Graph Data Compression Using Local IDs and Tree Partitioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for compressing graph data, such as the compressed sparse row (CSR) method, require significant storage space, which increases system costs, and there is a need for a more efficient compression method to reduce storage requirements.
Innovation Solution
A method and apparatus that divide graph data into multiple pieces, generate compressed data for each piece, and store the compressed data using a tree structure to minimize storage space, employing local identification information to reduce the data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression methods such as CSR are applied to graph data, then the data can be stored in a structured format, but a lot of storage space is still required which increases system cost
Solution Approach 1:
The patent divides graph data into multiple pieces and applies compression to each piece separately. This segmentation allows for more efficient compression by processing smaller, manageable units rather than the entire graph at once, thereby reducing the total storage space required while maintaining data accessibility.
Solution Approach 2:
The patent uses local identification information within each compressed piece instead of global identifiers. This local quality approach reduces the number of bits required to represent vertex identifiers within each piece, significantly decreasing the storage space needed for each compressed unit and overall system cost.
2Quantity of substance
If graph data is compressed using existing methods, then storage efficiency is improved to some extent, but the compression ratio is insufficient and more storage space is still needed
Solution Approach 1:
By segmenting graph data into smaller pieces, the patent enables more aggressive compression techniques to be applied locally. Each piece can be compressed independently with higher compression ratios, improving overall compression efficiency while maintaining the ability to retrieve specific graph portions efficiently.
Solution Approach 2:
The patent changes the parameter of vertex identification from global identifiers to local identifiers within each compressed piece. This parameter change dramatically reduces the storage requirements for vertex identifiers, thereby improving compression efficiency and reducing the total storage space needed.
Data Source
AI summary
Provided are a graph data compression method and apparatus. A computing device divides graph data including information about a source vertex and a destination vertex of an edge into a plurality of pieces, generates a plurality of compressed data obtained by compressing the plurality of pieces, and stores the plurality of compressed data as a file. The computing device may generate a plurality of pieces having a size equal to or less than a predefined data size by recursively dividing graph data.