RDF Tuple Compression Using Predicate Grouping and Delta Encoding
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Solution Overview
Problem
Existing methods for compressing Resource Description Framework (RDF) tuples suffer from low performance, with maximum insertion rates of RDF triples per second on standard machines rarely exceeding 700,000, failing to meet the demand for at least 1 Million RDF tuples per second required by current applications like CAD, CAE, and CAM systems.
Innovation Solution
A method involving encoding RDF tuples using indices, grouping by shared predicates, sorting within groups, and serializing differences between consecutive tuples to achieve efficient compression and decompression, utilizing techniques like Morton encoding and variable-length integers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing compression methods are used for RDF tuples, then data can be stored and transferred, but the insertion rate remains below 700,000 triples per second, failing to meet the 1 million triples per second requirement
Solution Approach 1:
The patent segments the RDF tuple compression process into distinct phases: encoding subjects and objects separately from predicates, grouping tuples by predicate, and processing differences between consecutive tuples. This segmentation allows parallel processing and optimization of each phase, achieving the required 1 million triples per second insertion rate that existing monolithic compression methods cannot attain.
Solution Approach 2:
The patent changes the parameter representation by encoding subjects and objects as integers rather than strings, and by computing differences between consecutive tuple encodings. This parameter transformation reduces the amount of data to be processed and transmitted, directly improving the insertion rate while reducing processing time and resource consumption.
2Quantity of substance
If RDF tuples are compressed using traditional methods, then some space reduction is achieved, but the parsing cost increases and redundancy is not fully eliminated
Solution Approach 1:
The patent performs preliminary encoding of subjects and objects into integer form before compression, and pre-groups tuples by predicate. This preliminary action eliminates redundancy upfront and structures the data in a way that simplifies subsequent parsing operations, reducing both data size and parsing complexity compared to traditional methods that handle full strings throughout the process.
Solution Approach 2:
The patent introduces integer encodings as an intermediary representation between the original RDF tuple strings and the final compressed form. This intermediary layer enables more efficient compression while maintaining a regular structure that is easier to parse, effectively mediating between space reduction goals and parsing simplicity.
3Adaptability or versatility
If more data is transferred for collaborative design processes, then better collaboration is enabled, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts and transmits only the essential differences between consecutive RDF tuples rather than transmitting complete tuples. By computing and transmitting only the delta changes in subject-object encodings, the method enables collaborative design processes with multiple users while minimizing network bandwidth consumption, as only the necessary modifications need to be transferred.
Data Source
AI summary
A method for compressing RDF tuples. The method including obtaining RDF tuples, obtaining a dictionary of indices, encoding for each RDF tuple the indices attributed to the subject and the object, grouping RDF tuples sharing the same predicate and for each group sorting the RDF tuples by considering the encoding of the subject and the object, and for each group of sorted RDF tuples, serializing the index of the shared predicate, serializing the encoding of the subject and the object of a first RDF tuple, and for each RDF tuple of the group of sorted RDF tuples subsequent to the first RDF tuple of the group, computing a difference between the encoding of the subject and the object of a current RDF tuple and the encoding of the subject and the object of a previous RDF tuple, and serializing the computed difference in a form of a variable-length integer.