Data Analysis Method Using Layered Calculation Model
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Solution Overview
Problem
Current Business Intelligence (BI) products face limitations in handling complex nested relationships of data indicators, reducing the flexibility of data analysis and making it difficult to meet user requirements.
Innovation Solution
A data analysis method that acquires a data analysis request, analyzes data indicators based on a calculation model to identify dependent basic indicators, and performs calculations on query results to obtain the data indicator results, using a directed acyclic graph (DAG) to represent dependency relationships and support flexible configuration and calculation of data indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current BI products use traditional data indicator calculation methods, then the system structure is simple, but the system cannot handle complex nested relationships of data indicators
Solution Approach 1:
The patent segments the calculation model into multiple layers (first layer, second layer, third layer) where each layer handles specific types of data indicators. Basic indicators are separated from derived indicators, and intermediate indicators are distinguished from final indicators. This segmentation allows the system to manage complex nested relationships by breaking them down into manageable computational stages while maintaining overall system structure.
Solution Approach 2:
The patent implements a nested calculation model where data indicators are organized in hierarchical layers. The first layer contains basic indicators that serve as foundations, the second layer contains intermediate indicators that depend on first layer indicators, and the third layer contains derived indicators that depend on second layer indicators. This nested structure enables the system to handle complex nested relationships by allowing indicators to be embedded within other indicators across multiple levels.
2Adaptability or versatility
If the system supports complex nested relationships of data indicators, then data analysis flexibility is improved, but the calculation complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the layered structure of the calculation model and pre-establishing the dependency relationships between indicators in different layers. The system pre-processes the data by separating basic indicators from derived indicators and establishing the calculation hierarchy before actual data analysis occurs. This preliminary structuring reduces calculation complexity during execution while maintaining high data analysis flexibility.
Solution Approach 2:
The patent implements a dynamic calculation model where the system can adaptively determine which layer of indicators to calculate based on user requirements. The calculation process dynamically traverses the layered structure, selecting and computing only the necessary indicators rather than calculating all possible indicators. This dynamic approach maintains data analysis flexibility while reducing actual calculation complexity by avoiding unnecessary computations.
3Adaptability or versatility
If traditional calculation methods are used, then the configuration process is simple, but user requirements cannot be met
Solution Approach 1:
The patent creates a universal calculation model that can handle multiple types of data indicators (basic indicators, intermediate indicators, derived indicators) within a single unified framework. The layered structure serves multiple functions: it organizes indicators hierarchically, manages dependencies, enables flexible querying, and supports various calculation scenarios. This multi-functionality allows the system to meet diverse user requirements while providing a consistent configuration interface.
Solution Approach 2:
The patent introduces intermediate indicators as mediators between basic indicators and derived indicators. These intermediate indicators serve as computational bridges that simplify complex calculations by breaking them into manageable steps. The intermediary layer allows users to configure sophisticated analysis requirements without directly managing the full complexity of nested calculations, as the intermediate indicators handle the computational mediation automatically.
Data Source
AI summary
The present disclosure provides a data analysis method, an apparatus, a device, and a computer-readable storage medium. The method includes acquiring a data analysis request, in which the data analysis request includes at least one data indicator; analyzing the at least one data indicator according to a calculation model of the at least one data indicator to obtain a basic indicator on which the at least one data indicator depends, in which the calculation model comprises a dependency relationship between the at least one data indicator and the basic indicator; and according to a query result of the basic indicator, performing calculation on the query result of the basic indicator through the calculation model to obtain a query result of the at least one data indicator.