CN112612778B - Enterprise data architecture method - Google Patents
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Abstract
An enterprise data architecture method comprises the steps of scanning an enterprise data warehouse, collecting metadata of the data warehouse, and obtaining an application data model; obtaining a cross mapping relation of a physical-application data model by using a data blood-edge analysis tool; identifying and defining a business data object according to the cross mapping relation of the physical-application data model; and analyzing the cross mapping relation of the physical-application data model, and establishing the relation between the business data objects to form an enterprise logic data model.
Description
Technical Field
The invention belongs to the field of manufacturing enterprise data management, and particularly relates to an enterprise data architecture method based on meta-model cross mapping.
Background
As manufacturing progresses, businesses become more complex from product design development, production to business activities, and business operations and decisions are increasingly dependent on efficient, high quality data support. In order to meet the requirement, the current practice of enterprises is to collect and sort and aggregate the data of the whole enterprise business database into a data warehouse so as to support cross-department and cross-system data analysis and query services.
However, with the development of civil aircraft manufacturing activities by civil aircraft enterprises, the data analysis requirements are more and more extensive and deep, and the civil aircraft manufacturing business and technology are very complex, so that the method has the characteristics of large data volume, wide distribution and multiple types. If the civil aircraft enterprise data architecture is unclear and a data panoramic view is lacking, the problems of data redundancy, untimely data response, inconsistent data provision and the like are easily caused. If the service efficiency of the civil aircraft enterprise data asset is low, the data sharing service capability is seriously restricted. Based on the above, the civil engineering manufacturing industry is urgent to construct a data architecture with global height of enterprises so as to support enterprise data management and ensure effective exertion of enterprise data value.
Disclosure of Invention
The invention aims to provide a data architecture method based on meta-model cross mapping, which is used for constructing a logic data model of enterprise global height by comparing the mapping relation between a business system physical model and a data warehouse application data model and providing powerful support for the fields of enterprise data management and data service.
One of the embodiments of the present invention, an enterprise data architecture method, comprises the following steps,
Scanning an enterprise data warehouse, and collecting metadata of the data warehouse to obtain an application data model;
obtaining a cross mapping relation of a physical-application data model by using a data blood-edge analysis tool;
identifying and defining a business data object according to the cross mapping relation of the physical-application data model;
And analyzing the cross mapping relation of the physical-application data model, and establishing the relation between the business data objects to form an enterprise logic data model.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
FIG. 1 is a flow chart of a method for enterprise data architecture based on metadata cross-mapping in accordance with one embodiment of the present invention.
Detailed Description
Currently, in the field of enterprise data architecture development, most enterprises are implemented by way of building data models. Conventional implementations typically have both top-down and bottom-up implementations. The top-down mode depends on familiarity to the service, and a great amount of service investigation is needed by a modeler, so that the acquired service information is accurate, but is time-consuming and labor-consuming. The bottom-up method needs to construct the mapping relation between the data and the service from the database, however, the system construction technical architecture of the enterprise is not uniform, and the data condition of the enterprise on the system is not completely mastered, so that the data asset checking result is difficult to attach to the service reality. Therefore, the enterprise needs a data architecture carding method with small difficulty in the hand, low personnel requirement, standardized process, accurate and reliable obtained result and higher touchdown property.
Aiming at the problems, the prior art solution is to collect technical metadata by using a metadata collection tool, collect corresponding service metadata from a system service scene as a source point, perform link analysis on the service metadata, integrate a data architecture and provide a data panoramic view so as to support data problem tracking and data heat analysis. According to the method, from the metadata perspective, the technical means is utilized to track the data link, the data architecture is constructed, the labor cost is effectively reduced, and the developed data architecture has strong data analysis capability. However, the scheme is based on the service system for data architecture design, so that the metadata integration process is complicated, the technology dependence degree is high, and implementation is difficult to advance.
In order to solve the problems, the method for efficiently constructing the enterprise global height data architecture is explored by considering the data of the data warehouse integrated by the service-oriented theme so as to promote enterprise data management, realize manageability and traceability of enterprise data assets and ensure effective sharing and service of data among various service systems.
In accordance with one or more embodiments, an enterprise data architecture method based on meta-model cross-mapping includes scanning a data warehouse, collecting metadata of the data warehouse, and obtaining an application data model; obtaining a cross mapping relation of a physical-application data model by using a data blood-edge analysis tool; identifying and defining a business data object according to the cross mapping relation of the physical-application data model; and analyzing the cross mapping relation of the physical-application data model, and establishing the relation between the business data objects to form a logic data model.
And the data warehouse is scanned, metadata information of the data warehouse is collected, and an application meta-model is obtained. The specific operation is as follows: collecting data warehouse metadata includes: metadata subject, data table name, field name, etc. Further, considering that the data warehouse construction is of an application driven type, a model whose metadata is constructed is referred to as an application data model. Metadata acquisition is divided into a client and a server, the server is adapted to a data warehouse and a business system and acquires metadata, and the client integrates and visually displays the metadata.
The method for obtaining the physical-application data model by using the data blood edge analysis tool comprises the following steps: with the aid of data lineage analysis tools, data sources are traced forward starting from metadata (application data model) of the data warehouse, in order to keep track of where the current data came from and what has been processed. Based on the blood-edge analysis result, source service system metadata (hereinafter, source service system metadata model is simply referred to as physical data model) is collected, and a mapping relation of the physical-application data model is constructed. Wherein collecting source business system metadata comprises: database type, connection mode, schema, data structure, database table name, field, description, etc.
The step of identifying and defining business data objects based on the cross-mapping of the physical-application data model extracts data objects based on the data warehouse application data model. The reason for this is that,
The physical data model of the source system generally strictly follows a third paradigm to support the scalability of the service, requiring further aggregation of the tightly-business-related metadata into one data object. The data warehouse application model emphasizes data integration, and the data warehouse form design is based on the principle of high cohesion and low coupling, so that in the physical implementation, the business relationship is compact, and the source system influence difference is small; the business relation is small, and the source system has great influence and is separated. In contrast, it is more efficient to identify data objects from the application data model. Moreover, the data warehouse application model generally adopts a star model, and consists of fact tables and dimension tables, wherein the fact tables record the total information of a certain business theme. Thus, the data warehouse fact table may be abstracted into a business data object.
In addition, in the implementation of several bins, there are multiple data tables to extract, integrate the same or similar source data, resulting in duplication or similarity of several bins of service metadata. Therefore, metadata directed to the same business data object needs to be merged during the data asset manipulation process to simplify subsequent business data object association analysis. In particular to a method for realizing the method,
And calculating the similarity of the data sources of the two business data objects according to the cross mapping relation of the physical-application data model, and merging the two data objects if the similarity is higher than a certain threshold value.
And analyzing the cross mapping relation of the physical-application data model, and establishing the relation between the business data objects to form a logic data model. The aim is to construct the association relation between the business data objects by analyzing the data association relation of the source system physical data model. The reason for this is that,
The source system physical model typically designs a fact table based on each business action in the business process to track business process detail data, while data objects identified based on data warehouse data typically aggregate the body information of one business process and keep the relationships between the data objects loose. It is therefore necessary to retrieve the association between data objects by means of a physical data model, which is implemented in such a way,
Aiming at the data objects with the homologous data, establishing the association relation between the corresponding data objects according to the calculation result. And inquiring whether any source data table between the two data objects has a main foreign key association relation aiming at the data objects without the homologous data, and if so, carrying out association of the two data objects.
The method is characterized in that a physical-application data model cross mapping relation analysis is adopted, so that a physical model is quickly converted into a business layer logic data model. The method effectively solves the problems that the time period for constructing the logic data model from top to bottom is long, knowledge of database data is not available from bottom to top, and the combing result cannot be attached to service facts.
In accordance with one or more embodiments, as illustrated in FIG. 1, a flow diagram of a data architecture method based on metamodel cross-mapping. The method comprises the following steps:
step 101, scanning a data warehouse, and collecting metadata of the data warehouse to obtain an application data model;
Here, the data warehouse is scanned according to the account number of the data warehouse, and all metadata information of the data warehouse is obtained. Collecting data warehouse metadata includes: metadata subject, fact table, table field information.
The metadata management unit is divided into a client and a server, the server is adapted to the data warehouse and the service system and collects data, and the client integrates and visually displays metadata.
102, Obtaining a cross mapping relation of a physical-application data model by using a data blood-edge analysis tool;
With the aid of data lineage analysis tools, data sources are traced forward starting from metadata (application data model) of the data warehouse, in order to keep track of where the current data came from and what has been processed.
And collecting metadata (physical data model) of the source business system based on the blood-margin analysis result, and constructing a mapping relation of the physical-application data model. Wherein recording source system metadata comprises: database type, connection mode, schema, data structure, database table name, field, description, etc.
Step 103, identifying and defining a business data object according to the cross mapping relation of the physical-application data model;
Here, data objects are extracted based on the data warehouse application data model and the data warehouse fact table is abstracted into one business data object. In particular, for data objects with higher similarity of data sources, merging into one data object is considered, and the following is specific:
Assuming that the two data objects are O 1,O2, according to the physical-application data model cross mapping relationship, if the data object O 1,O2 has homologous data, the similarity of the data sources is calculated. Assuming that the number of source data forms mapped by O 1,O2 is n 1,n2 (except for the main data form) and the number of homologous forms is m (except for the main data form), the data source similarity of O 1,O2 is:
Sim(O1,O2)＝m/min(n1,n2) (1)
a) When Sim (O 1,O2) > = epsilon (epsilon preset threshold), the business data object O 1,O2 is merged into one data object O 1.
B) When Sim (O 1,O2) < ε (ε preset threshold), business data object O 1,O2 does not need to be merged.
And 104, analyzing the cross mapping relation of the physical-application data model, and establishing the relation between the business data objects to form a logic data model.
On the one hand: for the data objects with the homologous data, according to the calculation result in step 103, an association relationship between the corresponding data objects is established.
On the other hand: aiming at the data objects without the homologous data, inquiring whether a data table mapped to a source system physical model by the data objects has a main foreign key association relationship, and if so, carrying out association of the two data objects;
And (3) integrating the data object association relation combing results, importing a setting template, and generating a global logic data model under a service view angle. To clarify the enterprise data range and data service capability, and support data management, data query and analysis.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (3)
1. A method for constructing an enterprise data model is characterized by comprising the following steps,
A1, scanning an enterprise data warehouse, collecting metadata of the data warehouse, and obtaining a model constructed by the metadata, wherein the model is defined as an application data model;
A2, by means of a data blood-edge analysis tool, taking an application data model of a data warehouse as a starting point, tracing forward a data source, collecting metadata of a source service system based on a blood-edge analysis result, defining the source service system metadata as a physical data model, and constructing a cross mapping relation of the physical-application data model;
A3, identifying and defining a service data object based on the data warehouse application data model, wherein the service data object identification calculates the similarity of a service data object data source according to the cross mapping relation of the physical-application data model, and if the similarity is higher than a certain threshold value, merging the service data objects;
A4, analyzing the cross mapping relation of the physical-application data model, establishing the relation between the business data objects to form an enterprise logic data model, and constructing the relation between the business data objects by analyzing the data relation of the physical data model of the source business system, wherein,
Establishing an association relation between corresponding business data objects aiming at the business data objects with homologous data;
for the business data object without the homologous data, inquiring whether any source data table between the two business data objects has a main foreign key association relationship, and if so, carrying out association of the two business data objects, wherein the specific process comprises the following steps:
Assuming that the two business data objects are O 1、O2, according to the physical-application data model cross mapping relation, if the business data object O 1、O2 has homologous data, the similarity of the data sources is calculated,
Assuming that the number of source data forms mapped by O 1、O2 is n 1,n2 excluding the main data form, and the number of homologous forms excluding the main data form is m, the similarity of the data sources of O 1、O2 is,
Sim(O1,O2)＝m/min(n1,n2) (1)
Wherein the number of the components is also one,
A) When Sim (O 1,O2) > = epsilon, epsilon being a preset threshold, the traffic data object O 1、O2 is merged into one data object O 1,
B) When Sim (O 1,O2) < epsilon, the business data object O 1、O2 does not need to be merged.
2. The method according to claim 1, wherein the step of scanning the data warehouse collects metadata of the data warehouse, including a theme, a data table name and a field name of the metadata, and the metadata is adapted to the data warehouse and the service system through the server and collected, and is integrated and visually displayed through the client.
3. The method of claim 1, wherein the business is a civil aircraft manufacturing business.
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