Method and apparatus for assessing value of data assets, and computing device

Abstract

The present application provides a method for assessing the value of data assets. The method is applied to a scenario where a commercial bank performs service recommendation or service delivery. The method comprises: acquiring data asset data to be assessed inputted by a user, said data asset data comprising data assets to be assessed and an association relationship between said data assets, and said data assets comprising a plurality of data table assets; generating a corresponding data asset graph on the basis of said data asset data; determining an assessment indicator for said data assets on the basis of the data asset graph, the assessment indicator comprising at least one of the following indicators: an information content indicator for the data table assets, a data transmission efficiency indicator for the data table assets, and a data flow impact indicator for the data table assets; and determining the respective value of the plurality of data table assets on the basis of the assessment indicator for said data assets. The present solution can improve the accuracy of assessing the value of data assets.

Description

Method, device and computing device for evaluating data asset value

[0001] The present application claims priority to the Chinese patent application No. 202411008277.9, filed on July 25, 2024, with the State Intellectual Property Office of China, and entitled "Method, device and computing device for evaluating data asset value", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the field of digital assets, and more particularly, to a method, device and computing device for evaluating data asset value. BACKGROUND

[0003] Data asset refers to a data resource owned or controlled by an enterprise, which is recorded in a physical or electronic manner and can bring future economic benefits to the enterprise. With the rapid development of digital economy, the scale of data assets owned by enterprises is growing, data asset transactions are becoming more frequent, and the number of domestic data exchanges is increasing year by year. How to quickly and accurately help enterprises evaluate the value of data assets, help enterprises identify and mine high-value data assets from large-scale data assets, and ensure convenient and fair data transactions between enterprises has become an urgent problem to be solved by enterprises.

[0004] At present, the industry usually regards data assets as a kind of intangible assets, and evaluates the value of data assets by market method, income method and cost method. Since data assets are not equivalent to intangible assets, the valuation methods suitable for intangible assets are difficult to accurately evaluate the value of data assets, and therefore, the existing schemes for evaluating the value of data assets have low accuracy.

[0005] Therefore, how to improve the accuracy of evaluating the value of data assets has become a technical problem to be solved urgently. SUMMARY

[0006] The present application provides a method, device and computing device for evaluating data asset value, which can improve the accuracy of evaluating the value of data assets.

[0007] In a first aspect, a method for evaluating a value of a data asset is provided. The method comprises: obtaining user inputted data asset data to be evaluated, wherein the data asset data to be evaluated comprises data assets to be evaluated and a correlation between the data assets to be evaluated, and the data assets to be evaluated comprise a plurality of data table assets; generating a corresponding data asset graph based on the data asset data to be evaluated, wherein the data asset graph comprises a plurality of nodes and edges between the nodes, the nodes represent the data assets to be evaluated, and the edges represent the correlation between the data assets to be evaluated; determining evaluation indexes of the data assets to be evaluated based on the data asset graph, wherein the evaluation indexes comprise at least one of the following indexes: an information content index of a data table asset, a data transmission efficiency index of the data table asset, and a data flow influence index of the data table asset; and determining respective values of the plurality of data table assets based on the evaluation indexes of the data assets to be evaluated.

[0008] In the above technical solution, the data asset value evaluation indexes are designed based on the data asset graph, and the value of the data asset is evaluated based on the data asset value evaluation indexes. Thus, the accuracy of the data asset value evaluation can be improved, and the user does not need to have a specific academic background, thereby reducing the complexity of the data asset value evaluation. Moreover, the method for evaluating the value of the data asset can be reused in other enterprises or application scenarios, thereby reducing the cost of evaluating the value of the data asset.

[0009] In combination with the first aspect, in some implementations of the first aspect, the respective values of the plurality of data table assets are determined based on the information content index of the data table asset, the data transmission efficiency index of the data table asset, the data table asset, and respective weight coefficients.

[0010] In the above technical solution, a function of setting weights of the evaluation indexes is further provided, which is used to control the proportion of different evaluation indexes on the data asset valuation. The user can flexibly select the weight coefficients corresponding to the evaluation indexes according to actual needs. Thus, the user can adjust the influence of the indexes on the value of the data asset according to actual needs, and calculate the valuation score of the data asset according to actual needs.

[0011] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: outputting a data asset sequence to the user, wherein the data asset sequence comprises the plurality of data table assets arranged in descending order of value.

[0012] In the technical solution, the user can determine the relative values of the data table assets in the data asset according to the sequence of the output data table, so that the enterprise can find data tables with higher values from the data asset. The user can focus on the high-value data asset, effectively improve the efficiency of data asset value evaluation, and help data transaction and improve the utilization efficiency of the data asset.

[0013] In combination with the first aspect, in some implementations of the first aspect, the data asset to be evaluated further includes data field assets, and the information content indicator of the data table asset is determined according to the number of data field assets included in the data table asset.

[0014] In the technical solution, the quantitative determination method is designed for the information content indicator of the data table asset, which can effectively improve the efficiency of data asset value evaluation.

[0015] In combination with the first aspect, in some implementations of the first aspect, the data transfer efficiency indicator of the data table asset is determined according to the directed reachable distance between the data table asset and other data table assets in the data asset graph.

[0016] In the technical solution, the quantitative determination method is designed for the data transfer efficiency indicator of the data table asset, which can effectively improve the efficiency of data asset value evaluation.

[0017] In combination with the first aspect, in some implementations of the first aspect, the data flow influence indicator of the data table asset is determined according to the degree of damage of deletion of the data table asset to the data flow relationship in the data asset graph.

[0018] In the technical solution, the quantitative determination method is designed for the data flow influence indicator of the data table asset, which can effectively improve the efficiency of data asset value evaluation.

[0019] In combination with the first aspect, in some implementations of the first aspect, the data asset graph is generated by using a data asset graph model according to the data asset to be evaluated.

[0020] In combination with the first aspect, in some implementations of the first aspect, the values corresponding to the plurality of data table assets are used for accurate recommendation of the business put by the commercial bank.

[0021] In a second aspect, a device for evaluating value of data assets is provided, which comprises an obtaining module, a generating module and a determining module. The obtaining module is configured to obtain data of data assets to be evaluated input by a user, wherein the data of data assets to be evaluated comprises data assets to be evaluated and a correlation between the data assets to be evaluated, and the data assets to be evaluated comprise a plurality of data table assets. The generating module is configured to generate a corresponding data asset graph according to the data of data assets to be evaluated, wherein the data asset graph comprises a plurality of nodes and edges between the nodes, the nodes represent the data assets to be evaluated, and the edges represent the correlation between the data assets to be evaluated. The determining module is configured to determine evaluation indexes of the data assets to be evaluated according to the data asset graph, wherein the evaluation indexes comprise at least one of an information content index of a data table asset, a data transmission efficiency index of the data table asset and a data flow influence index of the data table asset. The determining module is further configured to determine respective values of the plurality of data table assets according to the evaluation indexes of the data assets to be evaluated.

[0022] With reference to the second aspect, in some implementations of the second aspect, the determining module is specifically configured to determine the respective values of the plurality of data table assets according to the information content index of the data table asset, the data transmission efficiency index of the data table asset, the data table asset and respective weight coefficients.

[0023] With reference to the second aspect, in some implementations of the second aspect, the device further comprises an output module configured to output a data asset sequence to the user, wherein the data asset sequence comprises the plurality of data table assets arranged in descending order of value.

[0024] With reference to the second aspect, in some implementations of the second aspect, the data assets to be evaluated further comprise data field assets, and the information content index of the data table asset is determined according to a number of data field assets contained in the data table asset.

[0025] With reference to the second aspect, in some implementations of the second aspect, the data transmission efficiency index of the data table asset is determined according to a directed reachable distance between the data table asset and other data table assets in the data asset graph.

[0026] With reference to the second aspect, in some implementations of the second aspect, the data flow influence index of the data table asset is determined according to a degree of damage to data flow relationships in the data asset graph caused by deletion of the data table asset.

[0027] With reference to the second aspect, in some implementations of the second aspect, the generating module is specifically configured to generate the data asset graph by using a data asset graph model according to the data of data assets to be evaluated.

[0028] With reference to the second aspect, in some implementations of the second aspect, the values corresponding to the plurality of data table assets are used for the commercial bank to accurately recommend the business to be put in place.

[0029] It should be understood that the benefits of the second aspect and various implementations of the second aspect are similar to those of the first aspect and various implementations of the first aspect, which will not be repeated here.

[0030] In a third aspect, a computing device is provided, which includes a processor and a memory, and optionally, an input / output interface. The processor is configured to control the input / output interface to receive and send information, and the memory is configured to store a computer program. The processor is configured to call and run the computer program from the memory, so as to execute the method in the first aspect or any possible implementation of the first aspect.

[0031] Optionally, the processor can be a general-purpose processor, which can be implemented by hardware or software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented by software, the processor can be a general-purpose processor, which reads software codes stored in the memory to implement the processor. The memory can be integrated in the processor or exist independently outside the processor.

[0032] In a fourth aspect, a computing device cluster is provided, which includes at least one computing device, and each computing device includes a processor and a memory. The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device, so as to make the computing device cluster execute the method in the first aspect or any possible implementation of the first aspect.

[0033] In a fifth aspect, a chip is provided, which acquires instructions and executes the instructions to implement the method in the first aspect or any possible implementation of the first aspect.

[0034] Optionally, as an implementation, the chip includes a processor and a data interface. The processor reads instructions stored in the memory through the data interface, and executes the method in the first aspect or any possible implementation of the first aspect.

[0035] Optionally, as an implementation, the chip can further include a memory, which stores instructions. The processor is configured to execute the instructions stored in the memory. When the instructions are executed, the processor is configured to execute the method in the first aspect or any possible implementation of the first aspect.

[0036] In a sixth aspect, a computer program product containing instructions, which when executed by a computing device, cause the computing device to perform the method according to the first aspect and any one of the implementations of the first aspect.

[0037] In a seventh aspect, a computer program product containing instructions, which when executed by a cluster of computing devices, cause the cluster of computing devices to perform the method according to the first aspect and any one of the implementations of the first aspect.

[0038] In an eighth aspect, a computer-readable storage medium includes computer program instructions, which when executed by a computing device, cause the computing device to perform the method according to the first aspect and any one of the implementations of the first aspect.

[0039] By way of example, such computer-readable storage can include one or more of the following: read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), Flash memory, electrically EPROM (EEPROM), and hard drive.

[0040] Optionally, as an implementation, the storage medium can be a non-volatile storage medium.

[0041] In a ninth aspect, a computer-readable storage medium includes computer program instructions, which when executed by a cluster of computing devices, cause the cluster of computing devices to perform the method according to the first aspect and any one of the implementations of the first aspect.

[0042] By way of example, such computer-readable storage can include one or more of the following: read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), Flash memory, electrically EPROM (EEPROM), and hard drive.

[0043] Optionally, as an implementation, the storage medium can be a non-volatile storage medium. BRIEF DESCRIPTION OF DRAWINGS

[0044] FIG. 1 is a schematic diagram of the relationship between data assets.

[0045] FIG. 2 is a schematic flowchart of a method for evaluating the value of a data asset according to an embodiment of the present application.

[0046] FIG. 3 is a schematic diagram of a data asset graph according to an embodiment of the present application.

[0047] FIG. 4 is a schematic diagram of data field nodes connected to data table nodes T1 and T2 respectively according to an embodiment of the present application.

[0048] FIG. 5 is a schematic diagram of another connection between data table nodes and data field nodes according to an embodiment of the present application.

[0049] FIG. 6 is a schematic block diagram of an apparatus 600 for evaluating the value of a data asset according to an embodiment of the present application.

[0050] FIG. 7 is a schematic diagram of an architecture of a computing device 1500 according to an embodiment of the present application.

[0051] FIG. 8 is a schematic diagram of an architecture of a computing device cluster according to an embodiment of the present application.

[0052] FIG. 9 is a schematic diagram of a connection between computing devices 1500A and 1500B via a network according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] The technical solutions in the present application will be described below with reference to the accompanying drawings.

[0054] The present application will present various aspects, embodiments or features around a system including a plurality of devices, components, modules, etc. It should be understood and appreciated that each system can include additional devices, components, modules, etc., and / or can not include all of the devices, components, modules, etc. discussed in connection with the accompanying drawings. Furthermore, combinations of these solutions can also be used.

[0055] In addition, in the embodiments of the present application, the words “example”, “for example”, and the like are used to mean by way of example, illustration, or description. Any embodiment or design solution described as “example” in the present application should not be construed as more preferred or more advantageous than other embodiments or design solutions. Rather, the word “example” is used to present concepts in a concrete manner.

[0056] In the embodiments of the present application, “corresponding” and “relevant” can be used interchangeably at times. It should be noted that when the distinction is not emphasized, the meanings expressed are consistent.

[0057] The service scenarios described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of network architecture and the appearance of new service scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0058] In this specification, the phrase "one embodiment" or "some embodiments" etc. means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, the appearances of the phrase "in one embodiment", "in some embodiments", "in other some embodiments", "in yet other embodiments", etc. in various places in the specification are not necessarily all referring to the same embodiment, unless otherwise specifically noted. The terms "comprising", "including", "having" and their conjugates mean "including but not limited to", unless otherwise specifically noted.

[0059] In this application, "at least one" means one or more, and "multiple" means two or more. The term "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the following cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0060] For ease of description, the related concepts involved in the embodiments of the present application are explained as follows.

[0061] 1. Data asset

[0062] Data asset refers to data resources owned or controlled by an enterprise, which can bring future economic benefits to the enterprise in a physical or electronic form. These data can be internally generated or externally obtained. Data assets can include but are not limited to customer data, transaction data, product data, operation data, market data, social media data, etc.

[0063] As an example, the data assets of an enterprise include the following three types: data table, data job, and data field. The following describes these three types of data assets in detail.

[0064] a) Data table is the main form of data asset, which is used to store the relevant information of business activities in enterprises, and is the key object of data asset value assessment.

[0065] b) Data job refers to a piece of SQL statement, which is used to process the data in data table.

[0066] c) Data field refers to the column in data table, which is the basic unit of data storage in data table.

[0067] 2. Intangible assets

[0068] Intangible assets refer to resources that have no physical form but can bring economic benefits to enterprises. Intangible assets can include but are not limited to: intellectual property, brand, patent, copyright, trademark, trade secret, customer relationship, software, license, franchise, etc.

[0069] 3. Data asset map:

[0070] There may be various relationships between different asset entities in data assets. Taking data assets as nodes and the association relationship between data assets as edges, the graph structure formed is called data asset map.

[0071] As an example, as shown in FIG. 1, the association relationship between data assets can include but is not limited to: PARENT_CHILD and DATA_FLOW. The following will describe the two association relationships in detail.

[0072] a) PARENT_CHILD refers to the subordinate relationship between data table and data field.

[0073] b) DATA_FLOW refers to the data transformation relationship between data table and data job. According to the types of the two end nodes, the transformation relationship can be divided into two kinds, one of which is the transformation relationship between data table and data job, indicating that the data in the data table is extracted and transformed by the data job; the other is the transformation relationship between data job and data table, indicating that the processed data of the data job is loaded into a new data table.

[0074] 4. Extract, transform, and load (ETL)

[0075] ETL is a process of data processing and integration, which is usually used for the construction of data warehouse and data analysis, including the processes of extract, transform, and load.

[0076] a) Extract

[0077] Extraction refers to the process of retrieving data from various source systems. Source systems can be relational databases, non-relational databases, file systems, APIs, cloud storage, social media, etc. The extraction process needs to ensure the integrity and consistency of the data. The extracted data can be structured, semi-structured, or unstructured.

[0078] b) Transform

[0079] Transform refers to the process of converting the extracted data into a format suitable for the target system. The transformation step may include but is not limited to: data cleaning (such as removing duplicate data, handling missing values), data format conversion (such as date format, encoding format), data aggregation (such as summarizing, calculating average), data standardization (such as unit conversion, identifier unification), and data augmentation (such as supplementing data through external data sources). The purpose of this step is to adapt the data to the needs of the target system and improve the quality of the data.

[0080] c) Load

[0081] Load refers to the process of loading the transformed data into the target system. The target system is usually a data warehouse, data lake, data mart, etc. During the loading process, the integrity and consistency of the data need to be considered to ensure that the data can be efficiently stored and accessed in the target system. Loading can be one-time or incremental (i.e., only loading data that has changed since the last load).

[0082] 5、Graph Centrality

[0083] Graph centrality is a concept in graph theory that measures the importance or centrality of nodes in a graph. Different centrality measurement methods can define the different importance of nodes in the network according to specific application needs. Commonly used centrality measurement methods include degree centrality, betweenness centrality, eigenvector centrality, etc.

[0084] It should be understood that graph centrality is a measure of the importance of nodes in a graph, and there are other methods to evaluate the importance of nodes in a graph, such as information entropy, influence spread model, maximum flow minimum cut, etc.

[0085] The "Opinions on Building a More Perfect Market Allocation Mechanism of Factors" and "Opinions on Accelerating the Improvement of the Socialist Market Economy System in the New Era" officially list data as a production factor, data has value like land, and has attributes such as registration, mortgage, financing, and trading. In today's digital age, data has become a new type of asset and an important driving force for economic, technological and social development.

[0086] With the rapid development of the digital economy, enterprises are possessing increasingly large amounts of data assets, and data asset transactions are becoming more frequent. The number of domestic data exchanges is increasing year by year. How to quickly and accurately help enterprises assess the value of their data assets, help them identify and mine high-value data assets from their massive data assets, and ensure convenient and fair data transactions between enterprises has become an urgent problem for enterprises to solve.

[0087] Currently, the industry typically regards data assets as a type of intangible asset and assesses their value using the market approach, income approach, and cost approach.

[0088] 1. Market approach

[0089] The market approach determines the value of an asset by referencing the trading prices of similar assets in the open market. This method assumes that market participants would pay the same price for similar assets under the same or similar conditions, and therefore estimates the fair value of the asset by comparing market trading prices.

[0090] The market approach has the following four main drawbacks:

[0091] a) Market data availability. In some markets, there may be a lack of sufficient comparable transaction data, making it difficult to find suitable comparisons;

[0092] b) Market volatility. Market prices may be affected by short-term fluctuations, leading to instability in the assessment results;

[0093] c) Subjectivity of Adjustments. Adjustments to comparable asset prices are subjective; appraisers may have different judgments on the adjustment factors, affecting the accuracy of the appraisal results.

[0094] d) Not applicable to unique assets. For some unique or custom-made assets, there may be no direct comparable transactions, making it difficult to apply the market approach for valuation.

[0095] 2. Income approach

[0096] The income approach determines an asset's value by predicting its future earnings and discounting them to their present value. This method is based on the assumption that the value of an asset equals the present value of its future earnings.

[0097] The income approach has the following four main drawbacks:

[0098] a) Forecasting uncertainty. Forecasting future earnings is highly uncertain, and the accuracy of the forecast directly affects the assessment results;

[0099] b) Discount rate selection. The determination of the discount rate is subjective, and different discount rate choices can lead to significant differences in the evaluation results;

[0100] c) Complexity. The income approach requires detailed financial forecasts and cash flow analysis, a relatively complex process that demands a high level of expertise from the appraiser;

[0101] d) Restrictions on application conditions. The application of the income approach is limited for assets with unstable or unpredictable income.

[0102] 3. Cost approach

[0103] The cost approach, also known as the replacement cost approach or reconstruction cost approach, estimates the value of an asset by determining the cost required to replace or rebuild it. This approach is based on the assumption that a rational buyer will not pay more than the cost required to build or replace an identical or similar asset.

[0104] The cost approach has the following three main drawbacks:

[0105] a) Ignoring market demand. Failing to consider market demand and supply-demand relationships may lead to valuation results deviating from market value;

[0106] b) Depreciation estimation is difficult. The estimation of depreciation and impairment is subjective; different estimation methods can affect the assessment results.

[0107] c) Not applicable to all assets. The cost method may not be applicable to highly liquid assets.

[0108] Therefore, there are three main problems with the current industry's reliance on classic intangible asset valuation methods for valuing data assets.

[0109] (1) Existing valuation methods have low accuracy.

[0110] Data assets are not the same as intangible assets. Valuation methods applicable to intangible assets are insufficient for accurately assessing their value. For example, intangible assets are non-replicable, while data assets are, making it difficult to effectively use the cost approach to evaluate their value. Intangible assets have stable values, but data asset values ​​fluctuate, making it difficult to effectively use the income approach to evaluate their value. Intangible asset trading markets are well-developed, but data asset trading markets are lacking, making it difficult to effectively use the market approach to evaluate their value.

[0111] (2) Existing valuation methods are highly complex.

[0112] The existing valuation method needs specific professional background (such as economics, mathematics, etc.), complex calculation and analysis process, and therefore is not easy to understand and use. For example, the market method needs to select appropriate assets in the existing market as the reference of the data asset, which requires sufficient understanding of the market background, reference value, and higher professional knowledge of the evaluator.

[0113] (3) The existing valuation method has poor reusability

[0114] The existing valuation method needs to design a special data asset valuation model according to the specific application scenario, and cannot be reused for other enterprises or application scenarios. The existing data asset valuation model is usually one-off, and needs to be specially designed by data experts after understanding the business scenario information of the enterprise, which is time-consuming and laborious, and has high cost.

[0115] Therefore, the embodiment of the present application provides a method for evaluating the value of data assets, which can design data asset value evaluation indexes based on a data asset graph, and evaluate the value of data assets based on the data asset value evaluation indexes. This not only improves the accuracy of data asset value evaluation, but also does not require users to have a specific academic background, reducing the complexity of data asset value evaluation. Moreover, it can be reused for other enterprises or application scenarios, so that the method for evaluating the value of data assets can be reused, reducing the cost of evaluating the value of data assets.

[0116] FIG. 2 is a schematic flowchart of a method for evaluating the value of data assets according to an embodiment of the present application. As shown in FIG. 2, the method can include steps 210-240, which are described in detail below.

[0117] Step 210: Obtain the data asset data input by the user to be evaluated.

[0118] In the embodiment of the present application, the data asset data input by the user to be evaluated can be obtained, which includes the data asset to be evaluated and the association relationship between the data asset to be evaluated.

[0119] For example, the data asset to be evaluated can include but is not limited to at least one of the following: data table, data job, data field, etc.

[0120] For example, the association relationship between the data asset to be evaluated can include but is not limited to at least one of the following: PARENT_CHILD relationship, DATA_FLOW relationship.

[0121] Step 220: Generate a data asset graph according to the data asset data to be evaluated.

[0122] In the embodiments of the present application, after obtaining the data asset data to be evaluated input by the user, the data asset data to be evaluated can be imported into the system. The system can automatically extract the data asset to be evaluated and the association relationship between the data assets to be evaluated, and generate a data asset graph based on the data asset to be evaluated and the association relationship between the data assets to be evaluated.

[0123] In a possible implementation manner, the data asset to be evaluated and the association relationship between the data assets to be evaluated can be automatically extracted according to the data asset graph model, and a data asset graph is generated based on the data asset to be evaluated and the association relationship between the data assets to be evaluated.

[0124] An example is taken as the data asset graph model. The data asset graph model can automatically extract data tables, data jobs and data fields in data and PARENT_CHILD and DATA_FLOW relationships between these data assets, and one-key generation of a data asset graph is realized.

[0125] For example, FIG. 3 shows a data asset graph, which is a graph structure composed of nodes and edges between the nodes. In the graph structure, the nodes represent data assets, for example, data tables, data jobs and data fields in the data assets. The edges in the graph structure represent the association relationship or connection relationship between the data assets, for example, the association relationship includes but is not limited to PARENT_CHILD relationship and DATA_FLOW relationship.

[0126] Step 230: determining an evaluation index of the data asset value according to the data asset graph.

[0127] After the data asset graph is generated, the evaluation index of the data asset value can be determined according to the related information in the data asset graph.

[0128] Data table assets are the most common type of enterprise data assets, store relevant information of business activities, and are the key objects for data asset value assessment. In different application scenarios, there are complex data transformation processes (ETL) between data table assets, reflecting the real business needs of enterprises, and are the key to measuring the value of data table assets. Data asset graph has business relevance, complex data transformation between data tables, can reflect the production activity needs of enterprises, and can be used to measure the value of data tables. Even if the application scenario changes, the data transformation relationship (ETL) between data tables in the data asset graph still retains certain unchanged topological features. As shown in FIG. 3, when the data assets and the relationship between the data assets are constructed into a data asset graph, the data table assets are abstracted as nodes, the relationship (PARENT_CHILD, DATA_FLOW) between the data tables is abstracted as edges, and the data transformation process also presents a unique topological structure.

[0129] Therefore, the embodiments of the present application utilize these characteristics of the data asset graph to establish a correlation between the business characteristics and the topological characteristics of the data assets, and propose a general evaluation index of data asset value. The general evaluation index of data asset value can be not limited by the application scenario of the data assets, and can be applied to the evaluation of the value of data assets in different application scenarios, thereby assisting users to efficiently and accurately evaluate the value of data assets.

[0130] As an example, the general evaluation index of data asset value can include, but is not limited to, at least one of the following: an information content index, a data transfer efficiency index, a data flow influence index, and the like.

[0131] The general evaluation index of data asset value is explained below by taking data table assets as an example.

[0132] 1. Information content index

[0133] The information content index refers to the number of attributes in the data table. The more the number, the more comprehensive the data table can describe the data, and the higher the information content.

[0134] 2. Data transfer efficiency index

[0135] The data transfer efficiency index refers to the directed graph reachable distance between the data table and other data tables in the data asset graph. The shorter the distance, the faster the data table can transfer data to other data tables, and the higher the data transfer efficiency.

[0136] 3. Data flow influence index

[0137] The data flow influence index refers to the degree of influence of a data table on a data flow. A data table exists in more data flow relationship paths, which means that the data table has a greater influence on data transformation, and the data flow influence of the data table is greater.

[0138] In order to avoid relying on data experts to evaluate the data assets of an enterprise, and to rely too much on the subjective cognition of experts, thereby affecting the accuracy of the evaluation results. The embodiment of the present application refers to the graph theory centrality, and designs a quantitative formula for the evaluation index of each data asset, which can effectively improve the efficiency of data asset value evaluation.

[0139] 1. Quantitative formula of information content index

[0140] As an example, referring to the degree centrality theory, the embodiment of the present application measures the information content of the data table asset by the number of data fields in the data table.

[0141] For example, the calculation formula of the information content index of the data table asset is as follows.

[0142] IC(i) = |F(i)|

[0143] Wherein, IC(i) represents the information content of the data table node i;

[0144] F(i) represents the number of data fields in the data table node i.

[0145] For example, as shown in FIG. 4, since the number of data fields in the data table node T1 is greater than the number of data fields in the data table node T2, the information content of the data table node T1 is greater than the information content of the data table node T2.

[0146] 2. Quantitative formula of data transmission efficiency index

[0147] As an example, referring to the shortest path-based centrality method, the embodiment of the present application measures the data transmission efficiency of the data table asset by the directed graph reachable distance between the data table node and other data table nodes.

[0148] For example, the calculation formula of the data transmission efficiency index of the data table asset is as follows.

[0149] Wherein, DTE(i) represents the data transmission efficiency of the data table node i;

[0150] d ij represents the directed graph reachable distance between the data table node i and the data table node j;

[0151] It should be understood that if there is no path between data table node i and data table node j, then define d ij = ∞, that is

[0152] For example, as shown in FIG. 5, since the directed graph reachable distance between data table node T1 and other data table nodes is less than the directed graph reachable distance between data table node T2 and other data table nodes, the data transfer efficiency of data table node T1 is greater than the data transfer efficiency of data table node T2.

[0153] 3. Quantitative formula of data flow influence index

[0154] As an example, referring to the robustness of the network, the embodiments of the present application measure the data flow influence by the degree of damage of the data table node pair DATA_FLOW relationship, and the greater the degree of damage, the greater the data flow influence.

[0155] For example, the calculation formula of the data flow influence index of the data table asset is as follows.

[0156] Wherein, DFI(i) represents the data flow influence of data table node i;

[0157] U represents the set of disconnected data table node pairs caused by deleting data table node i;

[0158] d jk represents the directed graph reachable distance between data table node j and data table node k after data table node i.

[0159] For example, as shown in FIG. 5, since the directed graph reachable distance between the nodes (for example, data field node T3 and data field node T4) originally connected before and after data table node T2 is deleted is getting farther and farther, while the directed graph reachable distance between the data table nodes (for example, data field node T3 and data field node T5) originally connected before and after data table node T1 is deleted is not getting farther and farther, this means that the degree of damage of the data flow relationship caused by deleting data table node T2 is greater, that is, the data flow influence of data table node T2 is greater than the data flow influence of data table node T1.

[0160] Step 240: determining the value of each data table in the data asset according to the evaluation index of the data asset value.

[0161] The embodiments of the present application can determine the value of each data table in the data asset according to at least one of the above evaluation indexes respectively, and output a sequence of data tables according to the value.

[0162] The above data table sequence includes data tables arranged from high to low according to the value of the data table, or can also include data tables arranged from low to high according to the value of the data table, and the embodiments of the application do not make specific limitations.

[0163] In the above technical solution, the relative values of each data table asset in the data asset can be determined according to the sequence of the output data table, so as to help the enterprise find data tables with higher values from the data asset.

[0164] Optionally, the embodiments of the application also provide a function of setting the weight of the evaluation index, for controlling the proportion of different evaluation indexes on the valuation of the data asset, and the user can flexibly select the weight coefficient corresponding to each evaluation index according to the actual needs. So that the user can adjust the influence degree of the index on the value of the data asset according to the actual needs, so as to calculate the valuation score of the data asset according to the actual needs. After the user sets the corresponding weight coefficient for each evaluation index, the value score of all data table assets in the data asset graph can be calculated, and an ordered sequence of a data table asset is output according to the value score.

[0165] For example, a possible formula for calculating the valuation score of data table i is listed below.

[0166] Wherein, V(i) represents the valuation score of data table i;

[0167] β represents the weight coefficient of the information content index of data table i;

[0168] β represents the weight coefficient of the data transmission efficiency index of data table i;

[0169] γ represents the weight coefficient of the data flow influence index of data table i;

[0170] n represents the number of data tables in the data asset.

[0171] In the above technical solution, the value evaluation problem of the data asset is converted into the sorting problem of the important nodes in the data asset graph, the business relevance and topological stability of the data asset graph are used, and a general data asset value evaluation index is designed. And referring to the centrality of graph theory, a quantitative formula is designed for each data asset valuation index to obtain an objective data asset value evaluation score. The value sequence of the data asset is output according to the objective valuation score, which helps the user to focus on high-value data assets, effectively improves the efficiency of data asset value evaluation, and helps data transaction and improves the utilization efficiency of data assets.

[0172] In one example, the method provided by the embodiments of the present application can be applied to the scenario of commercial banks conducting business reasoning or business placement, for example, the commercial bank accurately recommends the placed business according to the respective values of the plurality of data table assets.

[0173] The scenario is illustrated below.

[0174] The Financial Industry Data Circulation Market Research Report released at the 2022 Global Data Business Conference shows that the financial industry data element procurement project has grown rapidly in the past five years, far exceeding the 26% compound annual growth rate of the total number of financial industry procurement projects. Among them, the transaction of personal information data products is particularly active, and the data procurement project data of the banking industry dominates in the financial industry. Commercial banks have accumulated various data in business operations due to their special business advantages, including currency fund data, business development and management data, and customer operation and consumption behavior data, etc. These data occupy a key position in the data asset market. Data asset management will become an important profit point for commercial banks. To meet the new needs of the digital age, commercial banks need to integrate and efficiently use data resources for innovation, provide personalized and differentiated financial products, and improve the competitiveness of commercial banks in the industry and meet the diversified needs of the digital economy market. However, in the face of massive data, how to effectively evaluate the value of data assets is still a great challenge.

[0175] To solve this problem, a bank introduces a data asset graph technology, compares the values of data assets by using the method provided in the application, identifies potential high-value data assets, and thus realizes efficient data asset value evaluation. First, through data collection and data integration operations, data tables storing bank data are collected from different data sources, including customer data, transaction data, regulatory data, market data, and operation data. According to the ETL operation, the correlation between data tables, the flow path, and the like are analyzed and extracted to form a data asset graph. Second, according to the data asset value evaluation index provided in the application, the relevant information in the data asset graph is counted, such as the shortest path between node pairs, the number of fields of the data table node, the number of data transformations participated by the data table, and the like, to provide data for the subsequent data value evaluation index calculation. The application provides three data asset value evaluation indexes, including an information content index, a data transmission efficiency index, and a data flow influence index. The bank can select one or more indexes according to needs to participate in the subsequent data asset value evaluation. For example, for the value evaluation of customer data, the proportion of information content is more important. The more detailed the customer data description is, the more accurate the customer portrait is, which can help the bank accurately recommend business. In addition, the bank can also set new value evaluation indexes according to its own needs. For example, regulatory data is unique data in the financial industry, such as anti-money laundering data, capital adequacy ratio data, and the like. Among them, the anti-money laundering data is mainly used for compliance and anti-money laundering supervision. The value evaluation of such data can be performed by setting evaluation indexes “customer identity verification” and “suspicious transaction data quality” according to the customer and transaction correlation in the data asset graph, evaluating whether the data can effectively associate customer identity information, evaluating the number and quality of suspicious transactions in the data, and thus determining the value of the data. Finally, through the calculation of the above evaluation indexes, each data asset is assigned an evaluation score, and the data assets are sorted according to the value of the data assets, so that the bank focuses on potential high-value data assets, thereby helping the bank to accurately recommend the business.

[0176] The method provided in the application is described in detail above in combination with FIGS. 1-5. The embodiments of the device of the application will be described in detail below in combination with FIGS. 6-9. It should be understood that the description of the method embodiments corresponds to the description of the device embodiments, and therefore, the parts not described in detail can be referred to the foregoing method embodiments.

[0177] FIG. 6 is a schematic block diagram of a device 600 for evaluating the value of data assets according to an embodiment of the application. The device 600 can be realized by software, hardware, or a combination of both. The device 600 provided in the application can realize the method flow shown in FIG. 2 of the application.

[0178] In an example, the apparatus 600 includes an obtaining module 610, a generating module 620, and a determining module 630. The obtaining module 610 is configured to obtain user-inputted data asset data to be evaluated, where the data asset data to be evaluated includes data assets to be evaluated and association relationships between the data assets to be evaluated, and the data assets to be evaluated include a plurality of data table assets. The generating module 620 is configured to generate a corresponding data asset graph according to the data asset data to be evaluated, where the data asset graph includes a plurality of nodes and edges between the nodes, the nodes represent the data assets to be evaluated, and the edges represent the association relationships between the data assets to be evaluated. The determining module 630 is configured to determine evaluation indexes of the data assets to be evaluated according to the data asset graph, where the evaluation indexes include at least one of an information content index of a data table asset, a data transmission efficiency index of the data table asset, and a data flow influence index of the data table asset. The determining module 630 is further configured to determine respective values of the plurality of data table assets according to the evaluation indexes of the data assets to be evaluated.

[0179] Optionally, the determining module 630 is specifically configured to determine the respective values of the plurality of data table assets according to the information content index of the data table asset, the data transmission efficiency index of the data table asset, the data table asset, and respective weight coefficients.

[0180] Optionally, the apparatus 600 further includes an output module configured to output a data asset sequence to the user, where the data asset sequence includes the plurality of data table assets arranged in descending order of value.

[0181] Optionally, the data asset data to be evaluated further includes data field assets, and the information content index of the data table asset is determined according to a quantity of data field assets contained in the data table asset.

[0182] Optionally, the data transmission efficiency index of the data table asset is determined according to a directed reachable distance between the data table asset and other data table assets in the data asset graph.

[0183] Optionally, the data flow influence index of the data table asset is determined according to a degree of damage to data flow relationships in the data asset graph caused by deletion of the data table asset.

[0184] Optionally, the generating module 620 is specifically configured to generate the data asset graph by using a data asset graph model according to the data asset data to be evaluated.

[0185] Optionally, the respective values of the plurality of data table assets are used for accurate recommendation of business to be launched by a commercial bank.

[0186] The apparatus 600 here can be embodied in the form of functional modules. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited.

[0187] For example, the "module" can be a software program, a hardware circuit, or a combination of both, which implements the above functions. For example, the implementation of the obtaining module 610 is described below. Similarly, the implementation of other modules, such as the generating module 620, the determining module 630, and the outputting module, can refer to the implementation of the obtaining module 610.

[0188] As an example of the software functional unit, the obtaining module 610 can include code running on a computing instance. The computing instance can include at least one of a physical host (computing device), a virtual machine, and a container. Further, the computing instance can be one or more. For example, the obtaining module 610 can include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers for running the code can be distributed in the same region, or in different regions. Further, the multiple hosts / virtual machines / containers for running the code can be distributed in the same availability zone (AZ), or in different AZs, and each AZ includes one data center or multiple data centers with similar geographical locations. Generally, one region can include multiple AZs.

[0189] Similarly, the multiple hosts / virtual machines / containers for running the code can be distributed in the same virtual private cloud (VPC), or in multiple VPCs. Generally, one VPC is set in one region, and communication between two VPCs in the same region, and between VPCs in different regions, needs to be set in each VPC to set a communication gateway, and the interconnection between VPCs is realized through the communication gateway.

[0190] As an example of a hardware functional unit, the obtaining module 610 can include at least one computing device, such as a server or the like. Alternatively, the obtaining module 610 can also be a device implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), and the like. The PLD can be implemented by a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0191] The multiple computing devices included in the obtaining module 610 can be distributed in the same region or in different regions. The multiple computing devices included in the obtaining module 610 can be distributed in the same AZ or in different AZs. Similarly, the multiple computing devices included in the obtaining module 610 can be distributed in the same VPC or in multiple VPCs. The multiple computing devices can be any combination of servers, ASICs, PLDs, CPLDs, FPGAs, and GALs.

[0192] Therefore, the modules of the various examples described in the embodiments of the present application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0193] It should be noted that the apparatus provided by the above embodiments is only used for executing the above method, and the above division of the functional modules is used for example, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to complete all or part of the above described functions. For example, the acquisition module 610 can be used to execute any step in the above method, the generation module 620 can be used to execute any step in the above method, the determination module 630 can be used to execute any step in the above method, and the output module can be used to execute any step in the above method. The steps responsible for implementation by the acquisition module 610, the generation module 620, the determination module 630 and the output module can be specified as needed, and the above apparatus can be implemented by the acquisition module 610, the generation module 620, the determination module 630 and the output module to implement different steps in the above method.

[0194] In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments above, which will not be repeated here.

[0195] The method provided by the embodiments of the present application can be executed by a computing device, which can also be referred to as a computer system. It includes a hardware layer, an operating system layer running on the hardware layer, and an application layer running on the operating system layer. The hardware layer includes hardware such as processing units, memories, and memory control units, and the functions and structures of the hardware will be described in detail later. The operating system is any one or more computer operating systems that implement business processing through processes, such as Linux operating system, Unix operating system, Android operating system, iOS operating system, or windows operating system, etc. The application layer contains application programs such as browsers, address books, word processing software, and instant messaging software. In addition, the computer system can be a handheld device such as a smartphone, or a terminal device such as a personal computer, and the present application is not particularly limited as long as it can execute the method provided by the embodiments of the present application. The execution subject of the method provided by the embodiments of the present application can be a computing device, or a functional module in the computing device that can call and execute programs.

[0196] Next, a computing device provided by the embodiments of the present application will be described in detail in combination with FIG. 7.

[0197] FIG. 7 is an architectural schematic diagram of a computing device 1500 provided by the embodiments of the present application. The computing device 1500 can be a server or a computer or other device with computing capability. The computing device 1500 shown in FIG. 7 includes at least one processor 1510 and a memory 1520.

[0198] It should be appreciated that the number of processors and memories in the computing device 1500 is not limited.

[0199] The processor 1510 executes instructions in the memory 1520, so that the computing device 1500 implements the method provided by the present application. Alternatively, the processor 1510 executes instructions in the memory 1520, so that the computing device 1500 implements the functional modules provided by the present application, thereby implementing the method provided by the present application.

[0200] Optionally, the computing device 1500 further includes a communication interface 1530. The communication interface 1530 uses a transceiver module such as, but not limited to, a network interface card and a transceiver, to implement the communication between the computing device 1500 and other devices or communication networks.

[0201] Optionally, the computing device 1500 further includes a system bus 1540, wherein the processor 1510, the memory 1520 and the communication interface 1530 are connected with the system bus 1540 respectively. The processor 1510 can access the memory 1520 through the system bus 1540, for example, the processor 1510 can read and write data in the memory 1520 or execute code in the memory 1520 through the system bus 1540. The system bus 1540 is a peripheral component interconnect express (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus 1540 is divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in FIG. 7, but it does not mean that there is only one bus or only one type of bus.

[0202] In one possible implementation, the function of the processor 1510 is mainly to interpret the instructions (or code) of the computer program and process the data in the computer software. The instructions of the computer program and the data in the computer software can be saved in the memory 1520 or the cache 1516.

[0203] Optionally, the processor 1510 is a chip that has a processing capability of signals. As an example but not limitation, the processor 1510 is a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. Among them, the general processor is a microprocessor and the like. For example, the processor 1510 is a central processing unit (CPU).

[0204] Optionally, each processor 1510 includes at least one processing unit 1512 and a memory control unit 1514.

[0205] Optionally, the processing unit 1512 is also called core or kernel, which is the most important component of the processor. The processing unit 1512 is manufactured by single crystal silicon with certain production process, and all the calculations, command receiving, command storage and data processing of the processor are executed by the core. The processing units respectively independently run program instructions, and use the parallel computing capability to speed up the program running. Various processing units have fixed logic structures, for example, the processing unit includes logic units such as a first level cache, a second level cache, an execution unit, an instruction stage unit and a bus interface.

[0206] In one implementation, the memory control unit 1514 is used to control the data interaction between the memory 1520 and the processing unit 1512. Specifically, the memory control unit 1514 receives a memory access request from the processing unit 1512, and controls the access to the memory based on the memory access request. As an example but not limitation, the memory control unit is a memory management unit (MMU) and the like.

[0207] In one implementation, each memory control unit 1514 addresses the memory 1520 through a system bus. And an arbiter (not shown in FIG. 7) is configured in the system bus, which is responsible for processing and coordinating the competitive access of multiple processing units 1512.

[0208] In one implementation, the processing unit 1512 and the memory control unit 1514 are communicatively connected through the internal connection line of the chip, such as the address line, so as to realize the communication between the processing unit 1512 and the memory control unit 1514.

[0209] Optionally, each processor 1510 also includes a cache 1516, which provides temporary storage of often-used data and instructions. Recent use patterns of the data can be tracked by the cache control 1518 in order to maximize the effectiveness of the cache 1516. The cache 1516 is typically more expensive per byte of storage than the main memory 1520, but is faster. Therefore, the cache 1516 acts as a bridge between the main memory 1520 and the processor 1510, allowing the processor 1510 to run faster while the "working set" of data and instructions fit into the smaller and faster cache 1516.

[0210] The memory 1520 can provide a space for processes in the computing device 1500 to run, for example, the memory 1520 stores computer programs (in particular, the codes of the programs) for generating the processes. After the computer programs are run by the processor to generate the processes, the processor allocates corresponding storage spaces in the memory 1520 for the processes. Further, the storage spaces further include a text segment, an initialized data segment, a bit initialized data segment, a stack segment, a heap segment, and the like. The memory 1520 stores data generated during the running of the processes, for example, intermediate data, or process data, and the like, in the storage spaces corresponding to the processes.

[0211] Optionally, the memory is also referred to as the internal memory, which is used to temporarily store the data for the operations of the processor 1510, and exchange the data with the external memory such as the hard disk. As long as the computer is running, the processor 1510 will call the data needed for the operation to the internal memory for the operation, and the processing unit 1512 will transmit the results after the operation is completed.

[0212] By way of example, and not limitation, memory 1520 is volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of example, and not limitation, nonvolatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), or flash memory. Volatile memory is random access memory (RAM), which acts as external cache. By way of example, and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM). The system and method described herein can be stored on or transmitted across one or more of these forms of RAM, or nonvolatile memory.

[0213] The structure of the computing device 1500 listed above is only exemplary, and the present application is not limited thereto. The computing device 1500 of the embodiments of the present application includes various hardware in the prior art computer system, for example, the computing device 1500 also includes other memories in addition to the memory 1520, such as disk memories and the like. Those skilled in the art should understand that the computing device 1500 can also include other devices necessary for normal operation. Meanwhile, according to specific needs, those skilled in the art should understand that the above-mentioned computing device 1500 can also include hardware devices for realizing other additional functions. In addition, those skilled in the art should understand that the above-mentioned computing device 1500 can also only include devices necessary for realizing the embodiments of the present application, and does not have to include all the devices shown in FIG. 7.

[0214] The embodiments of the present application also provide a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server. In some embodiments, the computing device can also be a desktop computer, a notebook computer, or a terminal device such as a smart phone.

[0215] As shown in FIG. 8, the computing device cluster includes at least one computing device 1500. The memory 1520 in one or more computing devices 1500 in the computing device cluster can store the same instructions for performing the above-described method.

[0216] In some possible implementation, the memory 1520 in one or more computing devices 1500 in the computing device cluster can also respectively store partial instructions for performing the above-described method. In other words, the combination of one or more computing devices 1500 can collectively perform the instructions of the above-described method.

[0217] It should be noted that the memory 1520 in different computing devices 1500 in the computing device cluster can store different instructions, respectively, for performing partial functions of the above-described apparatus. That is, the instructions stored in the memory 1520 in different computing devices 1500 can implement the functions of one or more modules in the above-described apparatus.

[0218] In some possible implementation, one or more computing devices in the computing device cluster can be connected through a network. The network can be a wide area network, a local area network, or the like. FIG. 9 shows one possible implementation. As shown in FIG. 9, two computing devices 1500A and 1500B are connected through a network. Specifically, the computing devices are connected to the network through the communication interfaces in the computing devices.

[0219] It should be understood that the functions of the computing device 1500A shown in FIG. 9 can also be performed by multiple computing devices 1500. Similarly, the functions of the computing device 1500B can also be performed by multiple computing devices 1500.

[0220] In this embodiment, a computer program product including instructions is also provided. The computer program product can be software or a program product including instructions that can run on a computing device or be stored in any available medium. When the computer program product runs on the computing device, it causes the computing device to perform the above-described method or causes the computing device to implement the functions of the above-described apparatus.

[0221] In this embodiment, a computer readable storage medium is also provided. The computer readable storage medium can be any available medium or data storage device including one or more available media that a computing device can store. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk), or the like. The computer readable storage medium includes instructions, which, when executed on a computing device, cause the computing device to perform the above-described method.

[0222] It should be understood that the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0223] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0224] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0225] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0226] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0227] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0228] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0229] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of assessing the value of a data asset, characterized by, The method comprises: obtaining user-inputted data asset data to be evaluated, wherein the data asset data to be evaluated comprises data assets to be evaluated and association relationships between the data assets to be evaluated, and the data assets to be evaluated comprise a plurality of data table assets; generating a corresponding data asset graph according to the data asset data to be evaluated, wherein the data asset graph comprises a plurality of nodes and edges between the nodes, the nodes represent the data assets to be evaluated, and the edges represent the association relationships between the data assets to be evaluated; determining evaluation indexes of the data assets to be evaluated according to the data asset graph, wherein the evaluation indexes comprise at least one of the following indexes: an information content index of a data table asset, a data transmission efficiency index of the data table asset, and a data flow influence index of the data table asset; respectively determining values corresponding to the plurality of data table assets according to the evaluation indexes of the data assets to be evaluated.

2. The method of claim 1, wherein, The method further comprises: outputting a data asset sequence to the user, wherein the data asset sequence comprises the plurality of data table assets arranged in descending order of value.

3. The method according to claim 1 or 2, characterized in that, The data asset data to be evaluated further comprises data field assets, The information content index of the data table asset is determined according to a quantity of data field assets contained in the data table asset; or 4. The method according to any one of claims 1 to 3, characterized in that, The data transmission efficiency index of the data table asset is determined according to a directed reachable distance between the data table asset and other data table assets in the data asset graph; or The data flow influence index of the data table asset is determined according to a degree of damage to data flow relationships in the data asset graph caused by deletion of the data table asset. The method further comprises: generating the data asset graph by using a data asset graph model according to the data asset data to be evaluated.

5. The method according to any one of claims 1 to 4, characterized in that, The values corresponding to the plurality of data table assets are used for commercial banks to accurately recommend business to be launched. The device comprises:

6. The method according to any one of claims 1 to 5, characterized in that, an obtaining module configured to obtain user-inputted data asset data to be evaluated, wherein the data asset data to be evaluated comprises data assets to be evaluated and association relationships between the data assets to be evaluated, and the data assets to be evaluated comprise a plurality of data table assets; 7. An apparatus for assessing value of a data asset, the apparatus comprising: a generating module configured to generate a corresponding data asset graph according to the data asset data to be evaluated, wherein the data asset graph comprises a plurality of nodes and edges between the nodes, the nodes represent the data assets to be evaluated, and the edges represent the association relationships between the data assets to be evaluated; and ​ ​ determining, according to the data asset graph, an evaluation index of the data asset to be evaluated, the evaluation index comprising at least one of an information content index of a data table asset, a data transmission efficiency index of the data table asset, and a data flow influence index of the data table asset; the determining module is further configured to determine a respective value of each of the plurality of data table assets according to the evaluation index of the data asset to be evaluated.

8. The apparatus of claim 7, wherein, The determining module is specifically configured to: determine the respective value of each of the plurality of data table assets according to the information content index of the data table asset, the data transmission efficiency index of the data table asset, the data table asset, and a respective weight coefficient.

9. The apparatus of claim 7 or 8, wherein, The apparatus further comprises: an output module configured to output a data asset sequence to the user, the data asset sequence comprising the plurality of data table assets arranged in descending order of value.

10. The apparatus of any one of claims 7 to 9, wherein, The data asset to be evaluated further comprises a data field asset, the information content index of the data table asset is determined according to a number of data field assets contained in the data table asset; or the data transmission efficiency index of the data table asset is determined according to a directed reachable distance between the data table asset and other data table assets in the data asset graph; or the data flow influence index of the data table asset is determined according to a degree of damage to data flow relationships in the data asset graph caused by deletion of the data table asset.

11. The apparatus of any one of claims 7 to 10, wherein, The generating module is specifically configured to: generate the data asset graph by using a data asset graph model according to the data asset to be evaluated.

12. The apparatus of any one of claims 7-11, wherein, The respective value of each of the plurality of data table assets is used for a commercial bank to accurately recommend a business to be launched.

13. A cluster of computing devices, characterized in that, at least one computing device, each computing device comprising a processor and a memory; the processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method of any one of claims 1 to 6.

14. A computer program product comprising instructions, characterized in that, When the instructions are executed by the cluster of computing devices, the cluster of computing devices performs the method of any one of claims 1 to 6.

15. A computer-readable storage medium, characterized in that, computer program instructions, when executed by the cluster of computing devices, cause the cluster of computing devices to perform the method of any one of claims 1 to 6.

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