Template-Based Data Processing System for Reducing Development Time
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Solution Overview
Problem
Existing methods for processing complex data require high investment and long development times, and they have low efficiency in data processing.
Innovation Solution
A method for processing complex data that involves generating target collection and screening templates based on a target data dictionary and user-input execution parameters, transmitting query instructions to a data processing engine to acquire an initial data set, standardizing the data, and importing it into a target collection template to create a target data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If specific analysis tasks are developed separately for each data source, then data can be extracted and processed from multiple sources, but the development time and investment increase significantly
Solution Approach 1:
The patent creates a universal data processing system that can handle multiple data sources through a single integrated platform. The system uses unified data dictionaries, collection templates, and screening templates that work across different data sources, eliminating the need to develop separate analysis tasks for each source. This multi-functional approach allows one system to serve multiple purposes and data sources simultaneously.
Solution Approach 2:
The patent employs template copying and reuse mechanisms where collection templates and screening templates can be replicated and adapted for different data sources. Instead of developing new tasks from scratch, users can copy existing templates and modify them as needed, significantly reducing development time while maintaining adaptability to various data sources.
2Adaptability or versatility
If separate analysis tasks are developed for each data source, then comprehensive data coverage is achieved, but processing efficiency decreases
Solution Approach 1:
The patent merges multiple data source processing capabilities into a single integrated system. By combining collection templates, screening templates, and data processing logic into one unified platform, the system achieves comprehensive data source coverage while improving processing efficiency. The merged system eliminates redundant operations and enables centralized management of all data sources.
Solution Approach 2:
The patent performs preliminary actions by pre-defining data dictionaries, collection templates, and screening templates that can be reused across different data sources. These pre-prepared structures enable rapid deployment and processing when new data sources need to be incorporated, significantly improving processing efficiency while maintaining comprehensive coverage.
3Adaptability or versatility
If traditional data processing methods are used, then data can be extracted from multiple sources, but the investment and development time are high
Solution Approach 1:
The patent segments the data processing system into distinct, manageable components: data dictionaries, collection templates, screening templates, and processing engines. Each component has a specific function and can be independently configured or modified. This segmentation reduces overall system complexity while maintaining the capability to process data from multiple sources, as each segment can be developed and maintained separately.
Data Source
AI summary
Disclosed in the present application is a method for processing complex data, which relates to the technical field of data processing. The method includes: generating a query instruction set by execution parameters of a target screening template through a data processing model; transmitting the query instruction set to a data processing engine for the data processing engine to obtain data from each connected target data source based on the query instruction set; and after the data is standardized through the data processing model, importing the data into a target collection template for analysis. A worker only needs to set the corresponding collection template and screening template according to a research scenario, and there is no need to develop a software according to the research scenario, which reduces preliminary preparation time and improves efficiency of data processing.