-readable storage medium, characterized in that, Includes program instructions that, when executed on a computing device, cause the computing device to perform the method as described in any one of claims 1 to 9.

21. A computer program product containing instructions, characterized in that, When the instruction is executed by the computing device cluster, the computing device cluster causes the computing device cluster to perform the method as described in any one of claims 1 to 9.

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