Business modeling analysis method based on big data

Abstract

The invention provides a business modeling analysis method based on big data, belongs to the field of big data modeling, relates to a method for configuring and constructing accessed data according to business logic rules to realize rule calculation and obtain a result set, and also supports a method for customizing and arranging a logic sequence according to a workflow mode for a plurality of established models so that model combinatorial analysis is made. Development steps are simplified, and cost is reduced. And the user can put more into the construction of the model system, so that the efficiency of the overall intelligence is improved.

Description

technical field [0001] The invention relates to the field of big data modeling, in particular to a business modeling and analysis method based on big data. Background technique [0002] As the degree of informatization continues to deepen, there are urgent requirements for intelligent transformation of how to rely on the characteristics of the industry to mine value and improve the high-quality development of the industry for the massive data accumulated continuously. Modeling is a combination of algorithms and requirements, and is the basic support for realizing the above requirements. A good model can improve work efficiency, generate huge industry value, and has unlimited potential. How to build a knowledge base based on the model is whether it can keep up with the pace of informatization the only way. However, the characteristics of data diversification and mass quantification put forward higher requirements for data analysis. First of all, the complicated data usage en...

AI Technical Summary

Problems solved by technology

However, the diversification and massive quantity of data put forward higher requirements for data analysis. Firstly, the complicated data usage environment requires unified standards and rules, and the use of big data governance technology to integrate data increases the cost of use; Secondly, the current mainstream modeling tools are more aimed at the collision and combination of data, and there is a certain threshold of use, which is not friendly to mainstream users; thirdly, the use of models is relatively single, and they cannot be related to each other, and cannot form a knowledge base. system and guarantee the efficient use of users; the manufacturer's modeling tools can only be used under the framework of their own technology, and are poorly compatible with other technologies and tools; the computing efficiency is low and the usability is poor

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