Variant Decomposition Diagrams for Database Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional approaches for managing large amounts of data in variant tables require significant computing resources, leading to inefficiencies and limitations, especially in systems with limited processing capabilities, as they result in large, unwieldy tables that are difficult to process and manipulate efficiently.
Innovation Solution
The use of variant decomposition diagrams (VDDs), which decompose data tables into a series of nodes and links, allowing for efficient compression and faster evaluation, manipulation, and deployment, while maintaining the relationships of the original data, thereby reducing the computational resources needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uncompressed variant tables are used to represent large amounts of product variant data, then the data can be easily accessed and manipulated, but the computing resources required (time, processing speed, power) become excessive and may exceed available resources
Solution Approach 1:
The patent applies segmentation by decomposing the large variant table into a hierarchical structure of nodes and subtables. The root node represents the entire table, which is segmented into multiple subtables, each representing a portion of the data. This segmentation allows the system to access only relevant portions of the data rather than processing the entire uncompressed table, thereby improving computing resource efficiency while maintaining data accessibility.
2Productivity
If compression techniques like BDDs are used to reduce the size of variant tables, then computing resources are reduced, but the number of nodes increases making the representation less efficient
Solution Approach 1:
The patent transitions from a two-dimensional table structure to a multi-dimensional hierarchical node structure. By organizing data in layers with root nodes, intermediate nodes, and leaf nodes, the system achieves more efficient compression than traditional BDDs. This dimensional transformation allows the representation to scale better with data size, reducing the effective number of nodes that need to be processed while maintaining computing resource efficiency.
3Adaptability or versatility
If MDDs are used for compression, then non-binary data can be represented, but the implementation becomes more complex
Solution Approach 1:
The patent segments the complex MDD implementation into a simplified hierarchical structure where each node contains references to subtables. This segmentation approach maintains the ability to represent non-binary data (adaptability) while reducing implementation complexity by using a consistent node structure throughout the hierarchy, avoiding the need for complex MDD-specific operations.
4Quantity of substance
If variant tables are compressed to reduce size, then the tables become easier to handle, but the processing and manipulation speed may be affected
Solution Approach 1:
The patent applies preliminary action by pre-processing the variant table data into a compressed hierarchical structure during an offline compilation phase. The VDD compiler performs the decomposition and compression work in advance, creating the node structure and subtable references before runtime. This preliminary action ensures that during actual processing and manipulation, the system works with the pre-compressed structure, achieving both reduced data size and maintained processing speed.
Data Source
AI summary
Systems and methods for increasing computing efficiency include a compiler configured to compress a variant table into a variant decomposition diagram (VDD). VDDs are compact representations of one or more variant tables and comprise a series of nodes and links that can be used to evaluate (e.g., filter, iterate, access) the variant table.