Data asset processing method, device, medium and computer program product

Abstract

The invention relates to a data asset processing method, which comprises the steps of representing data assets based on a preset unified multi-dimensional feature model; the multi-dimensional feature model comprises a technical dimension, a business dimension, a management dimension and an operation dimension; collecting each dimension index corresponding to the data assets in a preset processing period, and determining a dynamic value index of the data assets based on each dimension index; determining and executing a management strategy matched with the dynamic value index from a preset management strategy set according to the dynamic value index; in response to execution of the management strategy, obtaining an update value of at least one index of the data assets in the operation dimension to update the index of the operation dimension; and using the updated index of the operation dimension to determine the dynamic value index in a subsequent processing period. The invention also relates to a data asset processing device, a medium and a computer program product.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to data asset processing methods, devices, media, and computer program products. Background Technology

[0002] As data becomes a key production factor, enterprises have an increasingly urgent need for systematic management of their data assets. However, existing data management solutions generally suffer from several shortcomings. First, metadata management is fragmented, with different types of metadata (such as technical metadata and business metadata) scattered across different systems, lacking effective correlation and integration mechanisms. This results in a fragmented view of data assets, making it impossible to form a unified understanding. Second, value assessment methods are subjective and often lacking. Management decisions rely heavily on subjective experience rather than objective quantitative indicators, leading to an inability to accurately identify high-value assets and resulting in unreasonable resource allocation. Third, management activities often lag behind actual operations. Traditional static and passive management methods cannot respond to changes in the status of data assets in real time, leading to a disconnect between management strategies and operational status, and low management efficiency. Finally, automation and intelligence levels are insufficient. Tasks such as metadata collection and lineage discovery are highly dependent on manual labor, resulting in low efficiency and difficulty in adapting to large-scale, rapidly changing data environments. The root cause lies in the fact that existing technical architectures have failed to build a unified model that integrates technical, business, management, and operational attributes, and lack a feedback-based closed-loop continuous optimization mechanism. Summary of the Invention

[0003] The purpose of this invention is to provide data asset processing methods, devices, media, and computer program products to solve the technical problems of fragmented data asset management, subjective value assessment, disconnect between management and operation, and low level of automation in the prior art.

[0004] The first embodiment of the present invention discloses a data asset processing method for an electronic device, the method comprising:

[0005] The data assets are represented based on a pre-defined unified multi-dimensional feature model, which includes technical, business, management, and operational dimensions.

[0006] The data asset is collected in various dimensions according to a preset processing cycle, and the dynamic value index of the data asset is determined based on the various dimensions of the data asset.

[0007] Based on the dynamic value index, a management strategy matching the dynamic value index is determined from a preset set of management strategies and executed.

[0008] In response to the execution of the management strategy, the updated value of at least one indicator of the data asset in the operational dimension is obtained to update the indicator of the operational dimension.

[0009] The updated operational dimension metrics will be used to determine the dynamic value index in subsequent processing cycles.

[0010] A second embodiment of the present invention discloses an electronic device, which includes a memory storing computer-executable instructions and a processor. When the instructions are executed by the processor, the electronic device performs a data asset processing method according to a first embodiment of the present invention.

[0011] A third embodiment of the present invention discloses a computer storage medium storing instructions that, when executed on a computer, cause the computer to perform a data asset processing method according to a first embodiment of the present invention.

[0012] A fourth embodiment of the present invention discloses a computer program product including computer-executable instructions, which are executed by a processor to implement a data asset processing method according to a first embodiment of the present invention.

[0013] The main differences and effects of the embodiments of the present invention compared with the prior art are as follows:

[0014] In this invention, data assets are characterized by a pre-defined unified multi-dimensional feature model. A pre-defined processing cycle is used to collect indicators corresponding to each dimension of the data assets, and a dynamic value index is determined based on these indicators. According to the dynamic value index, a management strategy matching the dynamic value index is determined and executed from a pre-defined set of management strategies. In response to the execution of the management strategy, the updated value of at least one indicator in the operational dimension of the data assets is obtained to update the operational dimension indicators. The updated operational dimension indicators are then used to determine the dynamic value index in subsequent processing cycles. This solution offers the following advantages: First, by constructing a unified multi-dimensional feature model, metadata silos are broken down, achieving a unified and three-dimensional representation of data assets and solving the problem of fragmented management. Second, by periodically collecting indicators and calculating the dynamic value index, an objective and quantitative value assessment system is established, providing a data-driven basis for management decisions. Third, by automatically executing differentiated management strategies based on the value index, real-time linkage and automation of management and operations are achieved, transforming passive governance into proactive operation. Finally, by obtaining updated operational metrics after strategy execution and feeding them back into the value calculation for the next cycle, a complete closed loop from "collection-analysis-action-feedback" is formed, realizing dynamic, refined, and continuous optimization governance of data assets, and significantly improving the management efficiency and return on investment of data assets. Attached Figure Description

[0015] Figure 1A flowchart illustrating a data asset processing method according to an embodiment of this application is shown.

[0016] Figure 2 An architectural block diagram of a data asset processing system according to an embodiment of this application is shown.

[0017] Figure 3 This is a hardware structure block diagram of an electronic device implementing the embodiments of this application. Detailed Implementation

[0018] 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.

[0019] Existing data asset management solutions suffer from fragmented metadata management, subjective value assessment, management lagging behind operations, and insufficient automation and intelligence. The root cause lies in the failure to build a unified data asset model and the lack of a closed-loop feedback and optimization mechanism in the management process.

[0020] To address the aforementioned technical problems, embodiments of this application provide a data asset processing method. Figure 1 A flowchart of a method according to an embodiment of this application is shown, which can be applied to electronic devices, such as servers or server clusters. Figure 1 As shown, the specific steps of the method may include:

[0021] S101, based on a preset unified multi-dimensional feature model, represents data assets; the multi-dimensional feature model includes at least technical dimensions, business dimensions, management dimensions and operational dimensions.

[0022] In this embodiment, during the initialization phase, the system can predefine or allow users to customize a unified multi-dimensional feature model of data assets based on the enterprise's data architecture blueprint. This model provides a unified framework and foundation for subsequent data asset digitization and quantitative evaluation. Figure 2 In the system architecture shown, this model corresponds to the multi-dimensional feature model 205 in the core model service layer 202.

[0023] As a specific application scenario, we will use a data asset named "User Order Fact Table" (hereinafter referred to as the example scenario) in the data warehouse of an e-commerce company as an example for illustration. The representation process of this data asset is as follows:

[0024] From a technical perspective, the model can include attributes such as data source type (e.g., Hadoop), storage format (e.g., Parquet), schema version, and partition information. These attributes describe the physical form and technical details of the data assets. For the example scenario, its technical characteristics could be recorded as being stored in a Hadoop cluster using the Parquet columnar storage format.

[0025] From a business perspective, this model can include attributes such as the business line it belongs to (e.g., a transaction center), the subject area (e.g., a sales domain), business definition (e.g., detailed data recording user order behavior), and data steward. These attributes give data assets a clear business meaning and attribution. In the example scenario, its business dimension characteristics can be recorded as belonging to the order subject area under the sales business line.

[0026] From a management perspective, the model can include attributes such as security level (e.g., confidential), privacy compliance labels (e.g., containing personal information), quality verification rules (e.g., order amount must be greater than zero), and data lifecycle policies. These attributes define the governance requirements and management standards for data assets. In the example scenario, because it contains information such as user contact information, its security level can be set to "high" and labeled with a compliance tag.

[0027] From an operational perspective, the model can include dynamically changing attributes such as access frequency, query latency, storage consumption, and update frequency. These attributes reflect the state and performance of data assets during actual use. For the example scenario, the initial values ​​of its operational dimension features may be empty and will be filled in later steps by collecting logs.

[0028] This step constructs a structured digital profile for each data entity (such as in the example scenario), integrating information from multiple sources. This breaks down the silos of traditional metadata management and lays the foundation for a unified and comprehensive understanding of data assets.

[0029] S102 collects indicators of various dimensions corresponding to the data assets according to a preset processing cycle, and determines the dynamic value index of the data assets based on the indicators of each dimension.

[0030] In this embodiment, the system runs automatically at a preset processing cycle (e.g., every morning at midnight) to ensure that the assessment of data asset value can reflect its latest status in a timely manner. The core of this step is the aggregation, fusion, and quantification of data value.

[0031] Continuing with the example scenario, the system performs the following operations within one processing cycle:

[0032] First, the system collects multi-source heterogeneous metadata. Through a series of scalable connectors and adaptation layers, the system automatically synchronizes technical metadata and operational logs from distributed data sources (such as relational databases, Hadoop clusters, and Kafka stream processing platforms). For example, the system scans Hadoop's metadata storage to obtain the table structure of example scenarios (technical dimension) and parses information such as the number of queries, average response time, and storage space occupied (operational dimension) from system audit logs or job scheduling logs. Simultaneously, the system provides API interfaces or web interfaces for data stewards or data administrators to input and update business metadata (such as business definitions and responsible parties) and manage metadata (such as security policies and quality thresholds).

[0033] Secondly, metadata association and fusion are performed. The platform's data processing pipeline cleans, transforms, and loads (ETL) the collected metadata. Entity parsing and association technologies, such as primary key matching or Natural Language Processing (NLP) analysis of field names and business terms, are used to associate metadata from different sources with the same data asset entity. For example, access events to the table "user_order_fact" recorded in the logs are associated with the "User Order Fact Table" defined in the business directory. Finally, all associated metadata is constructed into a structure like... Figure 2 The interconnected metadata knowledge graph 206 shown can be stored in a graph database or relational database to support subsequent analysis and services.

[0034] Then, perform a value quantification calculation. For example... Figure 2 The value calculation engine 207 in the core model service layer 202, as shown, has a pre-built evaluation algorithm model that calculates the value for each asset node in the knowledge graph. This model can be represented as: V = f(W_q × Q, W_i × I, W_p × P, W_c × C), where V is the normalized dynamic value index, Q, I, P, and C are the four core indicators of normalized quality, importance, popularity, and cost, respectively, and W is the weight coefficient of each indicator, which can be adjusted by the administrator through the management interface. Specifically, quality (Q) can be calculated based on the pass rate of associated quality verification rules; importance (I) can be assessed based on the number of downstream dependencies in its lineage or its security level; popularity (P) can be calculated based on recent access frequency; and cost (C) can be assessed based on its storage consumption and computing resource consumption. In the example scenario, because it is frequently accessed by core reports (high popularity) and has many downstream dependencies (high importance), even with high storage costs, its final calculated value index V will be at a high level.

[0035] This step, through automated data collection and fusion, and an objective quantitative evaluation model, solves the problems of subjectivity and lack of traditional value assessment, providing management decisions with objective and quantitative data support.

[0036] S103, Based on the dynamic value index, determine and execute management strategies that match the dynamic value index from a preset set of management strategies.

[0037] In this embodiment, the system has a built-in strategy execution engine that can automatically trigger corresponding management actions based on the calculated dynamic value index.

[0038] Specifically, the system can preset a set of differentiated full lifecycle management strategies. Based on the dynamic value index, the system automatically classifies data assets. For example, assets with a value index greater than 0.8 are defined as "core assets", those between 0.3 and 0.8 are "general assets", and those less than 0.3 are "assets to be archived".

[0039] In the example scenario, if its calculated value index is 0.9, it is automatically classified as a core asset. The strategy execution engine will trigger the corresponding strategy executor, for example, automatically requesting more computing resource quotas to ensure query performance, or increasing the frequency and priority of its data quality monitoring and alarms. This falls under the resource protection and priority governance strategy.

[0040] Conversely, for another old log table with a value index of only 0.2, the system will mark it as an asset to be archived and automatically trigger cost optimization operations.

[0041] S104, in response to the execution of the management strategy, obtain the updated value of at least one indicator of the data asset in the operational dimension, so as to update the indicators in the operational dimension.

[0042] In this embodiment, the execution results of the management strategy will directly or indirectly affect the operational status of data assets. This step is to capture such changes and form feedback.

[0043] Continuing with the example in S103, when cost optimization operations (such as data archiving or compression) are performed on an old log table with a value index of 0.2, its actual storage consumption in the underlying storage system will be significantly reduced. The system can obtain this updated storage consumption value by monitoring the API return results of the underlying storage system or parsing subsequent storage audit logs.

[0044] S105 will use the updated operational dimension metrics to determine the dynamic value index in subsequent processing cycles.

[0045] This step completes the closed loop of the entire management process.

[0046] At the start of the next processing cycle (e.g., early the next morning), when performing the value calculation in S102, the system will use the latest operational metrics obtained in S104. For archived old log tables, their cost metrics (C) will drop significantly, which will cause a change in the recalculation of their dynamic value index V. This complete closed loop from "collection-analysis-action-feedback" makes data asset management no longer a static, one-time registration, but a dynamic, continuously optimized, and refined governance process, significantly improving management efficiency and alignment with actual operations.

[0047] Through the above embodiments, this application effectively overcomes the shortcomings of the prior art by constructing a unified multidimensional model, introducing a quantitative value index, and establishing a closed-loop automated management process. It achieves unified understanding, objective evaluation, proactive operation, and continuous optimization of data assets, significantly improving the operational efficiency and return on investment (ROI) of data assets.

[0048] According to some embodiments of this application, the method further includes: receiving user input, wherein the user input is used to update at least one indicator of data assets in dimensions other than the operational dimension; and using the user-input updated indicator for determining the dynamic value index in subsequent processing cycles.

[0049] In this embodiment, in addition to the indicators automatically collected by the system, the platform also supports manual intervention and information completion. For example, the data steward updates the business definition (business dimension) of the example scenario through a web interface, or the data administrator upgrades its security level (management dimension) according to the latest compliance requirements. These user-input updated indicators will be integrated into the metadata knowledge graph in real time or at the beginning of the next cycle, directly affecting the calculation of importance indicators (I) in the next round S102, thereby affecting the final dynamic value index. This ensures that the management model can reflect changes in business and governance requirements in a timely manner.

[0050] According to some embodiments of this application, the method further includes: when the dynamic value index meets a preset disposal trigger condition, outputting a disposal suggestion corresponding to the data asset, wherein the disposal suggestion is the context input by the user; receiving user input regarding the disposal suggestion; and when the user input is an instruction indicating agreement, executing a disposal operation corresponding to the disposal suggestion, and updating the operational dimension indicators through the execution result of the disposal operation.

[0051] In this embodiment, for some high-risk or irreversible management operations, the system adopts a "human-machine collaboration" model. For example, when the value index of a data asset (such as the aforementioned old log table) remains below 0.3 and has no access records within 90 days, the preset archiving trigger condition is met. The policy execution engine does not directly execute the archiving but generates a "pending archiving" disposal suggestion and pushes it to the designated administrator as a to-do task. The administrator can see the complete profile and value trend of the asset on the approval interface as a basis for decision-making. Only after the administrator clicks "agree" does the system call the API of the underlying storage system to execute the archiving instruction. This approach balances the efficiency of automation with the prudence of human decision-making.

[0052] According to some embodiments of this application, obtaining the updated value of at least one indicator of a data asset under the operational dimension includes: parsing operational events related to the data asset from system audit logs and job scheduling logs; and generating updated values ​​of operational dimension indicators based on the operational events, wherein the updated values ​​include at least one of access frequency, query latency, and storage consumption.

[0053] In this embodiment, the collection of operational metrics is highly automated. The platform's connector adaptation layer can interface with log sources from various systems. For example, by parsing the database audit logs, the total number of times the example scenario was accessed in the past 24 hours (access frequency) can be calculated; by parsing the data warehouse query logs, the average response time of query requests for the example scenario (query latency) can be calculated; and by calling commands or APIs of the distributed file system, the disk space occupied by the example scenario (storage consumption) can be obtained. These raw logs are parsed into structured operational events, and then aggregated and statistically analyzed to generate quantified operational metrics.

[0054] According to some embodiments of this application, data assets are characterized based on a preset unified multi-dimensional feature model, including: collecting technical metadata and operational logs; receiving business metadata and management metadata; merging technical metadata, operational logs, business metadata, and management metadata based on a unified identifier to generate a metadata knowledge graph containing data assets; and in subsequent processing cycles, merging the updated values ​​of at least one indicator of the operational dimension into the metadata knowledge graph to update the representation of the data assets.

[0055] For example, Figure 2 The diagram illustrates the architecture of a data asset processing system. This platform may include a data acquisition and adaptation layer 201 responsible for interfacing with heterogeneous data sources, a core model service layer 202 serving as the platform's central hub, an application function presentation layer 203 providing an interactive interface for users, and an open interface layer 204 for integration with external systems (such as data middleware platforms and operation and maintenance monitoring systems).

[0056] Specifically, the core model service layer 202 is the core of the platform's functionality, and it may include: a unified multi-dimensional feature model 205 that defines the data asset representation framework; a metadata knowledge graph 206 for storing and managing data asset entities and their complex relationships; a value calculation engine 207 responsible for performing the value quantification calculations described in S102; a lineage analysis engine 208 for supporting lineage tracing and impact analysis; and an intelligent recommendation engine 209 for personalized asset recommendations. The construction process of the metadata knowledge graph 206 is the data asset representation process. During the initial construction, metadata from different sources (automatically collected or manually entered) is aggregated and associated with graph nodes representing example scenarios through unified identifiers (such as "database name.table name"). The attributes of the nodes are their features in each dimension. In each processing cycle, the updated values ​​of operational metrics obtained in S104 refresh the operational dimension attributes on that node, thereby realizing the dynamic updating of the data asset representation.

[0057] According to some embodiments of this application, the method further includes: providing integrated asset portal services based on the represented data assets and dynamic value index; the asset portal services include at least one of: global directory retrieval, panoramic view of data assets, lineage tracing, and impact analysis.

[0058] In this embodiment, the application function presentation layer 203 provides a web interface, namely a data asset portal, for users with different roles. Users can perform global directory searches on this portal and quickly find the data assets they need by keywords, tags, or numerical ranges (such as value index > 0.8). The portal also provides a panoramic view of data assets, as well as advanced functions such as tracing lineage and influence analysis based on the metadata knowledge graph 206. In addition, the intelligent recommendation engine 209 in the core model service layer 202 can also push high-value assets that users may be interested in on the portal interface based on the user's behavioral profile and the similarity of assets.

[0059] According to some embodiments of this application, browsing a panoramic profile of data assets includes: generating and displaying a digital portrait of the data assets, the digital portrait including attributes of technical, business, management and operational dimensions; and generating and displaying a radar chart for visualizing a dynamic value index.

[0060] When a user clicks on a sample scenario in the portal, the system displays its complete digital profile, clearly listing all its attribute information across four dimensions. Simultaneously, to visually demonstrate its value composition, the system generates a radar chart, with four axes representing quality, importance, popularity, and cost (or their reciprocals). The area enclosed in the chart intuitively reflects its overall value.

[0061] According to some embodiments of this application: tracing lineage includes: in response to a tracing request for a data asset, traversing the relationship edges upwards in the metadata knowledge graph to determine the source asset; impact analysis includes: in response to an impact analysis request for a data asset, traversing the relationship edges downwards in the metadata knowledge graph to determine the downstream dependent assets.

[0062] The lineage analysis engine 208 in the core model service layer 202 is responsible for handling such requests. When a user initiates lineage tracing for an example scenario, the engine starts from the node representing the example scenario in the metadata knowledge graph 206, traversing upwards along edges representing relationships such as "generated" and "originates from," to find the ETL task node that generated it and the upstream source data table node, and presents this information in a visual graph. When a user initiates impact analysis, the engine traverses downwards along edges representing relationships such as "used by" and "flow to," finding all downstream reports, data marts, or applications that depend on the example scenario, helping the user assess the potential impact of changes.

[0063] According to some embodiments of this application, technical metadata, operational logs, business metadata, and management metadata are integrated, including: cleaning, transforming, and loading the collected and received metadata; and associating metadata from different sources with the same data asset through primary key matching or natural language processing technology.

[0064] This process, mentioned in S102, is a key technology for building knowledge graphs. For example, the system might discover that the term "customer order details" in the business catalog and the table name "tbl_cust_ord_dtl" in the technical metadata point to the same asset; this is achieved through semantic similarity analysis using NLP. This intelligent association capability is central to realizing a unified view of data assets.

[0065] According to some embodiments of this application, the dynamic value index includes quality indicators, importance indicators, popularity indicators, and cost indicators.

[0066] These four indicators form the core framework of the valuation, as detailed in S102. They comprehensively evaluate assets from four key perspectives: data availability, business impact, actual usage, and holding costs, resulting in a comprehensive and balanced valuation.

[0067] According to some embodiments of this application, a management strategy matching the dynamic value index is determined and implemented, including: classifying data assets according to the dynamic value index, the classification including core assets, general assets and archived assets; implementing resource protection and priority governance strategies for core assets; and performing cost optimization operations such as data archiving or compression for archived assets.

[0068] This is a concrete manifestation of the differentiated management strategy described in S103. Through automated hierarchical and strategy linkage, limited management and IT resources are prioritized for the highest value assets, while cost optimization is performed on low-value assets, thus achieving intelligent and optimized resource allocation.

[0069] Figure 3 This is a hardware structure block diagram of an electronic device implementing the embodiments of this application.

[0070] like Figure 3 As shown, the electronic device 300 may include one or more processors 302, a system motherboard 308 connected to at least one of the processors 302, system memory 304 connected to the system motherboard 308, non-volatile memory (NVM) 306 connected to the system motherboard 308, and a network interface 310 connected to the system motherboard 308.

[0071] Processor 302 may include one or more single-core or multi-core processors. Processor 302 may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In embodiments of the invention, processor 302 may be configured to perform one or more embodiments according to various embodiments of this application.

[0072] In some embodiments, the system motherboard 308 may include any suitable interface controller to provide any suitable interface to at least one of the processors 302 and / or any suitable device or component communicating with the system motherboard 308.

[0073] In some embodiments, system motherboard 308 may include one or more memory controllers to provide an interface to system memory 304. System memory 304 may be used to load and store data and / or instructions. In some embodiments, system memory 304 of electronic device 300 may include any suitable volatile memory, such as suitable dynamic random access memory (DRAM).

[0074] The NVM 306 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. In some embodiments, the NVM 306 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device, such as at least one of an HDD (Hard Disk Drive), a CD (Compact Disc) drive, or a DVD (Digital Versatile Disc) drive.

[0075] NVM 306 may include a portion of the storage resources on a device installed on electronic device 300, or it may be accessible by the device, but is not necessarily part of the device. For example, NVM 306 may be accessed over a network via network interface 310.

[0076] Specifically, system memory 304 and NVM 306 may each include a temporary copy and a permanent copy of instruction 320. Instruction 320 may include instructions that, when executed by at least one of processors 302, cause electronic device 300 to perform methods as described in any embodiment of this application. In some embodiments, instruction 320, hardware, firmware, and / or its software components may additionally / alternatively be located in system motherboard 308, network interface 310, and / or processor 302.

[0077] Network interface 310 may include a transceiver for providing a radio interface to electronic device 300, thereby enabling communication with any other suitable device (e.g., front-end module, antenna, etc.) via one or more networks. In some embodiments, network interface 310 may be integrated into other components of electronic device 300. For example, network interface 310 may be integrated into at least one of processor 302, system memory 304, NVM 306, and firmware device (not shown) with instructions, wherein when at least one of processor 302 executes the instructions, electronic device 300 implements one or more embodiments of various embodiments of this application.

[0078] The network interface 310 may further include any suitable hardware and / or firmware to provide a multiple-input multiple-output radio interface. For example, the network interface 310 may be a network adapter, a wireless network adapter, a telephone modem, and / or a wireless modem.

[0079] In one embodiment, at least one of the processors 302 may be packaged together with one or more controllers for the system motherboard 308 to form a system-in-package (SiP). In another embodiment, at least one of the processors 302 may be integrated on the same die with one or more controllers for the system motherboard 308 to form a system-on-a-chip (SoC).

[0080] The electronic device 300 may further include an input / output (I / O) device 312 connected to the system motherboard 308. The I / O device 312 may include a user interface enabling a user to interact with the electronic device 300; the peripheral component interface is designed to allow peripheral components to also interact with the electronic device 300. In some embodiments, the electronic device 300 may also include sensors for determining at least one of environmental conditions and location information related to the electronic device 300.

[0081] In some embodiments, I / O device 312 may include, but is not limited to, a display (e.g., a liquid crystal display, a touch screen display, etc.), a speaker, a microphone, one or more cameras (e.g., a still image camera and / or a video camera), a flashlight (e.g., a light-emitting diode flash) and a keyboard.

[0082] In some embodiments, the peripheral component interface may include, but is not limited to, a non-volatile memory port, an audio jack, and a power interface.

[0083] In some embodiments, the sensor may include, but is not limited to, a gyroscope sensor, an accelerometer, a proximity sensor, an ambient light sensor, and a positioning unit. The positioning unit may also be part of or interact with the network interface 310 to communicate with components of the positioning network, such as Global Positioning System (GPS) satellites.

[0084] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the electronic device 300. In other embodiments of this application, the electronic device 300 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0085] Program code can be applied to input instructions to perform the functions described in this invention and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, a system for processing instructions including processor 302 includes any system having a processor such as a digital signal processor (DSP), microcontroller, application-specific integrated circuit (ASIC), or microprocessor.

[0086] The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used when needed. In fact, the mechanisms described in this invention are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0087] One or more aspects of at least one embodiment can be implemented by instructions stored on a computer-readable storage medium, which, when read and executed by a processor, enable an electronic device to implement the methods of the embodiments described in this invention.

[0088] According to some embodiments of this application, a computer storage medium is disclosed, on which instructions are stored, which, when executed on a computer, cause the computer to perform a data asset processing method according to embodiments of this application.

[0089] The method embodiments of this application correspond to this embodiment, and this embodiment can be implemented in conjunction with the method embodiments of this application. The relevant technical details mentioned in the method embodiments of this application are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the method embodiments of this application.

[0090] According to some embodiments of this application, a computer program product is disclosed, including computer-executable instructions that are executed by a processor to implement a data asset processing method according to embodiments of this application.

[0091] The method embodiments of this application correspond to this embodiment, and this embodiment can be implemented in conjunction with the method embodiments of this application. The relevant technical details mentioned in the method embodiments of this application are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the method embodiments of this application.

[0092] It is understood that the specific embodiments described herein are merely for illustrative purposes and not for limiting the scope of this application. Furthermore, for ease of description, the accompanying drawings show only the parts relevant to this application, and not all of the structures or processes. It should be noted that similar reference numerals and letters in the drawings denote similar items throughout this application.

[0093] It should be understood that although the terms "first," "second," etc., may be used herein to describe various features, these features should not be limited by these terms. The use of these terms is merely for distinction and should not be construed as indicating or implying relative importance. For example, without departing from the scope of the exemplary embodiments, a first feature may be referred to as a second feature, and similarly, a second feature may be referred to as a first feature.

[0094] In the description of this application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set up," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this embodiment based on the specific circumstances.

[0095] The illustrative embodiments of this application include, but are not limited to, data asset processing methods, apparatus, media, and computer program products.

[0096] Various aspects of the illustrative embodiments will be described using terminology commonly employed by those skilled in the art to convey the essence of their work to others skilled in the art. However, it will be apparent to those skilled in the art that some alternative embodiments will be practiced using the features partially described. Specific figures and configurations are set forth for purposes of explanation in order to provide a more thorough understanding of the illustrative embodiments. However, it will be apparent to those skilled in the art that alternative embodiments may be practiced without specific details. In some other instances, well-known features have been omitted or simplified herein to avoid obscuring the illustrative embodiments of this application.

[0097] Furthermore, the various operations will be described as multiple separate operations in a manner most conducive to understanding the illustrative embodiments; however, the order of description should not be construed as implying that these operations must depend on the order of description, and many of these operations may be performed in parallel, concurrently, or simultaneously. Moreover, the order of the operations may also be rearranged. The process may be terminated when the described operations are completed, but may also include additional steps not included in the figures. The process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0098] References to "an embodiment," "embodiment," "illustrative embodiment," etc., in this application indicate that the described embodiment may include specific features, structures, or properties; however, each embodiment may or may not necessarily include specific features, structures, or properties. Furthermore, these phrases are not necessarily directed at the same embodiment. Moreover, when specific features are described in conjunction with specific embodiments, the knowledge of those skilled in the art can influence the combination of these features with other embodiments, whether or not those embodiments are explicitly described.

[0099] Unless the context otherwise specifies, the terms “comprising,” “having,” and “including” are synonyms. The phrase “A and / or B” means “(A), (B), or (A and B).”

[0100] As used herein, the term "module" may refer to, as part of, or include: a memory (shared, dedicated, or grouped), an application-specific integrated circuit (ASIC), electronic circuitry and / or a processor (shared, dedicated, or grouped), combinational logic circuitry, and / or other suitable components that provide the said functionality for running one or more software or firmware programs.

[0101] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order is not necessary. Rather, in some embodiments, these features may be illustrated in a manner and / or order different from that shown in the illustrative drawings. Furthermore, the inclusion of structural or methodological features in a particular drawing does not mean that all embodiments need to include such features; in some embodiments, these features may be omitted or may be combined with other features.

[0102] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions or programs carried or stored on one or more transient or non-transient machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors, etc. When the instructions or program are run by a machine, the machine may perform the various methods described above. For example, the instructions may be distributed via a network or other computer-readable media. Therefore, machine-readable media may include, but are not limited to, any mechanism for storing or transmitting information in a machine-readable (e.g., computer-readable) form, such as floppy disks, optical disks, optical disc read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), random access memories (RAMs), erasable programmable read-only memories (EPROMs), electronically erasable programmable read-only memories (EEPROMs), magnetic cards or optical cards, or flash memory or tangible machine-readable storage for transmitting network information via electrical, optical, acoustic, or other forms of signals (e.g., carrier waves, infrared signals, digital signals, etc.). Therefore, machine-readable media includes any form of machine-readable medium suitable for storing or transmitting electronic instructions or machine-readable (e.g., computer-readable) information.

[0103] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, the use of the technical solutions of this application is not limited to the various applications mentioned in the embodiments of this application. Various structures and modifications can be easily implemented with reference to the technical solutions of this application to achieve the various beneficial effects mentioned herein. Within the scope of knowledge possessed by those skilled in the art, all changes made without departing from the spirit of this application should be considered within the scope of this patent application.

Claims

1. A data asset processing method for an electronic device, comprising: The method comprises: characterizing a data asset based on a preset unified multi-dimensional feature model; the multi-dimensional feature model comprises a technology dimension, a business dimension, a management dimension, and an operation dimension; collecting each dimension indicator corresponding to the data asset in a preset processing period, and determining a dynamic value index of the data asset based on the each dimension indicator; determining and executing a management strategy matching the dynamic value index from a preset management strategy set according to the dynamic value index; in response to the execution of the management strategy, obtaining an updated value of at least one indicator of the data asset in the operation dimension to update the indicator of the operation dimension; using the updated indicator of the operation dimension in subsequent processing periods to determine the dynamic value index.

2. The method of claim 1, wherein, Further comprising: receiving user input for updating at least one indicator of other dimensions of the data asset except the operation dimension; using the indicator updated by the user input in subsequent processing periods to determine the dynamic value index.

3. The method of claim 2, wherein, Further comprising: when the dynamic value index meets a preset disposal trigger condition, outputting a disposal suggestion corresponding to the data asset, the disposal suggestion being a context of the user input; receiving user input for the disposal suggestion; in response to the user input being an instruction indicating agreement, performing a disposal operation corresponding to the disposal suggestion, and updating the indicator of the operation dimension through the execution result of the disposal operation.

4. The method of claim 1, wherein, The method further comprises: parsing an operation event related to the data asset from system audit logs and job scheduling logs; according to the operation event, statistically generating the updated value of the indicator of the operation dimension, the updated value comprising at least one of access frequency, query delay, and storage consumption.

5. The method of claim 1, wherein, The method further comprises: collecting technical metadata and operation logs; receiving business metadata and management metadata; based on a unified identifier, fusing the technical metadata, the operation logs, the business metadata, and the management metadata to generate a metadata knowledge graph containing the data asset; in subsequent processing periods, fusing the updated value of at least one indicator of the operation dimension to the metadata knowledge graph to update the characterization of the data asset.

6. The method of claim 5, wherein, The method further comprises: based on the characterized data asset and the dynamic value index, providing an integrated asset portal service; the asset portal service comprises at least one of global directory retrieval, data asset panoramic portrait browsing, blood relationship tracing, and impact analysis.

7. The method of claim 6, wherein, The data asset panoramic portrait browsing comprises: generating and displaying a digital portrait of the data asset, the digital portrait containing attributes of the technology dimension, the business dimension, the management dimension, and the operation dimension; and generating and displaying a radar chart for visualizing the dynamic value index.

8. The method of claim 6, wherein: The blood relationship tracing comprises: in response to a provenance request for the data asset, traversing upwards relationship edges in the metadata knowledge graph to determine a source asset; the impact analysis comprises, in response to an impact analysis request for the data asset, traversing downwards relationship edges in the metadata knowledge graph to determine a downstream dependent asset.

9. The method of claim 5, wherein, the fusing of the technical metadata, the operation log, the business metadata and the management metadata comprises: cleaning, transforming and loading processing of the collected and received metadata; through primary key matching or natural language processing technology, metadata of different sources are associated to the same data asset.

10. The method of claim 1, wherein, the dynamic value index comprises a quality indicator, an importance indicator, a heat indicator and a cost indicator.

11. The method of claim 1, wherein, the determining and executing of a management strategy matched with the dynamic value index comprises: grading the data asset according to the dynamic value index, the grading comprising a core asset, a general asset and an archived asset; for the core asset, a resource guarantee and a priority management strategy are executed; for the archived asset, a data archiving or compression cost optimization operation is executed.

12. An electronic device, comprising: the electronic device comprises a memory storing computer executable instructions and a processor, when the instructions are executed by the processor, the electronic device implements the data asset processing method according to any one of claims 1-11.

13. A computer storage medium, characterized in that on the computer storage medium, instructions are stored, when the instructions are run on a computer, the computer executes the data asset processing method according to any one of claims 1-11.

14. A computer program product, characterised in that, comprise computer executable instructions, the instructions are executed by a processor to implement the data asset processing method according to any one of claims 1-11.

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