CN111078783A - Data management visualization method based on supervision and protection - Google Patents
Data management visualization method based on supervision and protection Download PDFInfo
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- CN111078783A CN111078783A CN201911104173.7A CN201911104173A CN111078783A CN 111078783 A CN111078783 A CN 111078783A CN 201911104173 A CN201911104173 A CN 201911104173A CN 111078783 A CN111078783 A CN 111078783A
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- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
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Abstract
The invention discloses a data management visualization method based on supervision and protection, which comprises the following steps of receiving a data processing request through a third-party service interface; performing response processing on target data according to the data processing request and extracting an index; carrying out visualization processing on the indexes through a visualization technology; the visual index monitoring protection is realized, the visual index is displayed, the condition of a data management project can be directly judged through the presentation of the data management visual content, and the monitoring protection is provided while the visualization is realized.
Description
Technical Field
The invention relates to the technical field of data supervision and protection and data visualization, in particular to a data management visualization method based on supervision and protection.
Background
Government departments, internet enterprises, and large group enterprises have accumulated and precipitated a large amount of data resources. China has become one of the countries with the largest data generation and accumulation amount and the most abundant data types, and data has become the first resource from the perspective of national strategy and urban strategy. The data management platform establishes a global data standard system through means of data standard management, data quality management, metadata management, data supervision and protection and the like, provides management of a data full life cycle for governments or enterprises through data asset visualization and quality closed-loop management, improves data quality and exerts data value. The data governance platform needs to provide visual display and provide supervision protection while visually displaying.
Disclosure of Invention
In view of the above problems, the present invention provides a data governance visualization method based on supervisory protection, which provides a visualization display of a data governance platform and supervisory protection for data, and in order to achieve the above object, an embodiment of the present invention provides a data governance visualization method based on supervisory protection, including:
receiving a data processing request through a third-party service interface; performing response processing on target data according to the data processing request and extracting an index; carrying out visualization processing on the indexes through a visualization technology; and (5) supervision and protection are carried out on the visual indexes, and the visual indexes are displayed.
Further, the response processing of the target data according to the data processing request includes data standardization processing and data quality processing.
Further, the extracting indexes comprise indexes for extracting data of the data source according to the definition of the database type of the data source; defining indexes for extracting data standard contents according to the data standard; defining an index for extracting data quality content according to a data quality analysis result; and defining indexes for extracting the metadata according to the treatment result of the metadata.
Further, the target data includes data source data and metadata.
Further, the supervision and protection on the visual indexes comprise data grading management, data authorization management and data desensitization management.
Further, the data hierarchical management is to allocate data according to data authority, the data authorization management is to allocate data authority, and the data desensitization management is to desensitize data.
Further, the data desensitization management comprises dividing the data according to service objects of the data or according to data service users; making desensitization rules for the partitioned data; and (4) formulating desensitization tasks, discovering sensitive data and formulating desensitization strategies.
Furthermore, the desensitization strategy making comprises data masking on sensitive data, encryption on the sensitive data, security audit and log audit.
The embodiment of the invention provides a data management visualization method based on supervision and protection, which comprises the steps of receiving a data processing request through a third-party service interface; performing response processing on target data according to the data processing request and extracting an index; carrying out visualization processing on the indexes through a visualization technology; monitoring and protecting the visual indexes, and displaying the visual indexes; the condition of a data management project can be directly judged through the presentation of the data management visual content, supervision protection is provided during visualization, and sensitive data formulation and data desensitization management are provided.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 shows a schematic flow chart of a data governance visualization method based on supervisory protection.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
In the description and claims of the present invention and in some of the flows described in the above drawings, a plurality of operations are included in a specific order, but it should be clearly understood that these operations may be executed out of the order they appear herein or in parallel, and it should be noted that "first", "second", etc. are described herein for distinguishing different messages, devices, modules, etc. without representing a sequential order, and without limiting "first" and "second" to be different types.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With reference to fig. 1, a data processing request is received through a third party service interface; performing response processing on target data according to the data processing request and extracting an index; carrying out visualization processing on the indexes through a visualization technology; and (5) supervision and protection are carried out on the visual indexes, and the visual indexes are displayed. The visualization technology is a theory, a method and a technology which convert data into graphs or images to be displayed on a screen by using computer graphics and image processing technology and carry out interactive processing. The method relates to a plurality of fields of computer graphics, image processing, computer vision, computer aided design and the like, and becomes a comprehensive technology for researching a series of problems of data representation, data processing, decision analysis and the like.
And the response processing of the target data according to the data processing request comprises data standardization processing, data quality processing and metadata governance.
The standard data processing is to solve the problem of inconsistent data calibers of different departments: the meaning, the representation mode and the code of the service data are not uniform, and the credibility of the data is low; the shared data among different services can not be effectively shared, and the scientificity of management decision is influenced.
Can be managed by standards; managing a business object; data standard mapping; creating a standard execution task; a standard execution module; task analysis enables standardization of data.
Obtaining a data quality analysis result and data quality related content through data quality processing, wherein the data quality processing comprises data quality problem finding and data quality problem management, and the data quality problem finding comprises associating a data quality rule with an acquired data object to obtain a quality rule packet; creating an inspection task according to the data quality rule of the quality rule packet, and inserting the problem data into a formulated problem database by executing the inspection task; and (4) counting the data quality problems in the problem database and outputting a data quality problem inspection report. Utilize the fine solution of quality rule package along with data quality problem discovery require under the more and more complicated condition of higher and more, data quality rule also is difficult to the hidden data quality problem of accurate discovery, formulate and reference data's use through more accurate rule, let the data quality problem everywhere stealthy.
Managing data quality problems, including making a data quality monitoring and checking scheme and monitoring and checking the data quality; formulating a data quality rule base; performing data quality control at regular time according to the data quality rule base to obtain a data quality problem; managing data quality problems; and evaluating the data quality. Under the drive of the closed-loop management, new treatment requirements are continuously generated for the data quality, and the quality problem is continuously solved, so that the data quality is continuously improved.
The method has the advantages that highly integrated metadata is achieved through metadata governance, metadata storage is conducted according to unified standards, a global data map is formed by finding relationships among data, the relationship of the data blood relationship is rapidly located according to the analysis of the blood relationship of the metadata in the map, the root cause of quality problems is found, and the purpose of rapid verification is achieved.
The metadata management comprises the steps of establishing a meta-model, and performing abstraction on a source system to establish the meta-model, wherein the meta-model defines an object to be acquired in the source system; and managing metadata, namely acquiring metadata of a source system in real time according to the acquired objects defined by the meta-model, and managing, analyzing and applying the metadata of the meta-model.
The extraction indexes comprise indexes for extracting data of the data source according to the definition of the database type of the data source; defining indexes for extracting data standard contents according to the data standard; defining an index for extracting data quality content according to a data quality analysis result; and defining indexes for extracting the metadata according to the treatment result of the metadata.
The target data includes data source data and metadata.
The supervision and protection on the visual indexes comprise data grading management, data authorization management and data desensitization management.
The data hierarchical management is to distribute data according to data authority, the data authorization management is to distribute data authority, and the data desensitization management is to desensitize data.
The data desensitization management comprises dividing data according to service objects of the data or according to data service users; making desensitization rules for the partitioned data; and (4) formulating desensitization tasks, discovering sensitive data and formulating desensitization strategies.
The desensitization strategy formulation comprises data masking on sensitive data, encryption on the sensitive data, security audit and log audit.
With the arrival of the big data era, the mining of the business value of the big data and the accurate positioning of the user, the huge business value hidden in the big data is gradually mined, but the huge challenge, namely the protection of personal privacy information, is brought. Personal information and personal behaviors (such as position information, consumption behaviors and network access behaviors) and the like are privacy of people and sensitive information which is concerned about, and how to protect the privacy information of people on the basis of large data value mining is also a difficult problem which needs to be solved for data desensitization.
Data desensitization management includes dividing data according to service objects of the data or according to data service users; desensitization work is first classified according to the object of the data service, classifying users likely to use the data service in the future into: government agencies, institutions, enterprises, social teams, academic research institutions, pioneers, and the like. The desensitization requirements of various service objects for corresponding application data are different, so that the sensitive data need to be divided according to the data service objects.
Desensitization rules are formulated for the divided data, and corresponding standards and mechanisms are generated according to which service objects need to be subjected to data desensitization, and according to which technical means to perform desensitization on what kind of data. Through the customization of the rules (the regular rules or the SQL rules can be made), the desensitization of the data is ensured to be hierarchical, and different desensitization mechanisms are adopted for different data and different consumer groups;
and (3) formulating desensitization tasks to find sensitive data, and after desensitization rules are set, formulating desensitization tasks, including configuration of task time, period, task objects and the like, and carrying out automatic processing on data desensitization.
A desensitization strategy is formulated, and according to different scenes, the desensitization strategy comprises the following steps: data masking, data encryption, security audit and log audit.
Data masking: for sensitive data needing to be presented to the outside or at a client, the data needs to be masked according to requirements, and at present, the data is mainly masked in a way shown in table 1.
TABLE 1
Sensitive data are encrypted, storage encryption is carried out on data with higher sensitive level or without opening to the outside at all, the encryption is in an envelope form, and public and private keys are used for carrying out encryption on the data.
And safety audit, namely collecting the database operation logs by using a bypass technology, and performing risk early warning on the operation behaviors of the user according to log analysis (SQL analysis).
Log auditing, including application system logs: and applying log records to record all behaviors of logging in a system, accessing, modifying, adding and deleting data. At least comprises the following components: username, IP, MAC address, time, event. Each department configures an application system auditor to audit application system logs regularly, and abnormal behaviors are found in time; and (4) regularly counting the login condition of the application system, and temporarily stopping the account of the system when the account does not log in for three months, and opening the account when the account needs to be used.
The log management is also included, the application system log can store the system message into a file or send the system message to a log auditing system in a targeted mode, and by analyzing and summarizing the content of the system log, an administrator can know the current state of the system, check the security vulnerability, and check when and when the administrator tries to violate the security policy.
The data source mainly comprises a relational database, a file type database, a big database, an interface type database and other databases, and the data become the acquired objects.
Data standardization is the process by which an enterprise or organization standardizes the definition, organization, supervision, and protection of data; there are many methods for data standardization, and the common raw data, such as "minimum-maximum standardization", "Z-score standardization" and "normalization by decimal scale", are all converted into non-dimensionalized index values, i.e. each index value is in the same number level, and can be comprehensively evaluated and analyzed. Because different services in an organization have different definitions, different data descriptions and different data formats for the same data, shared exchange of the data is difficult, and therefore the data needs to be standardized to obtain standard data. The data standard is obtained according to a standard system of organization and construction, the data standard comprises data standard packets containing different data standards, data acquisition is carried out according to the standard of the standard packets to obtain data standard contents, and after visualization, a client is helped to look up and compare the standard contents in different periods; providing standard global inquiry, showing conditions of standard content, subscription, detection, evaluation, standard coverage and the like; helping the customer to see clearly the standard operating conditions within the organization.
The visualized data standard data content comprises visualized data standard difference analysis results (front-end analysis is evaluated through difference analysis, and standard execution difference is compared to identify specific conditions which do not meet data standard definition).
The data quality content comprises data quality monitoring content, data quality evaluation content, data quality comprehensive report content, data quality problem management content and the like.
The data quality content is obtained based on data quality check, and key data items of the data are checked according to check rules, including but not limited to null value check, repeated check, format check, reference check, value range check, consistency check, logic check, relationship check and the like.
And (4) checking a null value: refers to checking whether a column of data has an empty data item.
And (4) repeated checking: refers to checking whether two identical values exist for the same entity attribute.
And (3) format verification: which means checking whether the data format meets the standard.
And (5) reference checking: refers to checking whether a certain data value is present in the data.
Checking a value range: which refers to checking whether the data value meets the value range specified by the standard.
And (3) consistency checking: it means to check whether the values of the same entity in the two tables are consistent.
Logic verification: which refers to checking whether the data value meets the business logic requirements and common sense logic requirements.
And (3) relation checking: and checking whether the main foreign key association relationship of the data exists.
Obtaining a data quality problem according to the data quality measurement index, wherein the defining the data quality measurement index comprises: the integrity, the consistency, the repeatability, the correctness, the compliance and the relevance are used for formulating related quality detection rules according to the quality index meanings, if the integrity is used for detecting four aspects of entity missing, attribute missing, record missing and field value missing, for data which does not accord with the rules, the data is considered to have integrity problems, and the data is classified into data with data quality problems.
Integrity: the method refers to whether referential integrity exists or is consistent among data in a data warehouse, and the integrity is the content of detecting entity missing, attribute missing, record missing and field value missing.
Repeatability: refers to the measure of which data is duplicate data or which attributes of the data are duplicate.
Consistency: refers to the correctness of the table data (semantics). The purpose is to detect inconsistencies or conflicts in the data.
Standardization: which refers to whether the data is stored in a uniform format.
Correctness: which refers to whether the data is properly embodied on a verifiable data source.
Relevance: indicating which associated data is missing or not indexed.
And (3) timeliness: which refers to whether the data is valid at the required time.
And establishing a data quality evaluation model to carry out quantitative diagnosis and evaluation on the data quality.
The management of the metadata comprises the steps of acquiring the relationship among the data through uniformly acquiring, storing, integrating and analyzing the metadata, and providing data retrieval, a data map and metadata analysis, wherein the metadata management result comprises a metadata acquisition result, a storage result, an integration result, an analysis result, a relationship result among the data, a data retrieval result, a data map and a metadata analysis result.
The invention provides a data management visualization method based on supervision and protection, which comprises the following steps of receiving a data processing request through a third-party service interface; performing response processing on target data according to the data processing request and extracting an index; carrying out visualization processing on the indexes through a visualization technology; and (5) supervision and protection are carried out on the visual indexes, and the visual indexes are displayed. The data management platform is visually displayed, and supervision protection is provided while the data management platform is visually displayed.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic or optical disk, or the like.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by hardware that is instructed to implement by a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
While the data analysis method and system provided by the present invention have been described in detail, those skilled in the art will appreciate that the present invention is not limited to the above embodiments, and that various modifications, additions, substitutions, and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims.
Claims (8)
1. A data governance visualization method based on supervision and protection is characterized by comprising the following steps:
receiving a data processing request through a third-party service interface;
performing response processing on target data according to the data processing request and extracting an index;
carrying out visualization processing on the indexes through a visualization technology;
and (5) supervision and protection are carried out on the visual indexes, and the visual indexes are displayed.
2. The supervisory protection-based data governance visualization method according to claim 1, wherein the responsive processing of target data according to data processing requests comprises data normalization processing, data quality processing, and metadata governance.
3. The supervisory protection-based data governance visualization method according to claim 2, wherein extracting indicators comprises defining indicators for extracting data from a data source according to a database type of the data source; defining indexes for extracting data standard contents according to the data standard; defining an index for extracting data quality content according to a data quality analysis result; and defining indexes for extracting the metadata according to the treatment result of the metadata.
4. The supervisory protection-based data governance visualization method of claim 3, wherein the target data comprises data source data and metadata.
5. The supervisory protection-based data governance visualization method according to claim 4, wherein the supervisory protection of visualization indicators includes data staging management, data authorization management, and data desensitization management.
6. The supervisory protection-based data governance visualization method according to claim 5, wherein the data hierarchy management is assigning data according to data authority, the data authority management is assigning data authority, and the data desensitization management is desensitizing data.
7. The supervisory protection-based data governance visualization method according to claim 6, wherein the data desensitization management comprises partitioning data according to its serving object or according to its serving user; making desensitization rules for the partitioned data; and (4) formulating desensitization tasks, discovering sensitive data and formulating desensitization strategies.
8. The supervisory protection-based data governance visualization method according to claim 7, wherein said formulating a desensitization policy includes performing data masking on sensitive data, encrypting sensitive data, security auditing, and log auditing.
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| Publication number | Priority date | Publication date | Assignee | Title |
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