Unified Data Warehouse for Terminal and System Analysis
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Solution Overview
Problem
Current data management methods in enterprises involve separate systems for terminal and system information, preventing interconnection and intercommunication, which limits the accuracy of analyzing the operation states of terminals and systems simultaneously.
Innovation Solution
A data processing method that extracts original data from terminal devices and application systems into a message engine using a standardized interface, aggregates it, and stores it in a distributed file system to form fact tables, which are then processed in a columnar database to generate target charts, enabling simultaneous analysis of terminal and system operation states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate management systems are used for terminal information and system information, then each system can be managed independently, but interconnection and intercommunication of information cannot be achieved, resulting in low accuracy of analysis results
Solution Approach 1:
The patent merges terminal information and system information into a unified data warehouse, enabling integrated analysis. The data warehouse consolidates data from multiple sources including terminal devices, application systems, and third-party systems, allowing simultaneous analysis of both terminal and system operation states to improve measurement precision.
Solution Approach 2:
The patent introduces a message engine as an intermediary component that facilitates data exchange and integration between terminal information and system information. The message engine receives data from various sources, performs standardization and aggregation, and makes data available to the data warehouse, enabling interconnection without direct coupling between systems.
2Adaptability or versatility
If multiple independent systems are used for data management, then each system operates autonomously, but data interconnection and simultaneous analysis are prevented
Solution Approach 1:
The patent creates a universal data warehouse that can handle multiple types of data sources and analysis requirements through a single integrated platform. The system supports terminal data, system data, and third-party data, providing versatile data analysis capabilities while maintaining a unified architecture that reduces overall system complexity.
Solution Approach 2:
The patent segments the data architecture into distinct layers including data collection layer, message engine layer, data warehouse layer, and analysis layer. This segmentation allows each component to operate independently with well-defined interfaces, enabling autonomy while facilitating integration and simultaneous analysis across different data types.
3Measurement precision
If terminal data and system data are analyzed separately, then analysis processes are simple, but the operation state cannot be comprehensively analyzed
Solution Approach 1:
The patent performs preliminary data integration and standardization in the message engine before data reaches the analysis stage. Data from terminal devices and application systems is aggregated, validated, and standardized in advance, so that when analysis occurs, the data is already prepared for comprehensive processing, improving both accuracy and efficiency.
Solution Approach 2:
The patent establishes continuous data flow from collection through integration to analysis using the message engine and data warehouse. The system maintains continuous operation state monitoring by continuously ingesting data from multiple sources, processing it through the integrated platform, and generating analysis results, ensuring no gaps in the useful action chain.
Data Source
AI summary
A data processing method, platform, and an electronic device are disclosed. The method includes: extracting original data into a message engine based on a preset standardized data interface, the original data including terminal data and system data generated by an application system. The method further includes: reading the original data from the message engine; storing the original data in a distributed file system to form a fact table; storing the fact table in a columnar database; obtaining target column data required for generating a target chart from the columnar database; and generating the target chart according to the target column data.