Data Aggregation Using Plan Trees and Historical Records
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Solution Overview
Problem
Current data aggregation methods require high operating and maintenance costs, and are inefficient with low operation efficiency and high professional requirements due to the use of large-scale computing resources and complex computing logic.
Innovation Solution
A method that divides original data into sets, determines historical aggregation records, and uses plan trees to optimize aggregation, reducing repetitive work and resource consumption by reusing historical results and trimming unnecessary nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large-scale computing resources and complex computing logic are used for data aggregation, then data aggregation completeness is improved, but operating costs and maintenance costs increase
Solution Approach 1:
The patent segments the data aggregation process into multiple stages using a plan tree structure, where different aggregation operations are organized hierarchically. This allows the system to process data in manageable chunks rather than requiring all computing resources simultaneously, reducing overall resource consumption while maintaining aggregation completeness.
Solution Approach 2:
The patent implements historical aggregation record checking before performing actual aggregation operations. By determining whether data sets have been previously aggregated and storing these results, the system avoids redundant computations, significantly reducing operating costs and maintenance costs while ensuring data aggregation completeness is maintained.
2Reliability
If large-scale computing resources and complex computing logic are used for data aggregation, then data aggregation completeness is improved, but operation efficiency decreases
Solution Approach 1:
The plan tree structure divides the aggregation process into hierarchical levels with multiple data sets processed in parallel stages. This segmentation enables efficient resource utilization and improves operation efficiency while maintaining complete data aggregation through systematic processing of all segments.
Solution Approach 2:
By checking historical aggregation records before processing, the system identifies and skips already-aggregated data sets. This preliminary action prevents redundant computations, significantly improving operation efficiency while ensuring that all necessary aggregations are eventually performed for completeness.
3Reliability
If complex computing logic is used for data aggregation, then data aggregation completeness is improved, but professional requirements increase
Solution Approach 1:
The plan tree structure provides a universal framework that can handle multiple aggregation operations through a standardized hierarchical approach. This multi-functional structure simplifies the computing logic by providing a consistent method for organizing and executing various aggregation tasks, reducing professional requirements while maintaining aggregation completeness.
Solution Approach 2:
The systematic checking of historical aggregation records before processing creates a standardized, easy-to-follow workflow. This preliminary action simplifies the operational logic by providing clear decision points (check history → skip if exists → process if not), making the system easier to operate while ensuring complete data aggregation.
4Reliability
If data aggregation is performed without reusing historical records, then data aggregation completeness is improved, but resource consumption increases
Solution Approach 1:
The patent implements a preliminary check of historical aggregation records before performing aggregation operations. By determining whether data sets have been previously aggregated and storing these results, the system avoids redundant computations, significantly reducing resource consumption while ensuring data aggregation completeness is maintained through systematic processing.
Solution Approach 2:
The system recovers and reuses historical aggregation results instead of discarding them. By storing previously computed aggregation results and retrieving them when needed, the system avoids redundant resource consumption while maintaining complete and accurate data aggregation across multiple operations.
Data Source
AI summary
The present application discloses a method for data aggregation, the method includes: acquiring original data to be aggregated and dividing the original data into at least one first data set; determining whether each of the at least one first data set has a corresponding historical aggregation record; when there is at least one second data set with a historical aggregation record in the at least one first data set, acquiring a historical aggregation result corresponding to each second data set to obtain at least one first aggregation result; performing aggregation on each third data set without a historical aggregation record to obtain at least one second aggregation result; and determining a third aggregation result of the original data according to the at least one first aggregation result and the at least one second aggregation result, and determining a data tag of the original data according to the third aggregation result.