RDF Graph Storage Using Compressed Predicate Adjacency Matrices
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Solution Overview
Problem
Current methods for storing RDF graph data in graph databases are inefficient, particularly in handling large datasets, as they do not effectively compress data while allowing dynamic read/write operations, leading to high storage costs and environmental impact.
Innovation Solution
The method involves obtaining adjacency matrices representing RDF tuples, storing them as data structures with arrays of indices pointing to sub-divisions, and implementing functions for efficient query operations, such as checking, setting, and scanning cells, using a tree-like data structure that compresses adjacency matrices and allows dynamic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression techniques are applied to reduce storage size, then storage cost is reduced, but query efficiency and dynamic operations become slower
Solution Approach 1:
The patent segments the RDF graph data into multiple adjacency matrices, where each matrix represents a specific predicate. This segmentation allows the system to compress each matrix independently using bitmaps, reducing overall storage size while maintaining the ability to query specific predicates efficiently without decompressing entire datasets.
Solution Approach 2:
The patent implements a nested data structure where bitmaps are nested within adjacency matrices, which are in turn nested within predicate-specific groups. This nested structure enables hierarchical compression where common patterns at multiple levels can be exploited, achieving high compression ratios while allowing efficient navigation and query operations through the nested hierarchy.
2Quantity of substance
If standard storage formats are used for RDF triples, then data accessibility is maintained, but storage cost and environmental footprint increase
Solution Approach 1:
The patent changes the representation parameters of RDF data by converting traditional triple storage into adjacency matrix format with bitmap compression. This parameter transformation achieves significant space reduction (compressing terabyte-scale datasets), directly reducing storage hardware requirements and associated environmental footprint while maintaining data accessibility through efficient bitmap query operations.
3Ease of manufacture
If compression techniques are applied to RDF data, then storage efficiency improves, but dynamic read/write operations become more complex
Solution Approach 1:
The patent implements self-service mechanisms where the bitmap structure automatically manages compression and decompression during query operations. The adjacency matrix with bitmap encoding allows the system to perform efficient queries directly on compressed data without requiring complex manual decompression routines, reducing operational complexity while maintaining high storage efficiency.
Data Source
AI summary
A computer-Implemented method of storing RDF graph data in a graph database including a set of RDF tuples. The method includes obtaining one or more adjacency matrices wherein each adjacency matrix represents a group of tuples of the graph database comprising a same predicate. The method further includes storing, for each of the one or more adjacency matrices, a data structure includes an array. The array includes one or more indices each pointing to a sub-division of the adjacency matrix, and/or one or more elements each representing a group of tuples of the RDF graph database of a respective sub-division of the adjacency matrix.