Intelligent data index management method

Abstract

The invention discloses an intelligent data index management method. The method comprises the following steps of 1, creating a data model through a data set, a dimension set and a measurement set; 2, on the basis of the data model, constructing basic data indexes by defining various attributes of the data indexes; 3, after the basic indexes are constructed, whether the indexes need to be expanded or not is judged, if expansion is not needed, the next step is directly continued, and if expansion is needed, the next step is continued after production related indexes are expanded; and 4, combining a plurality of different basic indexes by adopting a calculation formula to form a brand new composite index. According to the intelligent data index management method, the creation and maintenance process of the associated data indexes can be effectively simplified, and the visual tracking of the relationship between the data indexes is realized.

Description

technical field [0001] The invention relates to a management method, and more particularly to an intelligent data index management method. Background technique [0002] Data indicators are an important criterion for measuring business development. In a typical business scenario, the same data indicator can be divided into more sub-indicators according to different filtering conditions such as statistical dimensions and time ranges. For example, the number of active users can be divided into the number of daily active users and the number of monthly active users according to the statistical time, and can also be further divided into the number of logged-in active users and the number of non-login active users according to the user interaction mode. These sub-indicators are related to each other, and there are only slight differences in the statistical methods. If they are managed and maintained as independent indicators, it will not reflect the internal relationship between t...

AI Technical Summary

Problems solved by technology

These sub-indicators are related to each other, and there are only subtle differences in statistical methods. If they are managed and maintained as independent indicators, the internal relationship between indicators will not be reflected, and it will be difficult to maintain and calculate due to the expansion of the number of indicators.

[0003] At present, data indicators with internal correlation are mainly managed by logical grouping, but logical grouping can only distinguish data indicators from the business concept, and cannot effectively reuse the calculation rules of similar indicators. Moreover, for data indicators with multi-level associations, logical grouping cannot well identify the levels and internal relationships between indicators.

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