Compact Binary Encoding for XML Data Processing
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Solution Overview
Problem
The standard text-based XML serialization format results in large document sizes, leading to increased transmission and storage overhead, slower performance due to limited bandwidth, and costly parsing and type conversion processes.
Innovation Solution
A compact binary encoding format for XML data that tokenizes tags, optimizes repetitive elements, encodes values in native types, and exploits schema metadata to minimize overhead, allowing for efficient processing and storage without decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If text-based XML serialization format is used, then information self-descriptiveness is maintained, but document size increases significantly
Solution Approach 1:
The patent changes the encoding parameter from text-based to binary-based representation. Specifically, it uses bit-level encoding where elements are represented by their index in a schema rather than full tag names, and values are encoded in their native binary types rather than as text strings. This parameter transformation maintains information completeness while dramatically reducing document size.
Solution Approach 2:
The patent extracts and removes redundant information from the XML representation. By using a schema to define element structures and using indices to reference schema definitions, it eliminates the need to repeatedly encode full element names and attributes. The schema itself serves as the reference dictionary, allowing the encoded data to contain only essential information.
2Adaptability or versatility
If text-based XML format is used, then compatibility and self-descriptiveness are maintained, but transmission overhead increases due to limited network bandwidth
Solution Approach 1:
The patent transforms the data representation parameter from text to binary encoding. This changes the fundamental way information is transmitted over the network, reducing the number of bits required for each element. The binary encoding maintains compatibility through schema-based definition, allowing receiving systems to reconstruct the original XML structure from the compact binary data.
3Productivity
If standard XML parsing is used, then data can be processed, but parsing cost and processing time increase significantly
Solution Approach 1:
The patent performs parsing actions in advance during the encoding process itself. Instead of parsing XML documents during application execution, the system pre-processes and encodes XML data into a compact binary format that is ready for direct processing. This preliminary encoding action eliminates the need for costly parsing operations later, as the data is already in an optimized structure.
Solution Approach 2:
The patent inverts the traditional processing sequence. Instead of reading text-based XML and then parsing it into a usable format, the system encodes data directly into the optimized binary format during storage or transmission. This reversal of the processing order eliminates the parsing step entirely, as the data arrives already in its final processed state.
4Productivity
If text-based values are used, then data can be stored and transmitted, but type conversion costs degrade application performance
Solution Approach 1:
The patent changes the data type parameter from text representation to native binary types. Instead of storing numbers as text strings that require conversion, the system stores them as actual integer values. Similarly, dates are stored as timestamp values rather than text strings. This parameter transformation eliminates the need for type conversion operations, significantly improving processing performance.
Data Source
AI summary
A compact binary encoding technique for information that is logically hierarchically structured, such as XML data, maintains all of the features of XML data in a useable form, such as the hierarchical structure underlying the data. Hence, data encoded in this format can undergo XML-based processing on-the-fly as it is being received or fetched, as if the data was being processed linearly in its textual character-based format. Processing of data encoded in this format can begin without having to wait for and decode the entire data set. The overhead due to XML tags is significantly minimized. The encoded data can be processed more efficiently because the data is pre-parsed. Values may be stored in their native type formats and, therefore, processing of the encoded data avoids costly type conversions. Further, any available structural constraint information can be effectively exploited.