CN111078695B - Method and device for calculating association relation of metadata in enterprise - Google Patents
Method and device for calculating association relation of metadata in enterprise Download PDFInfo
- Publication number
- CN111078695B CN111078695B CN201911206472.1A CN201911206472A CN111078695B CN 111078695 B CN111078695 B CN 111078695B CN 201911206472 A CN201911206472 A CN 201911206472A CN 111078695 B CN111078695 B CN 111078695B
- Authority
- CN
- China
- Prior art keywords
- metadata
- relation
- data flow
- relationship
- mapping
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000013507 mapping Methods 0.000 claims abstract description 94
- 230000002776 aggregation Effects 0.000 claims abstract description 55
- 238000004220 aggregation Methods 0.000 claims abstract description 55
- 238000010606 normalization Methods 0.000 claims abstract description 39
- 230000008569 process Effects 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 abstract description 10
- 238000004458 analytical method Methods 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 17
- 238000004891 communication Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 6
- 238000005206 flow analysis Methods 0.000 description 5
- 238000012216 screening Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000007726 management method Methods 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000000977 initiatory effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010223 real-time analysis Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2291—User-Defined Types; Storage management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24553—Query execution of query operations
- G06F16/24554—Unary operations; Data partitioning operations
- G06F16/24556—Aggregation; Duplicate elimination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The disclosure relates to a method and a device for calculating metadata association relations in an enterprise. Wherein the method comprises the following steps: the method comprises the steps of establishing a mapping relation between a plurality of selected metadata and lower metadata with an aggregation relation, obtaining a data flow relation between the lower metadata, correspondingly setting the data flow relation between the lower metadata to the plurality of selected metadata according to the mapping relation between the plurality of selected metadata and the lower metadata with the aggregation relation, correspondingly setting the data flow relation between assets represented by the lower metadata to the upper metadata through normalization mapping according to the aggregation relation and corresponding setting two operations according to the data flow relation, and expressing the association relation between the plurality of selected metadata through the data flow relation, thereby avoiding analysis of all asset data, consuming less resources, having high calculation speed and being beneficial to users to quickly obtain macroscopic information of the association relation of the metadata in an enterprise.
Description
Technical Field
The present disclosure relates to the field of computers, and in particular, to a method and apparatus for calculating metadata association in an enterprise.
Background
Metadata within an enterprise is data that describes data within the enterprise.
Metadata in enterprises is various in variety and form, and needs to be managed uniformly by means of metadata management products. For some enterprise managers, the attention scope is too wide, the detailed information is too large and can not effectively meet the needs of the users, and the users hope to acquire the information of the macro level of the metadata association relationship in the enterprise through metadata management products. However, analyzing data between all assets to determine metadata associations is overly complex, making metadata management products a time-consuming and difficult process to obtain metadata associations within an enterprise.
Therefore, how to enable metadata management products to quickly calculate metadata association relationships in enterprises is an urgent problem to be solved.
Disclosure of Invention
The present disclosure provides a method and an apparatus for calculating an intra-enterprise metadata association relationship, so as to achieve the purpose of rapidly calculating an intra-enterprise metadata association relationship.
To achieve the above object, the present disclosure provides a method for calculating metadata association in an enterprise. The method comprises the following steps: establishing a mapping relation between the selected metadata and the lower metadata with the aggregation relation; acquiring a data flow direction relation between the lower metadata; and correspondingly setting the data flow relation among the lower metadata to the selected metadata according to the mapping relation between the selected metadata and the lower metadata with the aggregation relation.
Optionally, the establishing a mapping relationship between the selected plurality of metadata and the metadata having an aggregation relationship includes: respectively carrying out normalization mapping processing on the selected metadata; the normalization mapping process includes: taking the selected metadata as root metadata; judging whether direct subordinate metadata with aggregation relation with the root metadata exists or not; if so, establishing an association relationship between the selected metadata and the directly lower metadata, and returning the directly lower metadata serving as updated root metadata to the step of judging whether the directly lower metadata with an aggregation relationship with the root metadata exists; and if not, ending the normalization mapping process.
Optionally, the method further comprises: and displaying the data flow relation among the selected metadata based on the html page.
Optionally, the presenting the data flow relation between the selected plurality of metadata based on the html page includes: the selected plurality of metadata is presented based on an html page, and a data flow relationship line is used to represent a data flow relationship between the selected plurality of metadata. The method further comprises the steps of: responsive to a user clicking the data stream relationship line, taking respective directly lower metadata of metadata at both ends of the data stream relationship line as updated selected plurality of metadata; and entering the step of establishing the mapping relation between the selected metadata and the lower metadata with the aggregation relation.
The present disclosure provides an apparatus for computing metadata associations within an enterprise. The device comprises: and the normalization mapping module is configured to establish a mapping relation between the selected plurality of metadata and the lower metadata with the aggregation relation. And the flow direction acquisition module is configured to acquire the data flow direction relation between the lower metadata. And the flow direction correspondence module is configured to correspondingly set the data flow direction relationship among the lower metadata to the selected metadata according to the mapping relationship between the selected metadata and the lower metadata with the aggregation relationship.
Optionally, the normalization mapping module includes: and the mapping initial sub-module is configured to respectively start the root setting sub-module for the selected metadata so as to perform normalization mapping processing. A root setting sub-module configured to take the selected metadata as root metadata. And the judging sub-module is configured to judge whether the directly lower metadata with the aggregation relation with the root metadata exists or not. The normalization mapping sub-module is configured to establish an association relationship between the selected metadata and the directly lower metadata if the judgment sub-module judges that the metadata exist, take the directly lower metadata as updated root metadata, and trigger the judgment sub-module to execute again; and if not, ending the normalization mapping process.
Optionally, the method further comprises: and the display module is configured to display the data flow relation among the selected metadata based on the html page.
Optionally, the presentation module is configured to present the selected plurality of metadata based on an html page and to represent a data flow relationship between the selected plurality of metadata using a data flow relationship line. The apparatus further comprises: and the drill-down response module is configured to respond to clicking the data flow relation line by a user, take the respective metadata of the metadata at the two ends of the data flow relation line as updated selected metadata, and re-trigger the normalization mapping module to execute.
The present disclosure provides a computer-readable storage medium having a computer program stored thereon. The program, when executed by a processor, implements the steps of the method for computing enterprise metadata associations provided in any of the embodiments of the present disclosure.
The present disclosure provides an electronic device, comprising: a memory having a computer program stored thereon; and a processor, configured to execute the computer program in the memory, to implement the steps of the method for calculating the association relationship of enterprise metadata provided in any embodiment of the disclosure.
According to the technical scheme, the mapping relation between the selected metadata and the lower metadata with the aggregation relation is established, normalization mapping according to the aggregation relation is achieved, the data flow relation among the lower metadata is obtained, the data flow relation among the lower metadata is correspondingly arranged among the selected metadata according to the mapping relation between the selected metadata and the lower metadata with the aggregation relation, therefore, the data flow relation among assets represented by the lower metadata is correspondingly arranged on the upper metadata through normalization mapping according to the aggregation relation and the relation according to the data flow relation, the association relation among the selected metadata is represented through the data flow relation, analysis of all asset data is avoided, the calculated amount is greatly reduced, the consumed resources are low, the calculation speed is high, the information of the macro-scale metadata association relation in an enterprise is convenient for a user to perform macro data flow analysis.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
FIG. 1 is a flowchart illustrating a method of computing metadata associations within an enterprise according to an exemplary embodiment of the present disclosure.
Fig. 2 is a schematic diagram of an enterprise metadata structure shown in accordance with an exemplary embodiment of the present disclosure.
FIG. 3a is a schematic diagram illustrating system metadata association according to an exemplary embodiment of the present disclosure.
Fig. 3b is a schematic diagram illustrating drill-down system association according to an exemplary embodiment of the present disclosure.
Fig. 3c is a schematic diagram illustrating drill-down database associations according to an exemplary embodiment of the present disclosure.
Fig. 3d is a schematic diagram illustrating drill-down data table associations according to an exemplary embodiment of the present disclosure.
Fig. 4 is a schematic diagram illustrating a metadata relationship determination process according to another exemplary embodiment of the present disclosure.
Fig. 5 is a block diagram illustrating an apparatus for calculating metadata associations within an enterprise according to an exemplary embodiment of the present disclosure.
Fig. 6 is a block diagram illustrating an apparatus for calculating metadata associations within an enterprise according to another exemplary embodiment of the present disclosure.
Fig. 7 is a block diagram of an electronic device, according to an example embodiment.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
FIG. 1 is a flowchart illustrating a method of computing metadata associations within an enterprise according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the method may include:
in step 110, a mapping relationship between the selected plurality of metadata and the metadata having an aggregation relationship at a lower level is established.
The establishment of the mapping relationship may be achieved by HashMap, for example. HashMap is a collection of record Key, value Key Value pairs. For example, for a mapping relationship between system metadata and database metadata that has an aggregate relationship, the mapping relationship may be established by recording the database metadata as Key and the system metadata as Value.
In the present disclosure, the lower metadata is metadata having a hierarchical relationship with the selected metadata. An aggregate relationship is a relationship that is aggregated by a hierarchy, e.g., system classification may aggregate systems, systems may aggregate databases, databases may aggregate data tables, and data tables may aggregate columns according to enterprise asset organization. Thus, in terms of aggregate hierarchical division, the structure of the intra-enterprise metadata from the upper level to the lower level may include, as shown in fig. 2: system class metadata, system metadata, database metadata, data table metadata, column metadata. The lower metadata of the system classification metadata comprises system metadata, database metadata, data table metadata and column metadata. The lower metadata of the system metadata includes database metadata, data table metadata, and column metadata. The lower metadata of the database metadata includes data table metadata and column metadata. The lower metadata of the data table metadata includes column metadata.
The system classification metadata is metadata describing system classification, and the system classification generally refers to classification of each system, such as business system classification, data warehouse system classification, report system classification, and the like. System classification metadata such as: reporting system classification metadata, business system classification metadata, and data warehouse system classification metadata. System metadata, which is metadata describing a system, typically refers to, for example, financial systems, CRM systems, OA systems, and the like. System metadata such as: financial ledger system metadata, financial reporting system metadata, CRM system metadata, and the like. Database metadata is metadata describing a database in a system. Data table raw data is metadata describing data tables in a database. Column metadata is metadata describing columns in a data table.
In summary, the selected plurality of metadata may include: selected multiple system category metadata, selected multiple system metadata, selected multiple database metadata, or selected multiple data table metadata. That is, the present disclosure may be used to calculate associations between system classification metadata to reveal panoramas of intra-enterprise system classification metadata, to calculate associations between system metadata to reveal panoramas of intra-enterprise system metadata, to calculate associations between database metadata to reveal panoramas of intra-enterprise database metadata, and to calculate associations between data table metadata to reveal panoramas of intra-enterprise data table metadata.
In step 120, a data flow relationship between the subordinate metadata is obtained.
Data flow relationships are used to describe the source or destination of data. For example, table a data in the database is synchronized from table B, and the relationship between table a and table B is the data flow relationship. For another example, the data flow relationship may be a column-to-column, table-to-view, or the like. Specifically, the data flow relation between the lower metadata can be screened from the database by utilizing the screening function of the database.
In step 130, according to the mapping relationship between the selected metadata and the metadata of the lower level having the aggregation relationship, the data flow relationship between the metadata of the lower level is correspondingly set between the selected metadata.
For example, after the data flow relationship between the lower metadata is obtained, the lower metadata may be replaced with the corresponding selected metadata having the mapping relationship in the data flow relationship between the lower metadata, and then the duplication removal is performed, so that the data flow relationship between the selected metadata may be obtained.
According to the technical scheme, the mapping relation between the selected metadata and the lower metadata with the aggregation relation is established, normalization mapping according to the aggregation relation is achieved, the data flow relation among the lower metadata is obtained, according to the mapping relation between the selected metadata and the lower metadata with the aggregation relation, the data flow relation among the lower metadata is correspondingly arranged among the selected metadata, therefore, the data flow relation among assets represented by the lower metadata is correspondingly arranged on the upper metadata through the normalization mapping according to the aggregation relation and the corresponding two operations of the data flow relation, the association relation among the selected metadata is represented through the data flow relation among the selected metadata, analysis of all asset data is avoided, the calculated amount is greatly reduced, the consumed resources are low, the calculation speed is high, the macro-scale information of the metadata association relation in an enterprise is facilitated to be quickly obtained, and macroscopic data flow analysis of users is facilitated.
Considering the difficulty and the calculation efficiency of the algorithm implementation, in an embodiment of the present disclosure, all the lower metadata with the aggregation relationship are mapped. Specifically, for example, the establishing a mapping relationship between the selected plurality of metadata and the lower metadata having the aggregation relationship may include: and respectively performing normalization mapping processing on the selected metadata.
The normalization mapping process includes: taking the selected metadata as root metadata; judging whether the direct subordinate metadata with the aggregation relation with the root metadata exists or not, for example, the direct subordinate metadata with the aggregation relation can be screened out from a database by utilizing the screening function of the database; if so, establishing a mapping relation between the selected metadata and the directly lower metadata, taking the directly lower metadata of the root metadata as updated root metadata, and returning to the step of judging whether the directly lower metadata with aggregation relation with the root metadata exists; and if not, ending the normalization mapping process.
In this embodiment, for the selected plurality of metadata, normalization mapping processing is performed on all the metadata of the lower level, and finally, correspondence of the data flow relationships is performed in a unified manner, so that the algorithm implementation difficulty is low and the calculation efficiency is high. In the step of mapping the lower metadata having the aggregation relation to the selected metadata, all the lower metadata having the aggregation relation may be mapped, or a part of the lower metadata having the aggregation relation may be mapped, according to the implementation environment. In the embodiment of mapping the lower metadata having the aggregation relationship, it is necessary to determine whether the lower metadata is sufficient to determine the data flow relationship between the selected plurality of metadata according to the mapping of the data flow relationship between the lower metadata.
In the following, a description is made in connection with a possible application scenario of the embodiments of the present disclosure. For example, as shown in the schematic diagram of the system metadata association in fig. 3a, the system metadata is in an application scenario of the metadata of the general ledger system and the metadata of the reporting system. The financial ledger system metadata aggregates the finance database metadata and the system reporting system metadata aggregates the report database metadata. The finish database metadata aggregates the replying table metadata and the report database metadata aggregates the person table metadata. The reployee table metadata has a data flow relationship to the person table metadata. According to the method of the embodiment of the disclosure, a mapping relation between the replayee table metadata and the financial ledger system metadata is established, a mapping relation between the person table metadata and the system reporting system metadata is established, and according to the mapping relation, the data flow direction relation from the replayee table metadata to the person table metadata is correspondingly set between the financial ledger system metadata and the system reporting system metadata, so that the data flow direction relation between the financial ledger system metadata and the system reporting system metadata is calculated.
In addition, the embodiment of the disclosure can also display the data flow relation among the selected metadata based on the html page. For example, the data flow relationship between the selected plurality of metadata may be exposed using the html 5-based GoJS. According to the embodiment of the disclosure, the data flow relation among the selected metadata is directly calculated through the memory, and the calculation result is displayed in the html page, so that real-time analysis can be realized, database entering and database fetching operations are not needed, a user can acquire information on a macroscopic level, namely the metadata association relation in an enterprise more quickly, and the macroscopic data flow analysis is more convenient for the user.
In order to facilitate the user to view the association details in the system for logging down, in an embodiment of the present disclosure, in a case that the selected metadata are displayed based on an html page, a data flow relationship line may be used to represent a data flow relationship between the selected metadata, and in response to the user clicking (for example, double clicking) on the data flow relationship line, the metadata at two ends of the data flow relationship line is used as updated selected metadata, and the step of establishing a mapping relationship between the selected metadata and the metadata having an aggregation relationship is entered, so that a specific data flow may be accurately located during logging down, and association details of the metadata for displaying the lower metadata are further refined.
For example, as shown in the schematic diagram of the drill-down system association shown in fig. 3b, when the user double-clicks the data flow relation line between two systems, the database metadata with the aggregation relation between the two systems is used as the selected metadata, and the association between the databases is displayed in a refined manner according to the mapping process and the data flow direction of the aggregation relation. As shown in the schematic diagram of the association of the drill-down databases in fig. 3c, when the user double-clicks the data flow relationship line between the two databases, the metadata of the data tables with the aggregation relationship with the two systems is used as the selected metadata, and the association between the data tables is displayed in a refined manner according to the mapping process and the data flow direction of the aggregation relationship. As shown in fig. 3d, when the user double clicks on the data flow relation line between two data tables, column metadata having an aggregate relation with the two tables is used as selected metadata, and the data flow relation between columns is directly displayed.
In order to make the method for calculating the metadata association relationship in the enterprise easier to understand, the following describes in detail a system metadata association relationship determination process schematic diagram shown in fig. 4. As shown in fig. 4, the method includes:
in step 401, it is determined whether or not the selected system metadata "system a", "system B", "system C", and "system D" include directly lower metadata having an aggregation relationship.
In step 402, if any, database metadata "database A1", "database A2", "database B1", "database C1", "database D2" having an aggregate relationship with "system a", "system B", "system C", and "system D", respectively, are screened out using the screening function of the database.
In step 403, according to the aggregate relationship, a mapping relationship between the system metadata "system a" and the database metadata "database A1" and the database A2 "is established, a mapping relationship between the system metadata" system B "and the database metadata" database B1 "is established, a mapping relationship between the system metadata" system C "and the database metadata" database C1 "is established, and a mapping relationship between the system metadata" system D "and the database metadata" database D1 "and the database D2" is established.
In step 404, it is determined whether or not the database metadata "database A1", "database A2", "database B1", "database C1", "database D2" include directly lower metadata having an aggregate relationship, respectively.
In step 405, if any, the data table metadata "data table A3", "data table A4", "data table B2", "data table C2", "data table D3", "data table D4" having an aggregate relationship with "database A1", "database A2", "database B1", "database C1", "database D2" respectively are screened out by the screening function of the database.
In step 406, according to the aggregation relationship, a mapping relationship between the system metadata "system a" and the data table metadata "data table A3" and "data table A4" is established, a mapping relationship between the system metadata "system B" and the data table metadata "data table B2" is established, a mapping relationship between the system metadata "system C" and the data table metadata "data table C2" is established, and a mapping relationship between the system metadata "system D" and the data table metadata "data table D3" and "data table D4" is established.
In step 407, a mapping relationship between the column metadata and the system metadata is established according to the aggregation relationship, which is not described herein.
In step 408, the data flow relationship between the lower metadata of the system metadata is obtained by using the screening function of the database, as shown in fig. 4, the data flow relationship from "data table A3" to "data table B2", the data flow relationship from "data table B2" to "data table D3", the data flow relationship from "database A1" to "database B1", the data flow relationship from "database B1" to "database D1", the data flow relationship from "data table A4" to "data table B2", and the data flow relationship from "database B1" to "database C1" are obtained.
In step 409, according to the mapping relationship between the metadata of the lower level and the metadata of the system, the data flow relationship from "data table A3" to "data table B2" is correspondingly set between "system a" and "system B", the data flow relationship from "data table B2" to "data table D3" is correspondingly set between "system B" and "system D", the data flow relationship from "database A1" to "database B1" is correspondingly set between "system a" and "system B", the data flow relationship from "database B1" to "database D1" is correspondingly set between "system B" and "system D", the data flow relationship from "data table A4" to "data table B2" is correspondingly set between "system a" and "system B", and the data flow relationship from "database B1" to "database C1" is correspondingly set between "system B" and "system C".
In step 410, the repeated data stream relationships are deduplicated to obtain the association relationship between the system metadata: the data flow direction relationship of "system A" to "system B", the data flow direction relationship of "system B" to "system C", and the data flow direction relationship of "system B" to "system D".
In the above technical solution, after the mapping result shown in step 407 is obtained through the normalization mapping process from step 401 to step 407, the association relationship between the system metadata is obtained through the data flow corresponding setting process from step 408 to step 410. It can be seen that, although in a large data center supporting platform construction project, for example, data service, a large data computing and storing platform, a problem library, a resource center, a data lake, each front-end processor and data association between each service system are complex, and these relations are scattered in codes of an ETL tool and a program, according to the method provided by the embodiment of the disclosure, metadata of the scattered systems can be mapped together according to an aggregation relation and a data flow direction relation, a macroscopic association relation is established, and the data flow direction relation is displayed in a graphical manner, so that a user can be helped to comb the association between the systems, macroscopic data flow direction association between the systems is displayed, and the user can find and solve abnormal association in time. For example, in a system metadata panorama, there is a data flow relationship between two systems, but in an actual business process, the data flow relationship should not exist. After viewing the system metadata panorama, the platform manager can find the abnormal data flow direction relationship, and can quickly locate the position of the abnormal relationship by utilizing a drill-down function, so that the platform manager can solve the corresponding problem as soon as possible.
Fig. 5 is a block diagram illustrating an apparatus for calculating metadata associations within an enterprise according to an exemplary embodiment of the present disclosure. As shown in fig. 5, the apparatus may include: a normalization mapping module 510, a flow direction obtaining module 520, and a flow direction correspondence module 530.
The normalization mapping module 510 may be configured to establish a mapping relationship between the selected plurality of metadata and the lower metadata that it has an aggregate relationship with.
The flow direction obtaining module 520 may be configured to obtain a data flow direction relationship between the lower metadata.
The flow direction correspondence module 530 may be configured to set a data flow direction relationship between the lower metadata to the selected plurality of metadata according to a mapping relationship between the selected plurality of metadata and the lower metadata having an aggregation relationship.
According to the technical scheme, the mapping relation between the selected metadata and the lower metadata with the aggregation relation is established, normalization mapping according to the aggregation relation is achieved, the data flow relation among the lower metadata is obtained, according to the mapping relation between the selected metadata and the lower metadata with the aggregation relation, the data flow relation among the lower metadata is correspondingly arranged among the selected metadata, therefore, the data flow relation among assets represented by the lower metadata is correspondingly arranged on the upper metadata through the normalization mapping according to the aggregation relation and the corresponding two operations of the data flow relation, the association relation among the selected metadata is represented through the data flow relation among the selected metadata, analysis of all asset data is avoided, the calculated amount is greatly reduced, the consumed resources are low, the calculation speed is high, the macro-scale information of the metadata association relation in an enterprise is facilitated to be quickly obtained, and macroscopic data flow analysis of users is facilitated.
Considering the difficulty and the calculation efficiency of the algorithm implementation, in an embodiment of the present disclosure, all the lower metadata with the aggregation relationship are mapped. Specifically, as shown in fig. 6, which is a block diagram of an apparatus for calculating metadata association in an enterprise according to another exemplary embodiment of the present disclosure, the normalization mapping module 510 may include: the mapping initiation sub-module 511, the root setting sub-module 512, the judgment sub-module 513, and the normalization mapping sub-module 514.
The mapping initiation sub-module 511 may be configured to turn on the root setting sub-modules for the selected plurality of metadata, respectively, for normalization mapping processing.
The root settings sub-module 512 may be configured to take the selected metadata as root metadata.
The determination sub-module 513 may be configured to determine whether there is directly lower metadata having an aggregate relationship with the root metadata.
The normalization mapping sub-module 514 may be configured to, if the determination sub-module determines that the metadata exists, establish an association relationship between the selected metadata and the directly lower metadata, and re-trigger the determination sub-module 513 to execute with the directly lower metadata as updated root metadata; and if not, ending the normalization mapping process. In this embodiment, for a plurality of metadata selected, normalization mapping processing is performed on all the metadata of the lower level, and finally mapping of the data flow direction is performed in a unified manner, so that the algorithm implementation difficulty is low and the calculation efficiency is high.
Optionally, as shown in fig. 6, a block diagram of an apparatus for calculating metadata association relationship in an enterprise according to another exemplary embodiment of the present disclosure, the apparatus may further include: the presentation module 540 may be configured to present the data flow relationship between the selected plurality of metadata based on the html page. According to the embodiment of the disclosure, the data flow relation among the selected metadata is directly calculated through the memory, and the calculation result is displayed in the html page, so that real-time analysis can be realized, database entering and database fetching operations are not needed, a user can acquire information on a macroscopic level, namely the metadata association relation in an enterprise more quickly, and the macroscopic data flow analysis is more convenient for the user.
To facilitate viewing of the association details within the user drill-down system, in one embodiment of the present disclosure, as shown in fig. 6, the presentation module 540 may be configured to present the selected plurality of metadata based on an html page and to use a data flow relationship line to represent a data flow relationship between the selected plurality of metadata. May further include: the drill-down response module 541 may be configured to, in response to a user clicking on the data flow relationship line, re-trigger execution of the normalization mapping module 510 by taking the respective immediately lower metadata of the metadata at both ends of the data flow relationship line as the updated selected plurality of metadata. By the embodiment, specific data flow directions can be precisely positioned by drill down, and associated details of lower metadata are further displayed in a refined mode.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 7 is a block diagram of an electronic device 700, according to an example embodiment. As shown in fig. 7, the electronic device 700 may include: a processor 701, a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700 to perform all or part of the steps in the method for calculating metadata association in an enterprise. The memory 702 is used to store various types of data to support operation on the electronic device 700, which may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, and so forth. The Memory 702 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 703 can include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 702 or transmitted through the communication component 705. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 705 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic device 700 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated ASIC), digital signal processors (Digital Signal Processor, abbreviated DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated DSPD), programmable logic devices (Programmable Logic Device, abbreviated PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described method of calculating metadata associations within an enterprise.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the method for computing metadata associations within an enterprise described above. For example, the computer readable storage medium may be the memory 702 including program instructions described above, which are executable by the processor 701 of the electronic device 700 to perform the method of calculating metadata associations within an enterprise described above.
In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described method of computing an intra-enterprise metadata association when executed by the programmable apparatus.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.
Claims (8)
1. A method for computing metadata associations within an enterprise, comprising:
establishing a mapping relation between the selected metadata and the lower metadata with the aggregation relation;
acquiring a data flow direction relation between the lower metadata;
according to the mapping relation between the selected metadata and the lower metadata with the aggregation relation, correspondingly setting the data flow relation among the lower metadata among the selected metadata;
the establishing a mapping relationship between the selected metadata and the lower metadata with the aggregation relationship comprises the following steps:
respectively carrying out normalization mapping processing on the selected metadata;
the normalization mapping process includes:
taking the selected metadata as root metadata;
judging whether direct subordinate metadata with aggregation relation with the root metadata exists or not;
if so, establishing an association relationship between the selected metadata and the directly lower metadata, and returning the directly lower metadata serving as updated root metadata to the step of judging whether the directly lower metadata with an aggregation relationship with the root metadata exists;
and if not, ending the normalization mapping process.
2. The method as recited in claim 1, further comprising:
and displaying the data flow relation among the selected metadata based on the html page.
3. The method of claim 2, wherein the exposing the data flow relationship between the selected plurality of metadata based on the html page comprises: presenting the selected plurality of metadata based on an html page, and representing a data flow relationship between the selected plurality of metadata using a data flow relationship line;
the method further comprises the steps of:
responsive to a user clicking the data stream relationship line, taking respective directly lower metadata of metadata at both ends of the data stream relationship line as updated selected plurality of metadata;
and entering the step of establishing the mapping relation between the selected metadata and the lower metadata with the aggregation relation.
4. An apparatus for calculating metadata associations within an enterprise, comprising:
the normalization mapping module is configured to establish a mapping relation between the selected metadata and the lower metadata with the aggregation relation;
the flow direction acquisition module is configured to acquire a data flow direction relation between the lower metadata;
a flow direction correspondence module configured to set a data flow direction relationship between the lower metadata according to a mapping relationship between the selected plurality of metadata and the lower metadata having an aggregation relationship thereof, to the selected plurality of metadata;
the normalization mapping module comprises:
a mapping initial sub-module configured to open a root setting sub-module for the selected plurality of metadata, respectively, so as to perform normalization mapping processing;
a root setting sub-module configured to take the selected metadata as root metadata;
a judging sub-module configured to judge whether there is directly lower metadata having an aggregation relationship with the root metadata;
the normalization mapping sub-module is configured to establish an association relationship between the selected metadata and the directly lower metadata if the judgment sub-module judges that the metadata exist, take the directly lower metadata as updated root metadata, and trigger the judgment sub-module to execute again; and if not, ending the normalization mapping process.
5. The apparatus as recited in claim 4, further comprising:
and the display module is configured to display the data flow relation among the selected metadata based on the html page.
6. The apparatus of claim 5, wherein the presentation module is configured to present the selected plurality of metadata based on an html page and to represent a data flow relationship between the selected plurality of metadata using a data flow relationship line;
the apparatus further comprises:
and the drill-down response module is configured to respond to clicking the data flow relation line by a user, take the respective metadata of the metadata at the two ends of the data flow relation line as updated selected metadata, and re-trigger the normalization mapping module to execute.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-3.
8. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911206472.1A CN111078695B (en) | 2019-11-29 | 2019-11-29 | Method and device for calculating association relation of metadata in enterprise |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911206472.1A CN111078695B (en) | 2019-11-29 | 2019-11-29 | Method and device for calculating association relation of metadata in enterprise |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111078695A CN111078695A (en) | 2020-04-28 |
| CN111078695B true CN111078695B (en) | 2023-11-21 |
Family
ID=70312158
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911206472.1A Active CN111078695B (en) | 2019-11-29 | 2019-11-29 | Method and device for calculating association relation of metadata in enterprise |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111078695B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111694890A (en) * | 2020-06-30 | 2020-09-22 | 望海康信(北京)科技股份公司 | Relation chart generation method and device, computer equipment and storage medium |
| CN111831720A (en) * | 2020-07-15 | 2020-10-27 | 北京思特奇信息技术股份有限公司 | Data display method and system and electronic equipment |
| CN111966866A (en) * | 2020-08-11 | 2020-11-20 | 福建博思数字科技有限公司 | Data asset management method and device |
| CN116450637A (en) * | 2022-01-05 | 2023-07-18 | 中移(成都)信息通信科技有限公司 | Data management method, device, electronic equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101364240A (en) * | 2008-10-14 | 2009-02-11 | 杭州华三通信技术有限公司 | Metadata management method and device |
| CN103744676A (en) * | 2014-01-07 | 2014-04-23 | 上海新炬网络技术有限公司 | Method for displaying relevance of multi-level information |
| CN108197182A (en) * | 2017-12-25 | 2018-06-22 | 百味云科技股份有限公司 | A kind of data atlas analysis system and method |
| US10289620B1 (en) * | 2017-11-15 | 2019-05-14 | Accenture Global Solutions Limited | Reporting and data governance management |
| CN110019252A (en) * | 2019-04-16 | 2019-07-16 | 成都四方伟业软件股份有限公司 | The method, apparatus and electronic equipment of information processing |
| CN110287223A (en) * | 2019-06-24 | 2019-09-27 | 北京明略软件系统有限公司 | Information storage means and device, electronic device and storage medium |
| CN110347882A (en) * | 2019-06-27 | 2019-10-18 | 北京明略软件系统有限公司 | Consanguinity analysis method and device, storage medium and the electronic device of data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8554784B2 (en) * | 2007-08-31 | 2013-10-08 | Nokia Corporation | Discovering peer-to-peer content using metadata streams |
-
2019
- 2019-11-29 CN CN201911206472.1A patent/CN111078695B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101364240A (en) * | 2008-10-14 | 2009-02-11 | 杭州华三通信技术有限公司 | Metadata management method and device |
| CN103744676A (en) * | 2014-01-07 | 2014-04-23 | 上海新炬网络技术有限公司 | Method for displaying relevance of multi-level information |
| US10289620B1 (en) * | 2017-11-15 | 2019-05-14 | Accenture Global Solutions Limited | Reporting and data governance management |
| CN108197182A (en) * | 2017-12-25 | 2018-06-22 | 百味云科技股份有限公司 | A kind of data atlas analysis system and method |
| CN110019252A (en) * | 2019-04-16 | 2019-07-16 | 成都四方伟业软件股份有限公司 | The method, apparatus and electronic equipment of information processing |
| CN110287223A (en) * | 2019-06-24 | 2019-09-27 | 北京明略软件系统有限公司 | Information storage means and device, electronic device and storage medium |
| CN110347882A (en) * | 2019-06-27 | 2019-10-18 | 北京明略软件系统有限公司 | Consanguinity analysis method and device, storage medium and the electronic device of data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111078695A (en) | 2020-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11741089B1 (en) | Interactive location queries for raw machine data | |
| US11258693B2 (en) | Collaborative incident management for networked computing systems | |
| CN111078695B (en) | Method and device for calculating association relation of metadata in enterprise | |
| US11768811B1 (en) | Managing user data in a multitenant deployment | |
| US11429627B2 (en) | System monitoring driven by automatically determined operational parameters of dependency graph model with user interface | |
| US20200388084A1 (en) | Precise manipulation of virtual object position in an extended reality environment | |
| US10565220B2 (en) | Generating visualizations for search results data containing multiple data dimensions | |
| JP6865219B2 (en) | Event batch processing, output sequencing, and log-based state storage in continuous query processing | |
| US20200104401A1 (en) | Real-Time Measurement And System Monitoring Based On Generated Dependency Graph Models Of System Components | |
| US20190095478A1 (en) | Information technology networked entity monitoring with automatic reliability scoring | |
| US10929202B2 (en) | Cloud service notifications | |
| US11410403B1 (en) | Precise scaling of virtual objects in an extended reality environment | |
| US20200250178A1 (en) | Data source correlation user interface | |
| US20140173509A1 (en) | Device, Method and User Interface for Emphasizing Divisions in Data | |
| CN107133309B (en) | Method and device for storing and querying process example, storage medium and electronic equipment | |
| US11657582B1 (en) | Precise plane detection and placement of virtual objects in an augmented reality environment | |
| US11790623B1 (en) | Manipulation of virtual object position within a plane of an extended reality environment | |
| US20190213110A1 (en) | Object oriented data tracking on client and remote server | |
| US11625254B1 (en) | Interface for customizing dashboards based on parallel edges | |
| US11055925B1 (en) | Techniques for placement of extended reality objects relative to physical objects in an extended reality environment | |
| US11544282B1 (en) | Three-dimensional drill-down data visualization in extended reality environment | |
| US10644971B2 (en) | Graph search in structured query language style query | |
| US11574429B1 (en) | Automated generation of display layouts | |
| US11860889B2 (en) | Cascading data impact visualization tool | |
| US10552455B2 (en) | Analytics enablement for engineering records |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |