XML Data Compression via Binary Tag-Length-Value Encoding
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Solution Overview
Problem
Existing XML processing technologies face inefficiencies due to verbosity and high processing time and space requirements, particularly in handling textual data with limited vocabularies, where compression methods like WBXML and Huffman encoding have limitations such as needing pre-agreed encoding and statistical analysis, and are sub-optimal for protocols with small, predefined value sets.
Innovation Solution
A system and method for processing markup language information that involves a first data processor for compressing XML data by assigning labels and embedding binary data in comment fields, allowing for efficient compression and decompression, using a second data processor to decode and associate labels with original data, thereby reducing the need for string operations and achieving more compact binary encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If XML data is represented using plain text format, then the data can be easily read and processed by standard tools, but the information density is low and processing time and space requirements are high
Solution Approach 1:
The patent changes the representation parameter of XML data from plain text to binary format. This parameter change reduces the size of data representation and accelerates processing operations, directly addressing the contradiction between ease of processing and processing time.
Solution Approach 2:
The patent substitutes string-based text processing with binary data processing. This replacement eliminates the need for complex string operations and pattern matching, significantly reducing processing time while maintaining ease of processing through standardized binary protocols.
2Ease of manufacture
If XML data is represented using plain text format, then the data structure is human-readable and simple to implement, but the information density is low and space requirements are high
Solution Approach 1:
The patent changes the data representation parameter from text to binary format, which compactly encodes XML elements, attributes, and values. This parameter change directly reduces the quantity of data transmitted and stored while maintaining implementation simplicity through standardized encoding rules.
3Quantity of substance
If string compression algorithms like Huffman encoding are used, then the data compaction is dense, but statistical analysis of input data is required and both entities must agree in advance on the encoding
Solution Approach 1:
The patent segments XML data into distinct components (elements, attributes, values) and applies specific binary encoding rules to each segment type. This segmentation eliminates the need for global statistical analysis and pre-agreed encodings, as each segment is independently encoded according to fixed rules, reducing device complexity while maintaining data compaction.
Solution Approach 2:
The patent changes from statistical encoding (Huffman) to fixed-rule binary encoding. This parameter change in the encoding approach eliminates the need for statistical analysis and pre-established agreements, reducing complexity while achieving effective data compaction through efficient binary representation.
4Productivity
If WBXML compression is used, then the processing efficiency is improved, but not all XML DTD protocols provide a WBXML encoding table and pre-agreed encoding is required
Solution Approach 1:
The patent creates a universal binary encoding system that can handle various XML DTD protocols without requiring protocol-specific encoding tables. This universal approach enhances adaptability and versatility across different protocols while maintaining the processing efficiency benefits of binary encoding.
Solution Approach 2:
The patent segments the encoding process into protocol-agnostic components that can be applied universally across different XML DTD protocols. This segmentation eliminates the need for protocol-specific pre-agreed encodings, enhancing compatibility while preserving processing efficiency.
Data Source
AI summary
A system and method for processing of markup language information, such as extensible markup language (XML) based control information is disclosed. The system comprises a compression sender entity and a decompression receiver entity. The sender entity uses its internal, binary representation derived from textual XML-based protocol header and encodes the binary representation as a tag-length-value (TLV) binary value. The TLV binary value is embedded as encoded data in a specifically marked comment, which may be detected by the receiver entity. The receiver entity builds up an internal, binary data structure based on the encoded data representing the original XML data. The system avoids the usage of string data to represent a small set of well known strings and uses a more compact binary encoding which reduces space and time to process the data.