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Data modeling method for data mart and data warehouse

A data mart and data warehouse technology, applied in the data modeling field of data marts and data warehouses, can solve problems such as inability to reuse, data islands, and inability to guarantee the standardization, consistency, and accuracy of enterprise data, and achieve It is convenient to carry out decision-making analysis, facilitate traceability, and ensure the effect of data standardization

Pending Publication Date: 2020-12-15
重庆富民银行股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention intends to provide a data modeling method for data marts and data warehouses to solve the problem in the prior art that each independent data mart is independently constructed by each user or department, and its data structures are independent of each other and cannot be replicated. It is easy to cause data islands, and at the same time, it cannot guarantee the standardization, consistency and accuracy of enterprise data.

Method used

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  • Data modeling method for data mart and data warehouse
  • Data modeling method for data mart and data warehouse
  • Data modeling method for data mart and data warehouse

Examples

Experimental program
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Effect test

Embodiment 1

[0033] A data modeling approach for data marts and data warehouses such as figure 1 shown, including the following steps:

[0034] The steps to build the bus architecture include the following steps:

[0035]Automatically integrate according to preset data specifications and data standards, and integrate into public consistent dimensions as master data. Data specifications and data standards are formulated by those skilled in the art according to industry standards. Master data is common data among various business data, including customers, products, institutions, channels and addresses. The business system is an intelligent system used by each business department in the enterprise. Since the business of each business department is different, the business data obtained by each business system is also different. Through the integration of the common data among the business data, the establishment of The association between various business data.

[0036] Construct a standar...

Embodiment 2

[0062] The difference between this embodiment and Embodiment 1 lies in: a data modeling method for data marts and data warehouses, further comprising the following steps:

[0063] Common processing steps specifically include the following steps:

[0064] According to the preset common requirements, business data is extracted from the partial source model layer, and loaded to the common processing layer, and the extracted business data is integrated to automatically generate a common basic table, and the common basic table is stored. Common requirements are the basic information requirements that all business systems in the enterprise need to call, such as business product information that all business systems need. By extracting business product data, the business product data is integrated to generate a product table, such as the product table of the risk control credit line. When in use, the common basic table can be directly called for use. By sharing the common basic tabl...

Embodiment 3

[0066] The difference between this embodiment and Embodiment 2 lies in: a data modeling method for data marts and data warehouses, which also includes the following steps:

[0067] The steps of applying the marketplace include the following steps:

[0068] According to the preset application requirements, business data is regularly extracted from the partial source model layer, and loaded to the application market layer, and data analysis and calculation are performed based on the extracted business data to generate application analysis results. The application requirements are the application analysis requirements for the high frequency of use of various business systems in the enterprise. For example, for the wind control system, data analysis such as vintage, migration rate, reminder rate, rolling rate, and first pass rate will be carried out regularly, so as to grasp The repayment information of corporate customers and the effectiveness of corporate marketing strategies. ...

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Abstract

The invention relates to the technical field of data marts and data warehouses, in particular to a data modeling method for data marts and data warehouses, which comprises the following steps: a stepof building a bus architecture: extracting main data of a service system for automatic integration, obtaining a public consistency dimension, and building a standard bus interface; a data processing step: classifying the business data tables, and automatically screening a data extraction mode to extract the business data tables according to a classification result; and a model integration step: combining the business data tables through the inverse three normal forms, splitting the business data tables to form a fact table and a plurality of dimension tables, and constructing a dimension modelwhich takes the fact table as a main part according to the fact table and the plurality of dimension tables. By the adoption of the scheme, the technical problems that in the prior art, due to the fact that all independent data markets are independently constructed by all users or departments, data architectures of the independent data markets are independent of one another and cannot be reused,data islands are easily caused, and meanwhile standardization, consistency and accuracy of enterprise data cannot be guaranteed can be solved.

Description

technical field [0001] The invention relates to the technical field of data marts and data warehouses, in particular to a data modeling method for data marts and data warehouses. Background technique [0002] Data warehouse refers to a data collection with the characteristics of subject-oriented, integration, stability and time-varying, which mainly provides data support for decision-making analysis. Data mart, also known as data market, refers to a collection of data stored in a multi-dimensional manner to meet the needs of specific departments or users, and its data can be extracted from enterprise-level data warehouses. [0003] The current data mart can be divided into two types: one is the subordinate data mart, which is top-down and data source-oriented, and its data flow direction is data source-data warehouse-data mart, for example, the data of the business system As a data source, the data of the business system is cleaned, loaded, and integrated to form a data war...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/215G06F16/25G06F16/28G06Q40/02
CPCG06F16/215G06F16/252G06F16/254G06F16/283G06Q40/03
Inventor 张洪春
Owner 重庆富民银行股份有限公司
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