CN112612778A - Enterprise data architecture method - Google Patents
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Abstract
An enterprise data architecture method comprises the following steps of scanning an enterprise data warehouse, collecting metadata of the data warehouse, and obtaining an application data model; acquiring a cross mapping relation of a physical-application data model by using a data blood margin 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, establishing the relation between business data objects, and forming 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
With the development of manufacturing industry, the enterprise becomes more and more complex from product design, development, production to business activities, and the operation and decision of the enterprise increasingly depend on efficient and high-quality data support. For the requirement, the current practice of enterprises is to collect, sort and gather the data of the whole enterprise business database into a data warehouse 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 analysis requirements on data are more and more extensive and deeper, and the civil aircraft manufacturing business and technology are very complex, and have the characteristics of large data volume, wide distribution and multiple types. If the civil aircraft enterprise data architecture is unclear and lacks a data panoramic view, data redundancy is easily caused, data response is not timely, and the problem of inconsistent data supply is solved. If the data asset utilization efficiency of the civil aircraft enterprise is low, the improvement of the data sharing service capability is seriously restricted. Based on the above, the civil aircraft manufacturing industry needs to construct a data architecture with global height of an enterprise urgently to support enterprise data management and guarantee 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 logical data model of the enterprise global height by comparing the mapping relation between a business system physical model and a data warehouse application data model, and provides powerful support for the fields of enterprise data management and data service.
In one embodiment of the present invention, an enterprise data architecture method includes the following steps,
scanning an enterprise data warehouse, and acquiring metadata of the data warehouse to obtain an application data model;
acquiring a cross mapping relation of a physical-application data model by using a data blood margin 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, establishing the relation between business data objects, and forming an enterprise logic data model.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the 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 flowchart of a metadata cross-mapping based enterprise data architecture method, according to one embodiment of the invention.
Detailed Description
Currently, in the field of enterprise data architecture development, most enterprises are implemented by building data models. Conventional implementations typically have both top-down and bottom-up. The top-down mode depends on the familiarity with the business, modeling personnel need a great deal of business research, and although the obtained business information is accurate, the time and the labor are often consumed. The bottom-up method needs to start from a database to construct a mapping relation between data and services, however, system construction technical architectures of enterprises are not uniform, and the data conditions of the enterprises to the systems are not completely mastered, so that the data asset inventory result is difficult to fit the actual services. Therefore, enterprises need a data architecture combing method which has the advantages of small difficulty, low personnel requirement, standardized process, accurate and reliable obtained result and higher landing performance.
For the above problems, the existing 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 to support data problem tracking and data heat analysis. The method is based on the metadata angle, the data link is tracked by using a technical means, the data architecture is constructed, the labor cost is effectively reduced, and the developed data architecture has strong data analysis capability. However, the scheme carries out data architecture design based on a business system, the metadata integration process is complex, the technical dependence degree is high, and implementation is difficult to promote.
In order to solve the problems, the invention considers starting from data warehouse data integrated facing business 'theme', explores a method for efficiently constructing an enterprise global high-level data architecture, so as to promote enterprise data management, realize enterprise data asset manageability and traceability and ensure effective sharing and service of data among all business systems.
According to one or more embodiments, an enterprise data architecture method based on meta-model cross-mapping includes scanning a data warehouse, collecting meta-data of the data warehouse, and obtaining an application data model; acquiring a cross mapping relation of a physical-application data model by using a data blood margin 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, establishing the relation between service data objects, and forming a logic data model.
And the scanning data warehouse collects the metadata information of the data warehouse and obtains the application metadata model. The specific operation is as follows: collecting data warehouse metadata includes: the subject of the metadata, the name of the data table, the name of the field, and the like. In addition, considering that the data warehouse construction is application-driven, the model constructed by the metadata is called an application data model. The metadata collection is divided into a client and a server, the server is adapted to the data warehouse and the service system and collects the metadata, and the client integrates and visually displays the metadata.
The physical-application data model is obtained by using a data blood margin analysis tool, and the method comprises the following steps: with the aid of a data blood margin analysis tool, a data source is traced forward by taking metadata (application data model) of a data warehouse as a starting point, so as to clear where the current data comes from and what kind of processing is performed. And collecting source business system metadata (hereinafter, the source business system metadata is referred to as a physical data model) based on the blood-related analysis result, and constructing a mapping relation of the physical-application data model. Wherein collecting source service system metadata comprises: the information of 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 relationship of the physical-application data model extracts data objects based on the data warehouse application data model. The reason is that the above-mentioned materials are,
the physical data model of the source system generally strictly follows the third paradigm to support the extensibility of the business, and metadata with close business relations needs to be further aggregated to form a data object. The data warehouse application model emphasizes data integration, and the design of the data warehouse form is based on the principle of 'high cohesion and low coupling', and in physical implementation, the integration is performed on the data warehouse form with tight business relation and small source system influence difference; the business relation is small, and the source system influences the big difference to divide and place. In contrast, identifying data objects from an application data model is more efficient. Moreover, the data warehouse application model usually adopts a star model, and is composed of a fact table and a dimension table, wherein the fact table records the full information of a certain business theme. Thus, the data warehouse fact table may be abstracted as a business data object.
In addition, in general, in a multi-bin implementation, there are multiple data tables to extract and integrate the same or similar source data, resulting in duplication or similarity of the multi-bin service metadata. Therefore, in the process of data asset combing, metadata pointing to the same business data object needs to be merged to simplify the analysis of the incidence relation of subsequent business data objects. The concrete implementation mode is that,
and calculating the similarity of the data sources of the two service 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, establishing the relation between service data objects, and forming a logic data model. The method aims to construct the incidence relation among the business data objects by analyzing the data incidence relation of a physical data model of a source system. The reason is that the above-mentioned materials are,
the source system physical model typically designs a fact table to track business process detail data based on each business action in the business process, while data objects identified based on data warehouse data typically aggregate the subject information of a business process and keep the relationships between data objects loose. Therefore, the association relationship between the data objects needs to be retrieved by means of the physical data model, and the specific implementation manner is,
and aiming at the data objects with the homologous data, establishing an incidence 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 or not aiming at the data objects without the homologous data, and if so, associating the two data objects.
According to the method, a physical model is quickly converted into a business level logic data model through analysis of a physical-application data model cross mapping relation. The method effectively solves the problems that the time period for constructing the logic data model from top to bottom is long, the database data is not known from bottom to top, and the combing result cannot fit the business fact.
In accordance with one or more embodiments, a flow diagram of a data architecture method based on meta-model cross-mapping is shown in FIG. 1. 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: the subject of the metadata, fact table, table field information.
The metadata management unit is divided into a client and a server, the server is matched with the data warehouse and the service system and collects data, and the client integrates and visually displays the metadata.
102, acquiring a cross mapping relation of a physical-application data model by using a data blood margin analysis tool;
with the aid of a data blood margin analysis tool, a data source is traced forward by taking metadata (application data model) of a data warehouse as a starting point, so as to clear where the current data comes from and what kind of processing is performed.
And collecting source business system metadata (physical data model) based on the blood relationship analysis result, and constructing a mapping relation of the physical-application data model. Wherein recording the source system metadata comprises: the information of database type, connection mode, Schema, data structure, database table name, field, description, etc.
here, the data object is extracted based on the data warehouse application data model and the data warehouse fact table is abstracted as a business data object. Particularly, for a data object with high similarity at the source of the data, the data object is considered to be merged into one data object, specifically:
assume two data objects are O1,O2If the data object O is in a physical-application data model cross-mapping relationship1,O2If homologous data exists, the similarity of the data sources is calculated. Suppose O1,O2The number of the mapped source data forms is n respectively1,n2(except for the main data sheet), the number of the homologous forms is m (except for the main data sheet), and then O1,O2The data source similarity is as follows:
Sim(O1,O2)＝m/min(n1,n2) (1)
a) when Sim (O)1,O2) Epsilon (epsilon preset threshold), business data object O1,O2Are merged into one data object O1。
b) When Sim (O)1,O2) < ε (ε Preset threshold), service data object O1,O2No merging is required.
And 104, analyzing the cross mapping relation of the physical-application data model, establishing the relation between service data objects, and forming a logic data model.
On one hand: and aiming at the data objects with the homologous data, establishing an incidence relation between the corresponding data objects according to the calculation result in the step 103.
On the other hand: for a data object without homologous data, whether a data table mapped to a physical model of a source system by the data object has a main foreign key association relation or not needs to be inquired, and if so, association of the two data objects is carried out;
and integrating the association relationship combing results of the data objects, importing a set template, and generating a global logic data model under a business view angle. The data management and analysis method is used for determining the data range and the data service capability of the enterprise and supporting data management, data query and analysis.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. An enterprise data architecture method, comprising the steps of,
scanning an enterprise data warehouse, and acquiring metadata of the data warehouse to obtain an application data model;
acquiring a cross mapping relation of a physical-application data model by using a data blood margin 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, establishing the relation between business data objects, and forming an enterprise logic data model.
2. The enterprise data architecture method of claim 1, wherein the step of scanning the data warehouse collects metadata of the data warehouse including a subject, a data table name, a field name of the metadata,
the server is adapted to the data warehouse and the business system, metadata is collected, and the metadata is integrated and visually displayed through the client.
3. The enterprise data architecture method of claim 2, wherein the step of utilizing a data consanguinity analysis tool comprises,
by means of a data blood relationship analysis tool, taking an application data model of a data warehouse as a starting point, tracing back a data source forwards,
and collecting source service system metadata based on the blood relationship analysis result, defining the source service system metadata model as a physical data model, and constructing a mapping relation of the physical-application data model.
4. The enterprise data architecture method of claim 3, wherein the step of identifying and defining business data objects based on physical-to-application data model cross-mappings comprises,
extracting data objects based on the data warehouse application data model;
and calculating the similarity of the data sources of the business data objects according to the cross mapping relation of the physical-application data model, and merging the data objects if the similarity is higher than a certain threshold value.
5. The enterprise data architecture method of claim 4, wherein the step of analyzing the cross-mapping relationships of the physical-application data models, establishing relationships between business data objects, and forming the logical data model constructs relationships between the business data objects by analyzing data relationships of the physical data models of the source system, the step being performed by the process of analyzing the data relationships of the physical data models of the source system,
aiming at the data objects with the same source data, establishing an incidence relation between the corresponding data objects;
and inquiring whether any source data table between the two data objects has a main foreign key association relation or not aiming at the data objects without the homologous data, and if so, associating the two data objects.
6. The enterprise data architecture method of claim 1, wherein the enterprise is a civil aircraft manufacturing enterprise.
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