Multi-dimensional Decomposition Computing via Recursion Topology
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Solution Overview
Problem
Existing multi-dimensional decomposition techniques using relational databases face high computation complexity and limited data processing due to their two-dimensional table structure, which is inadequate for handling big data.
Innovation Solution
A method and system that generate a recursion topology from pre-processed big data, define optimized fixed dimension combinations and computation paths based on a default strategy, and activate computation tasks to reduce complexity and improve processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If relational database with two-dimensional tables is used for multi-dimensional decomposition, then data organization is simple and easy to implement, but computation complexity is high and data processing amount is limited
Solution Approach 1:
The patent segments the computation process into multiple independent computation tasks, each handling specific dimension combinations. The recursion topology divides the multi-dimensional decomposition problem into smaller sub-problems that can be processed separately and then combined, reducing the overall computation complexity while maintaining ease of implementation.
Solution Approach 2:
The patent transitions from traditional two-dimensional table structures to a multi-dimensional recursion topology that incorporates additional dimensions for organizing computation tasks. This dimensional expansion allows efficient handling of big data by structuring the computation space to match the multi-dimensional nature of the data, thereby reducing computation complexity.
2Ease of manufacture
If relational database with two-dimensional tables is used for multi-dimensional decomposition, then data organization is simple and easy to implement, but the amount of data processed is limited
Solution Approach 1:
The recursion topology structure serves multiple functions: it organizes computation tasks, manages data flow between different dimension combinations, and scales to handle various sizes of data sets. This multi-functional design allows the system to process large amounts of big data while maintaining the simplicity of implementation through a unified structural approach.
3Device complexity
If optimized fixed dimension combination and computation path are used, then computation complexity is reduced, but system flexibility may be limited
Solution Approach 1:
The system employs dynamic computation paths that can adapt to different query requirements. While fixed dimension combinations provide optimization for common cases, the recursion topology allows dynamic selection and combination of computation paths based on specific data requests, maintaining system flexibility without sacrificing computation efficiency.
Solution Approach 2:
The recursion topology acts as an intermediary layer between the optimized fixed dimension combinations and the actual data queries. It mediates between the structured computation tasks and diverse data requests, allowing the system to maintain optimized computation paths while adapting to various flexibility requirements through the topological structure.
Data Source
AI summary
Disclosed is a multi-dimensional decomposition computing method and system, related to the field of multi-dimensional decomposition computing technique, applicable to performing multi-dimensional decomposition on big data to reduce computation complexity. The method executes: generating an recursion topology based on pre-processed big data; the recursion topology comprising dimension combination and recursion path among the dimension combinations; based on default fixed strategy, defining a fixed dimension combination and defining a computation path forming an optimized fixed dimension combination; based on the recursion topology, generating computation tasks; and based on the fixed dimensional combination and the computation path of the optimized fixed dimension combination, activating the computation tasks, computing the computation tasks and obtaining computing results. The disclosed solution is applicable to multi-dimensional decomposition.