CN117573930A - Data asset management method, system, medium, equipment and terminal - Google Patents
Data asset management method, system, medium, equipment and terminal Download PDFInfo
- Publication number
- CN117573930A CN117573930A CN202311297223.4A CN202311297223A CN117573930A CN 117573930 A CN117573930 A CN 117573930A CN 202311297223 A CN202311297223 A CN 202311297223A CN 117573930 A CN117573930 A CN 117573930A
- Authority
- CN
- China
- Prior art keywords
- data
- data asset
- asset management
- asset
- assets
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Software Systems (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to the technical field of information data management, and discloses a data asset management method, a system, a medium, equipment and a terminal, wherein data assets managed by a system nano tube are integrated, data asset management factors are defined by defining a data asset management category, and a data asset association relationship is determined; and combining knowledge graph technology to realize multi-source and multi-form data asset association integration, constructing a data asset association relationship graph and determining the data asset context. The data asset management method based on the knowledge graph provides traceability and rechecking capabilities for data asset management, and solves the problem of a data black box in the data asset process. In the data asset management methodology provided by the invention, the construction of metadata information acquisition tools and knowledge maps adopted in the data asset acquisition system and the technical realization thereof are all technical key points. The invention satisfies the application scenes of the management of most data assets, and has the universality of floor practice and scene multiplexing.
Description
Technical Field
The invention belongs to the technical field of information data management, and particularly relates to a data asset management method, a system, a medium, equipment and a terminal.
Background
At present, a knowledge graph is a relational network obtained by connecting all different kinds of information (Heterogeneous Information).
Knowledge representation expresses various types of knowledge in the real world as computer-storable and computable structures. The machine must have knowledge, especially common sense knowledge, to achieve true humanoid intelligence. From the history of artificial intelligence, there has been a study of knowledge representation. Knowledge representation of the knowledge graph describes concepts, entities and their relationships in the objective world in a structured form, and represents information of the internet in a form closer to the human cognitive world, providing a basic support for understanding internet content.
In the prior art, some methods for managing data assets already exist, but most of the methods have problems of industry pertinence and scene multiplexing limitation, and deep dialysis insight is not achieved for the association relationship between data assets managed by enterprises.
Patent application publication number CN112732924a discloses a knowledge-graph-based power grid data asset management system and method. The method solves the problems that the current data are not effectively utilized, so that the time for acquiring the power grid data in the power grid dispatching process is long, the efficiency is low, and the effectiveness of the acquired power grid data is low. However, the application mainly aims at providing a method for data assets in the power industry, and the related subject of the map is a device entity and a data entity, so that the scene multiplexing capability is insufficient and the use is limited.
Patent application publication number CN113254507a discloses a method for intelligently constructing inventory of data asset catalogues. And the complete data asset catalogue is constructed by comprehensive inventory data and technical means. And carrying out knowledge fusion on the system function catalog, the service data catalog and the library table to form a knowledge fusion map.
In summary, in the prior art, the management method is provided for the access point by using the life cycle management of the data assets, and various dependency relationships are formed between the data assets in the whole life cycle. Therefore, there is a need to devise a new data asset management approach.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) In the prior art, a management method is provided for an access point by data asset life cycle management, various dependency relations are formed among data assets in the whole life cycle, and the association relations among the assets cannot be cleared based on a common management method, so that the problem of partial data black boxes in the data asset management is caused.
(2) The prior art mainly aims at providing a method for data assets in the power industry, and the related subject of the map is a device entity and a data entity, so that the scene multiplexing capability of the map is insufficient and the map has use restriction. The method mainly aims to solve the problems that the time for acquiring the power grid data is long, the efficiency is low, and the effectiveness of the acquired power grid data is low in the power grid dispatching process. And the knowledge graph base for integrating and sharing the resource entity data assets of the power grid equipment is used for designing a micro-service architecture module. It is difficult to provide a complete, universal reference value for industry data asset management approaches.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a data asset management method, a system, a medium, equipment and a terminal, in particular to a data asset management method, a system, a medium, equipment and a terminal based on a knowledge graph.
The present invention is thus embodied in a data asset management method comprising: integrating data assets of the system nanotubes, defining data asset management elements by defining a data asset management category, and determining a data asset association relationship; and combining knowledge graph technology to realize multi-source and multi-form data asset association integration, constructing a data asset association relationship graph and determining the data asset context.
Further, the data asset management method comprises the steps of:
step one, planning assets and defining asset types, and defining element information;
the data asset types include: application systems, databases, data tables, APIs, data dictionaries, data reports, AI models, tasks, and the like. Element information refers to multi-dimensional information defining and describing data assets, including general information, data information, business information, management information, security information, and the like. The element information of the data asset can be obtained through a metadata tool, and information improvement can be performed through manual registration.
Step two, collecting data assets and synchronizing the data assets;
through the data asset discovery capability, multiple types of data assets are discovered and synchronized from multiple channels such as an external platform, an application system, a data source and the like, and metadata information of the multiple types of data assets is identified and collected.
And thirdly, integrating the association relationship, constructing a knowledge graph, and realizing data asset management.
And constructing a data asset map through three elements of entities, relationships and attributes. The dependency relationships between data asset entities and the computational logic between data, and the equity relationships between data assets and organizations are presented in an interactive visual manner.
The data asset management method comprises the following steps: carrying out data asset planning work according to industry service attributes, defining a data asset management category, and defining data asset granularity; and dividing the data asset types, perfecting asset attribute information and defining asset source targets.
Further, in the second step, the data asset is collected by using a metadata information collection tool.
Further, the data asset management method further includes: based on the overall planning of the earlier stage of data asset management, corresponding data assets are collected and synchronized from all source channels, and the association relationship of the data assets is analyzed through technical principles and business principles.
Further, the data asset management method further includes: and constructing a polymorphic knowledge graph of the data asset by combining the technical capability of the graph engine, so as to realize unified integration of the multi-element data asset.
Another object of the present invention is to provide a data asset management system to which the data asset management method is applied, the data asset management system comprising:
the data asset planning module is used for carrying out data asset planning work according to the industry business attribute, defining the data asset management category and defining the data asset granularity;
the asset type definition module is used for dividing the data asset types, perfecting asset attribute information and defining asset source targets;
the data asset acquisition module is used for acquiring and synchronizing corresponding data assets from various source channels based on overall planning of the data asset management earlier stage;
the association relation analysis module is used for analyzing the association relation of the data asset through a technical principle and a business principle;
the knowledge graph construction module is used for constructing a data asset association relationship graph, determining the data asset context overall view and realizing data asset management.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the data asset management method.
It is a further object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the data asset management method.
Another object of the present invention is to provide an information data processing terminal for implementing the data asset management system.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
firstly, the invention provides a data asset management method based on a knowledge graph, which is characterized in that the knowledge graph technology is combined to realize multi-source and multi-form data asset association integration by defining the data asset management category, defining the data asset management elements and exploring the data asset association relationship, so as to construct a data asset graph, master the whole appearance of the data asset, provide the traceability and rechecking capability for the data asset management and solve the problem of a data black box in the data asset process. The data asset management methodology provided by the invention is characterized in that metadata information acquisition tools adopted in a data asset acquisition system, construction of a knowledge graph and technical realization thereof are all technical key points.
Secondly, patent application publication number CN112732924a and the present invention both propose a data asset management method based on a knowledge graph, but the application is mainly directed to a data asset proposal method in the power industry, and the related body of the graph is a device entity and a data entity, so that the scene multiplexing capability is insufficient and the application is limited. The data asset management method based on the knowledge graph, provided by the invention, meets the requirement of most data asset management application scenes, and has the universality of floor practice and scene multiplexing.
Thirdly, as inventive supplementary evidence of the claims of the present invention, the following important aspects are also presented:
expected benefits and commercial value after the technical proposal of the invention is converted
1) The method has the advantages that data management is comprehensively and comprehensively carried out around the whole life cycle of the data asset, diversified data asset ecology for promoting activity is established, high-quality data element supply is enhanced, data element circulation is quickened, a data element development and utilization mechanism is innovated, the development efficiency of data enabling business is improved, and the data asset value of industry is dominant.
2) The multi-channel data fusion pull-through is realized through the production element resource of the nano-tube big data product, the unified data collection, convergence and fusion are realized, the value release of the data asset is promoted by the standardized, visualized and micro-serviced lean service capability, the transformation from 'passive control' to 'active service' of enterprises is facilitated, and the industrial data asset management standard is erected.
3) The data resource is reasonably configured through the production element resource of the nanotube multi-channel, the data intercommunication sharing is enhanced, a set of data asset management system integrating management, use and operation is established, the quality of the data asset is effectively improved, the use cost of the asset is reduced, the energy efficiency of the data application is increased, and the value presentation of the data asset in the aspects of 'internal increment and external synergy' is promoted.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for data asset management provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a data asset management method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In view of the problems existing in the prior art, the present invention provides a data asset management method, system, medium, device and terminal, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the data asset management method provided by the embodiment of the invention includes the following steps:
s101, planning assets and defining asset types, and defining element information;
the planning data asset management scope is database, data table, API, task asset. After the management scope of the data asset is defined, the technical element information, business element information and management element information of various data assets are defined.
S102, collecting data assets and synchronizing the data assets;
and through the data discovery capability, discovering databases, data tables and associated API assets and task assets in each system, and synchronizing the data assets to a data asset platform for unified nano-tube.
And S103, integrating the association relationship, constructing a knowledge graph, and realizing data asset management.
The invention provides an intelligent data asset management method, which comprises the following steps:
step one: automatically planning assets and defining asset types by using an AI technology, and defining element information;
step two: automatically collecting and synchronizing data assets of the system nanotubes using AI technology;
step three: utilizing machine learning technology, such as deep learning or graphic neural network, automatically learning and integrating the association relationship between data assets, and constructing a knowledge graph of the association relationship of the data assets;
step four: and automatically executing a data asset management strategy by using a knowledge graph and an AI decision system to realize multi-source and multi-form data asset association and integration and determine the data asset context.
AI techniques in step one may include Natural Language Processing (NLP) and semantic understanding techniques for parsing and understanding the semantics and context of the data asset. The AI technique in step two may include a machine learning algorithm, such as a clustering algorithm or an anomaly detection algorithm, for detecting and synchronizing data assets. The machine learning technique in step three may include deep learning or a graph neural network for learning and modeling complex relationships between data assets. The AI decision system in step four may include reinforcement learning or decision tree algorithms for automatically formulating and executing an optimal data asset management policy based on the status and environment of the data asset.
In the data asset management method provided by the invention, the processing process of signals and data can be described in detail as follows:
1. planning assets and defining asset types, and defining element information: this is the first step of data processing and is also the most important step. In this step, a clear plan is first made for the data asset and the type of data asset is determined. For example, the data assets include user behavior data, transaction data, product information, and the like. Then, it is necessary to clarify the element information of the data asset, which includes the source of the data, the attribute of the data, the quality of the data, and the like.
2. Collecting and synchronizing data assets: in this step, it is necessary to collect data assets from a variety of different sources and synchronize these data assets. The data collection may be performed in various ways, such as by obtaining data through an API interface, reading data from a database, or collecting data directly from the user side. Data synchronization is a critical step in ensuring that all data assets are up-to-date and most accurate. The data synchronization includes operations such as updating, deleting, inserting, etc. of the data.
3. Integrating the association relationship and constructing a knowledge graph: this step is the core step of the data processing. In this step, a knowledge graph of the data asset needs to be constructed by analyzing the association relationship between the data assets. Knowledge graph is a technique for representing and processing complex data association relationships, which can help us understand the association relationships between data assets, thereby better managing the data assets. The knowledge graph construction is realized through various data mining technologies, such as association rule mining, cluster analysis, classification analysis and the like.
4. Realizing data asset management: the final step is to use the constructed knowledge-graph to implement data asset management. The goals of data asset management include improving the utilization of data assets, ensuring the security of data assets, improving the quality of data assets, and the like. These objectives can be achieved more effectively by knowledge-graph.
The above is a detailed process of processing signals and data in such a data asset management method.
As shown in fig. 2, the data asset management method based on the knowledge graph provided by the embodiment of the invention defines the data asset management category, defines the data asset management elements, explores the association relationship of the data asset, combines the knowledge graph technology to realize the association and integration of multi-source and multi-form data assets, constructs the data asset graph, grasps the whole appearance of the data asset, provides the traceability and rechecking capability for the data asset management, and solves the problem of the data black box in the data asset process.
The data asset management methodology provided by the embodiment of the invention is characterized in that metadata information acquisition tools adopted in a data asset acquisition system, construction of a knowledge graph and technical realization thereof are all technical key points.
The core of the embodiment of the invention is to integrate the data assets of the system nanotubes, construct an asset association relationship map, grasp the data asset context overview and improve the asset carding efficiency.
According to the embodiment of the invention, data asset planning work is carried out according to industry business attributes, the data asset management category is defined, the data asset granularity is defined, the data asset types are divided, asset attribute information is perfected, and asset source targets are defined; based on the overall planning of the early stage of data asset management, corresponding data assets are collected and synchronized from various source channels, the association relationship of the data assets is explored through technical principles and business principles, and a multi-form knowledge graph of the data assets is constructed by combining the technical capability of a graph engine, so that the unified integration of multi-element data assets is realized.
The data asset management system provided by the embodiment of the invention comprises:
the data asset planning module is used for carrying out data asset planning work according to the industry business attribute, defining the data asset management category and defining the data asset granularity;
the asset type definition module is used for dividing the data asset types, perfecting asset attribute information and defining asset source targets;
the data asset acquisition module is used for acquiring and synchronizing corresponding data assets from various source channels based on overall planning of the data asset management earlier stage;
the association relation analysis module is used for analyzing the association relation of the data asset through a technical principle and a business principle;
the knowledge graph construction module is used for constructing a data asset association relationship graph, determining the data asset context overall view and realizing data asset management.
The intelligent full-link asset blood-edge analysis, namely the full-link data tracing from the data source, the physical table and the label to the business application, enables the source and the flow direction of the data to be traceable, and provides necessary basis and guidance for the use and the update of the data. The multi-element asset map system is designed based on the graph engine technology, so that the problems of pain points of unknown data resources, uncontrollable data quality, uncontrollable data relationship and unclear data venation in actual business are effectively solved, a complete asset link relationship is provided for asset users, the blood relationship and influence of data assets are comprehensively clarified, and guidance is provided for subsequent business decisions.
In an actual business scene, enterprises perform association mapping on technical metadata, business metadata, management metadata and operation metadata generated in the production process of the data asset by management of the data asset, and the technical metadata, the business metadata, the management metadata and the operation metadata are presented to a data asset manager in a visual map mode.
The following are two specific application examples:
example 1: financial institution data asset management
Financial institutions often have a large number of data assets, including customer information, transaction records, risk assessment, and the like. In this case, the present invention applies the above-described data asset management method.
Step one: planning an asset and defining asset types. In this example, the property type may include customer information (e.g., name, age, gender, occupation, etc.), transaction records (e.g., transaction time, transaction amount, transaction type, etc.), and risk assessment (e.g., credit score, loan breach probability, etc.). Element information is explicitly defined, and the information is used for establishing a data asset association relationship graph.
Step two: data assets are collected and synchronized. Financial institutions collect and generate large amounts of data each day that need to be collected and updated synchronously.
Step three: and integrating the association relationship and constructing a knowledge graph. For example, fraud or credit risk may be found by analyzing transaction records and customer information. Through the knowledge graph, the relationships between the data assets can be better understood and more accurate decisions made accordingly.
Example 2: e-commerce platform data asset management
The electronic commerce platform has multi-source and multi-form data assets, such as user behavior data, commodity information, transaction data and the like. The invention applies to the data asset management method described above.
Step one: planning an asset and defining asset types. In this example, the asset types may include user behavior data (e.g., browsing history, purchase history, click-through rate, etc.), merchandise information (e.g., merchandise descriptions, prices, sales, etc.), and transaction data (e.g., transaction time, transaction amount, transaction status, etc.). Element information is explicitly defined, and the information is used for establishing a data asset association relationship graph.
Step two: data assets are collected and synchronized. Each time a user acts on the e-commerce platform, new data is generated that needs to be collected and updated synchronously.
Step three: and integrating the association relationship and constructing a knowledge graph. For example, by analyzing user behavior data and merchandise information, accurate recommendations can be made for user preferences. Through the knowledge graph, the relationship between the data assets can be better understood, and more accurate recommendations can be made accordingly.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (9)
1. A data asset management method, the data asset management method comprising: integrating data assets of the system nanotubes, defining data asset management elements by defining a data asset management category, and determining a data asset association relationship; and combining knowledge graph technology to realize multi-source and multi-form data asset association integration, constructing a data asset association relationship graph and determining the data asset context.
2. The data asset management method of claim 1, wherein the data asset management method comprises the steps of:
step one, planning assets and defining asset types, and defining element information;
step two, collecting data assets and synchronizing the data assets;
and thirdly, integrating the association relationship, constructing a knowledge graph, and realizing data asset management.
3. The method for managing data assets according to claim 2, wherein in said step two, data assets are collected by using a metadata information collection tool; the data asset management method further comprises: carrying out data asset planning work according to industry service attributes, defining a data asset management category, and defining data asset granularity; and dividing the data asset types, perfecting asset attribute information and defining asset source targets.
4. The data asset management method of claim 1, wherein the data asset management method further comprises: based on the overall planning of the earlier stage of data asset management, corresponding data assets are collected and synchronized from all source channels, and the association relationship of the data assets is analyzed through technical principles and business principles; the data asset management method further comprises: and constructing a polymorphic knowledge graph of the data asset by combining the technical capability of the graph engine, so as to realize unified integration of the multi-element data asset.
5. The method of claim 1, wherein in step two, the collection of the data asset may obtain the data through an API interface, read the data from a database, or collect the data directly from the client; synchronization of data assets includes update, delete, insert operations of data; in the third step, the knowledge graph is constructed by various data mining technologies; in step four, the objectives of data asset management include improving the utilization of the data asset, ensuring the security of the data asset, and improving the quality of the data asset.
6. The method of claim 2, wherein in step four, the multi-source multi-modal data asset association integration is implemented by defining a data asset management category, defining data asset management elements, and determining data asset association relationships, a data asset association relationship map is constructed, and a data asset context overview is determined.
7. A data asset management system applying the data asset management method of any one of claims 1 to 6, characterized in that the data asset management system comprises:
the data asset planning module is used for carrying out data asset planning work according to the industry business attribute, defining the data asset management category and defining the data asset granularity;
the asset type definition module is used for dividing the data asset types, perfecting asset attribute information and defining asset source targets;
the data asset acquisition module is used for acquiring and synchronizing corresponding data assets from various source channels based on overall planning of the data asset management earlier stage;
the association relation analysis module is used for analyzing the association relation of the data asset through a technical principle and a business principle;
the knowledge graph construction module is used for constructing a data asset association relationship graph, determining the data asset context overall view and realizing data asset management.
8. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the data asset management method of any one of claims 1 to 6.
9. An information data processing terminal for implementing the data asset management system of claim 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311297223.4A CN117573930A (en) | 2023-10-09 | 2023-10-09 | Data asset management method, system, medium, equipment and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311297223.4A CN117573930A (en) | 2023-10-09 | 2023-10-09 | Data asset management method, system, medium, equipment and terminal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117573930A true CN117573930A (en) | 2024-02-20 |
Family
ID=89890610
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311297223.4A Pending CN117573930A (en) | 2023-10-09 | 2023-10-09 | Data asset management method, system, medium, equipment and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117573930A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119918065A (en) * | 2025-04-02 | 2025-05-02 | 上海市大数据中心 | A data risk management system and method based on large model |
| CN120110785A (en) * | 2025-04-03 | 2025-06-06 | 浙商银行股份有限公司 | Full-link network mapping recognition and construction system and method based on knowledge graph |
-
2023
- 2023-10-09 CN CN202311297223.4A patent/CN117573930A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119918065A (en) * | 2025-04-02 | 2025-05-02 | 上海市大数据中心 | A data risk management system and method based on large model |
| CN120110785A (en) * | 2025-04-03 | 2025-06-06 | 浙商银行股份有限公司 | Full-link network mapping recognition and construction system and method based on knowledge graph |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Golfarelli et al. | A model-driven approach to automate data visualization in big data analytics | |
| Phillips-Wren et al. | Business analytics in the context of big data: A roadmap for research | |
| Elgendy et al. | Big data analytics: a literature review paper | |
| US7574379B2 (en) | Method and system of using artifacts to identify elements of a component business model | |
| Maier et al. | Towards a big data reference architecture | |
| CN117573930A (en) | Data asset management method, system, medium, equipment and terminal | |
| Dhyani et al. | Big data analytics using Hadoop | |
| EP2427861A2 (en) | Business information and innovation management | |
| Li et al. | Design and implementation of intelligent traffic and big data mining system based on internet of things | |
| Grander et al. | Big data as a value generator in decision support systems: a literature review | |
| Nandi et al. | Data Science fundamentals and practical approaches: understand why data science is the next | |
| Zhang | Evaluation and analysis of human resource management mode and its talent screening factors based on decision tree algorithm | |
| Kaufmann et al. | SQL and NoSQL Databases | |
| US20200201866A1 (en) | Attribute annotation for relevance to investigative query response | |
| Georgieva-Trifonova et al. | Application of semantic web technologies for supporting customer relationship management: A systematic literature review | |
| Dill | Big data | |
| Raisinghani et al. | From big data to big insights: A synthesis of real-world applications of big data analytics | |
| Hunker et al. | A systematic classification of database solutions for data mining to support tasks in supply chains | |
| Otten et al. | Towards decision analytics in product portfolio management | |
| Dibouliya | Review on: Modern Data Warehouse & how is it accelerating digital transformation | |
| Liyanagamage et al. | Descriptive analytics methods in big data: a systematic literature review | |
| CN109716366A (en) | System and method for automated extraction and visualization of knowledge about enterprise technology, people, and business functions | |
| Kim et al. | A survey of big data technologies and how semantic computing can help | |
| Madaan et al. | Big data analytics: A literature review paper | |
| Kaufmann et al. | Database management |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |