Knowledge Package Library for Data Analysis Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data analysis systems struggle to provide industry-specific insights and complex causal relationship analysis due to the lack of deep understanding of business ecosystems and data modeling, making it difficult for organizations to capitalize on big data effectively.
Innovation Solution
A data analysis method and system that generates a knowledge package library by combining user-specific data analysis requests with previously-stored knowledge packages, measuring satisfaction levels based on parameters like business area, location, analysis, time, and data type, to provide a comprehensive understanding of data selection, modeling, and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single platform performs data analysis, then the analysis process is simple and fast, but the depth of industry-specific insights and business ecosystem understanding is limited
Solution Approach 1:
The patent combines multiple single-platform analysis results into a comprehensive knowledge package library. Each platform PF-A to PF-Z contributes its analysis results, and these are merged into an integrated knowledge library that provides both the speed of individual platforms and the depth of comprehensive business ecosystem understanding.
Solution Approach 2:
The system performs preliminary data collection and analysis across multiple platforms before generating final results. Knowledge packages are pre-generated from historical data and stored in the knowledge library, allowing quick retrieval and combination when new analysis requests are received, thus maintaining fast response while incorporating deep industry insights.
2Loss of information
If multiple platforms are used for comprehensive analysis, then industry-specific insights are improved, but the system complexity increases
Solution Approach 1:
The system segments the complex multi-platform analysis into independent knowledge packages, each corresponding to a specific platform's analysis results. These segmented knowledge packages are stored separately in the knowledge library and can be independently managed, retrieved, and combined, thus reducing system complexity while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The knowledge package library serves as an intermediary layer between multiple analysis platforms and users. Instead of directly managing complex interactions between platforms, the system uses the knowledge library as a mediator to store, organize, and retrieve analysis results, significantly simplifying the system architecture.
3Measurement precision
If custom data analysis is performed for each user request, then analysis accuracy is high, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary analysis across multiple platforms and stores the results as knowledge packages in advance. When a user requests data analysis, the system quickly retrieves relevant pre-analyzed knowledge packages from the library and combines them, rather than performing complete custom analysis from scratch. This maintains high accuracy while significantly reducing analysis time.
Solution Approach 2:
The system creates copies of proven analysis patterns and knowledge packages from previous successful analyses. These copied knowledge packages can be quickly retrieved and adapted for new user requests, maintaining the accuracy of proven analysis methods while avoiding the time cost of re-analyzing the same patterns.
Data Source
AI summary
Methods and systems for analyzing data. The data analysis method includes generating a case data set corresponding to the data analysis request; collecting and storing raw data corresponding to the case data set; generating a knowledge package based on the raw data; generating a knowledge package library based on the knowledge package; and providing the user with the knowledge package library. Also, the case data set includes first selection parameters, and the knowledge package library includes second selection parameters different from the first selection parameters. According to the embodiments of the present disclosure, a deep understanding of business and ecosystem which is previously obtained from big data analysis and insights about data group selection, modeling, and analysis method can be provided, such that a big data analysis can be performed with enhanced efficiency.