Parallel Data Analysis System Using Key-Value Classification
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Solution Overview
Problem
Conventional relational databases face difficulties in efficiently processing and analyzing massive quantities of data in parallel data processing architectures, particularly when complex data analysis such as classification and report generation is required.
Innovation Solution
A system comprising a master server and multiple slave servers, where the master server allocates data blocks to slave servers for parallel processing, using preset key-value pairs to classify and analyze data, and merges results for further analysis, including filtering and comparative analysis to generate warnings based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If relational databases are used for data analysis, then data processing can be performed, but it becomes very difficult to efficiently process and analyze massive quantities of data in parallel data processing architecture
Solution Approach 1:
The patent segments massive data into multiple data blocks and distributes them across multiple slave servers for parallel processing. Each server handles a specific portion of the data, enabling scalable processing of large volumes without overwhelming a single system, thus resolving the contradiction between processing efficiency and system complexity.
Solution Approach 2:
The patent transitions from traditional single-dimension relational database processing to multi-dimensional parallel processing architecture. By adding the dimension of parallelism across multiple servers and implementing multi-stage processing pipelines, the system achieves higher productivity while managing complexity through structured organization of processing stages.
2Adaptability or versatility
If conventional relational databases are used, then basic data processing is possible, but complex data analysis such as classification and report generation becomes particularly difficult
Solution Approach 1:
The patent creates a universal parallel processing framework that can handle multiple types of data analysis tasks including classification, report generation, filtering, and aggregation. The master server coordinates multiple slave servers that can perform various analysis operations, making the system adaptable to different complex analysis requirements while managing architecture complexity through standardized interfaces.
Solution Approach 2:
The patent implements dynamic multi-stage processing where the data flow can be routed through different processing stages based on analysis requirements. The system dynamically adjusts processing pipelines, filtering criteria, and aggregation operations to match specific analysis needs, enhancing versatility while maintaining manageable complexity through modular stage design.
3Quantity of substance
If massive quantities of data are processed, then comprehensive analysis is achieved, but processing time and system resource consumption increase
Solution Approach 1:
The patent divides massive data volumes into smaller data blocks that are processed in parallel across multiple slave servers. This segmentation enables the system to handle large quantities of data simultaneously, reducing overall processing time while maintaining comprehensive analysis coverage across the entire dataset.
Solution Approach 2:
The patent implements continuous parallel processing where multiple slave servers continuously process different data blocks simultaneously without idle time. The master server continuously coordinates and aggregates results from all slave servers, ensuring that the entire system operates at full capacity throughout the processing period, thereby reducing total processing time for massive data volumes.
Data Source
AI summary
Data analysis is disclosed, including: receiving data to be analyzed, wherein the data includes one or more data identifiers (IDs) and one or more preset key-value pairs, wherein each preset key-value pair includes a preset key and a preset value; acquiring data to be analyzed based at least in part on the data IDs; segmenting the acquired data into one or more data elements; classifying the one or more data elements based at least in part on one preset key of the one or more preset key-value pairs; and analyzing the classified one or more data elements based at least in part on one preset value of the one or more preset key-value pairs.