Data Visual Analysis Execution Units for BI Bottlenecks
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Solution Overview
Problem
Current Business Intelligence (BI) systems are inefficient in processing massive data, requiring intermediate storage and iterative data model updates, and lack the ability to perform personalized data analysis according to user demands, making them slow and inflexible for visual analysis.
Innovation Solution
A data visual analysis method and system that generates a data analysis model with multiple execution units, allowing for dynamic invocation of data sources, analysis, and visual output of results, with the option to update execution units based on results, enabling user-defined modeling and flexible computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the BI system processes massive data using traditional methods, then the data can be analyzed, but the processing time is excessive and requires intermediate storage
Solution Approach 1:
The patent segments the data analysis process into multiple independent execution units, each handling specific data sources and analysis tasks. This allows parallel processing of different data subsets, eliminating the need for sequential processing and intermediate storage, thereby significantly reducing overall processing time while maintaining analysis completeness.
Solution Approach 2:
The system performs preliminary actions by pre-defining multiple execution units with their respective data sources and analysis logic before actual data processing begins. This preparation enables immediate parallel execution when data arrives, eliminating the need for intermediate storage and repeated model iteration, thus improving processing speed.
2Adaptability or versatility
If the data model needs to be modified in the current BI system, then the analysis can be updated, but the whole data model needs to be iterated again increasing processing time
Solution Approach 1:
The patent divides the data model into independent execution units that can be modified individually. When a modification is needed, only the specific execution unit requiring update is changed, while other units remain unchanged and can continue executing. This selective update mechanism eliminates the need for full model iteration, significantly reducing update time while maintaining model flexibility.
Solution Approach 2:
The system implements dynamic model updating by allowing execution units to be independently modified, added, or removed based on changing analysis requirements. This dynamic structure enables the model to adapt to new demands without requiring complete reiteration, thus improving both adaptability and reducing update time.
3Ease of operation
If the current BI system uses fixed preset scenarios, then the system is simple to operate, but it cannot perform personalized modeling according to user demands
Solution Approach 1:
The patent creates a universal execution unit framework that can handle both preset scenarios and personalized analysis requests. The same execution unit structure serves multiple functions: processing predefined analysis templates and accommodating user-customized analysis configurations, thereby achieving both ease of operation for standard tasks and adaptability for personalized requirements.
Solution Approach 2:
The system dynamically adjusts between fixed preset scenarios and flexible personalized modeling based on user needs. Execution units can operate in predefined modes for simplicity or be customized by users for specific analysis requirements, providing a dynamic balance between ease of operation and adaptability.
Data Source
AI summary
A data visual analysis method, system and terminal, and a computer readable storage medium are provided. The method includes: obtaining to-be-analyzed parameters and generating a data analysis model, the data analysis model including a plurality of execution units; the data sources collecting data information related to the to-be-analyzed parameters; the execution units performing analysis on the data information collected by the data sources, to obtain execution results of the execution units; and visually outputting the execution results of the execution units.