Method for quick-speed realizing data store house process based on metadata

Abstract

The invention discloses a method that is based on a metadata and can quickly realize the database procedure. The invention parses and lists containing tolerance and dimensionality to existing one group of basic cube model in the database; users combine the basic cube with common dimensionality and utilize prior tolerance to create new counting tolerance according to the requirements; the invention leads the definition in the database to form the combining cube model; the invention proceeds the related combination to the truth database corresponding to the basic cube as the out keys corresponding to the common dimensionality according to the combining cube definition information confirmed by the users and form the new truth table or view as the data supporting to the cube combination. By adopting the invention, users can freely create multidimensional analysis model during the using procedure rather than the developers design and fulfill beforehand, thereby greatly enhancing the applying ability of the database application to the operation analyzing requirement and accelerating the database procedure.

Description

technical field [0001] The present invention relates to a method for accelerating the process of data warehouse, improving application adaptability and expansion ability, specifically a method based on metadata, by providing a human-computer interaction interface, so that users of data warehouse applications can conveniently pass existing Multi-dimensional cubes (called base cubes) are composed to form new multi-dimensional cubes (called composite cubes) based on metadata to quickly implement the method of data warehouse process. Background technique [0002] The concept of online analytical processing (OLAP) was first proposed by E.F. Codd, the father of relational database, in 1993. At that time, Codd believed that online transaction processing (OLTP) could no longer meet the needs of end users for database query and analysis, and simple queries on large databases could not meet the needs of user analysis. The user's decision analysis needs a large number of calculations ...

AI Technical Summary

Problems solved by technology

It is difficult for requirements analysts to discuss and communicate with them carefully on the details of requirements;

[0006] 2) Since the construction of data warehouse applications in China is still in its infancy, enterprise business personnel and management personnel lack a basic understanding of this field, and do not know what query and analysis methods the system can provide;

[0007] 3) A large number of routine queries, statistics and reports have been completed in the business system. Even if enterprise managers have deeper management analysis needs, they are often in a state of "unclear".

Due to the high complexity of this work, the construction of the data warehouse project is difficult and the cycle is long

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