Metadata-based management and analysis system

Abstract

The invention provides a metadata-based management and analysis system, and the system comprises a meta-model management module, a mapping management unit and a metadata analysis module; the meta-model management module configures a service unit containing a source system service data table, the source system service data table stores a plurality of incremental fields, and the mapping management unit is used for creating a mapping relationship and a code matching rule of metadata; the metadata acquisition module is used for acquiring metadata and transmitting the acquired metadata to the metadata processing module; the metadata acquisition module comprises a template management unit, a metadata base and a storage auditing unit; the template management unit is used for providing a template for importing and exporting metadata, the metadata base is configured with a plurality of metadata, and the storage auditing unit is used for providing a storage auditing function for the changed metadata; and the metadata processing module is used for processing the source system service data table and integrating the source system service data table into the metadata base. According to the invention, reliable and convenient support can be provided for establishing a metadata management system for an enterprise.

Description

technical field [0001] The invention relates to the technical field of metadata management, in particular to a management and analysis system based on metadata. Background technique [0002] Metadata, also known as intermediary data and relay data, is the data describing data (data about data), mainly describing the definition of the model in the data warehouse, the mapping relationship between each level, monitoring the data status of the data warehouse and ETL task running status. Generally, metadata is stored and managed uniformly through a metadata database, and its main purpose is to make the design, deployment, operation and management of the data warehouse reach a consensus. Metadata is an important part of data warehouse management. Metadata management is the core part of enterprise-level data warehouses. It runs through the entire life cycle of data warehouses. Using metadata to drive the development of data warehouses makes data warehouses automatic and visualized...

AI Technical Summary

Problems solved by technology

[0006] The data warehouse needs metadata management, because the data processing of the data warehouse is complicated, and the user access method is complicated. Take the DW data of a bank as an example: there are 60 upstream systems, more than 20 downstream systems, and as many as 12,000 tables in the warehouse. Multiple, more than 6,000 ETL tasks are running, and new versions are launched every month. There are a lot of data information to be maintained, many upstream and downstream systems, and frequent changes; the maintenance cycle is long

In addition, existing technologies often use three-paradigm modeling, so the core table of LDM will have a large number of sources and targets. Once the impact analysis is done, the results will be diffused and magnified

end up rendering the analysis results unusable

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