Multilevel and multidimensional method and device for analyzing data attributes

Abstract

The invention relates to a multilevel and multidimensional method and a device for analyzing data attributes, wherein the method comprises the following steps: building a public code platform for maintaining the public basic data, wherein the public basic data comprises basic data and / or a public code table; building a data relationship model for the basic data analysis as the basic data analysis platform; performing the multilevel and multidimensional analysis on the data attributes according to the data relationship of the basic data analysis platform. The multilevel and multidimensional method and the device for analyzing data attributes achieve a tree-shaped hierarchy show for the multidimensional analysis on the data attributes through the analysis dimensionalities of the basic data analysis types and the tree-shaped hierarchy expansion, and fast achieve the building and flexible expansion for the public basic data through the public code platform. The method and the device fast achieve the building of the data attribute analysis types and analysis dimensions based on the basic data analysis platform, and achieve the multilevel and multidimensional analysis on the data attributes and dynamic expansion of the analysis dimensions in a tree-shaped hierarchy expansion way. The dimensional data analysis rules are arranged to flexibly achieve the statistics summary of the analysis data.

Description

Technical field [0001] The invention relates to the application field of information systems, in particular to a multi-level and multi-dimensional data attribute analysis method and device. Background technique [0002] At present, in the work of enterprise management, it is necessary to analyze and count data attributes, such as the title or salary grade in the employee attributes, in order to more reasonably and quickly realize the management of enterprise employee information, improve work efficiency, and realize the standardization of employee information management And automation. [0003] The prior art data attribute analysis is usually processed by adding fields instead of dynamic expansion; its analysis dimension is completed by program code logic, and the analysis dimension of data attributes is written by program logic. If an analysis dimension needs to be added, only Program adjustment or secondary development can be carried out. For each new analysis dimension, a dimen...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, data attribute analysis is usually processed by adding fields, and does not support dynamic expansion; the analysis dimension is completed by the program code logic, and the analysis dimension of the data attribute is hard-coded by the program logic. If you need to add an analysis dimension, you only need to It can be adjusted through the program or carried out secondary development. Every time an analysis dimension is added, a dimension table is added, which makes the control logic complicated

Therefore, the prior art data attribute analysis has a single dimension, does not support dynamic expansion, has no unified data attribute model, and has no abstraction for analysis logic planning

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