Schema-Based Binary Encoding for Configuration Data Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Devices with limited memory struggle to store and manage configuration data in human-readable text formats, which are verbose and large in size, making it difficult for devices like network interface cards (NICs) and system on chip (SoC) devices to handle due to memory constraints.
Innovation Solution
Encoding data formatted in human-readable text according to a schema into binary, using a schema dictionary for canonical ordering and sequence numbering, which removes labels and maximally compresses data while allowing for reversible decoding back into human-readable text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is formatted in human-readable text according to a schema, then ease of operation and readability are improved, but data size and memory requirements increase
Solution Approach 1:
The patent changes the representation parameter of data from human-readable text format to binary format. This parameter change fundamentally alters how data is stored and processed, enabling compact representation while maintaining the ability to decode back to human-readable text when needed, thus resolving the contradiction between readability and data size.
Solution Approach 2:
The patent creates a compressed binary copy of the original human-readable text data. This binary copy maintains all the information from the original text but in a much more compact form, allowing devices with limited memory to store and process configuration data efficiently while still being able to reference the human-readable version when needed.
2Ease of operation
If data is stored in human-readable text format, then ease of operation is improved, but device complexity increases due to memory constraints
Solution Approach 1:
The patent changes the storage parameter from text-based to binary-based, fundamentally altering how configuration data is handled in devices with limited memory. This parameter change reduces memory requirements while maintaining operational ease through the ability to decode binary data back to human-readable text when necessary.
Solution Approach 2:
The patent introduces binary format as an intermediary between human-readable text and device storage. This intermediary layer allows devices with limited memory to store compressed binary data while still being able to interface with human-readable configurations when needed, thus reducing device complexity without sacrificing ease of operation.
3Quantity of substance
If data is encoded into binary format, then data size is reduced, but device complexity increases due to encoding/decoding requirements
Solution Approach 1:
The patent performs the encoding action in advance, creating a standardized binary representation scheme that can be consistently applied. By establishing the encoding rules beforehand through schema definitions, the actual encoding process becomes systematic and manageable, reducing the complexity burden on devices during data storage operations.
Solution Approach 2:
The patent changes the data representation parameter to binary format, which fundamentally simplifies storage requirements. While encoding and decoding are required, the systematic nature of binary encoding based on predefined schemas makes the complexity manageable and consistent, allowing devices with limited resources to handle the transformation efficiently.
Data Source
AI summary
Data is organized in a hierarchical data tree having nodes, and is formatted in human-readable data according to a schema. The data is canonically ordered in correspondence with a canonical ordering of a schema dictionary generated from the schema. The canonically ordered data is encoded into binary, including for each node, removing a label of the node, and adding a sequence number of the node corresponding to the canonical ordering, in binary.