Binary Serialization Metadata for Random-Access Data Transformation
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Solution Overview
Problem
Existing data transformation techniques for hierarchical data formats, such as XML, CSV, and JSON, require significant processing and memory resources due to the need to read and transform entire files, especially when only a portion of the data is needed.
Innovation Solution
The implementation of a file formatting component that modifies files to include metadata, such as indexes and byte descriptions, allowing for efficient data transformation by storing data in a binary format that supports random access and reduces memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in hierarchical formats (XML, CSV, JSON), then data can be accessed and transformed, but significant processing and memory resources are required to read and transform entire files
Solution Approach 1:
The patent segments the data file into fixed-size records with predefined structures. Each record contains specific data elements at known memory offsets, allowing the system to process only the necessary portions of the file rather than loading entire hierarchical structures into memory. This segmentation enables partial file processing and reduces memory consumption during data transformation operations.
Solution Approach 2:
The patent applies preliminary action by pre-defining the binary structure and layout of data records before actual data processing occurs. File formats are established with fixed record lengths, field positions, and data types in advance, eliminating the need for runtime parsing and dynamic memory allocation. This pre-structured approach allows direct access to specific data elements without processing the entire file hierarchy.
2Adaptability or versatility
If entire files are read into memory for transformation, then complete data access is enabled, but memory usage increases significantly
Solution Approach 1:
The patent extracts only the necessary data elements from the file based on predefined record structures. By using fixed-offset binary formats with known field positions, the system can extract specific records or data elements directly from the file without loading the entire file content into memory. This selective extraction maintains data access versatility while minimizing memory consumption.
Solution Approach 2:
The file is divided into discrete, fixed-size records that can be independently accessed. Each record segment contains specific data fields at predetermined memory offsets, enabling the system to load and process only the required segments rather than the complete file. This segmentation strategy allows random access to specific data portions while keeping memory usage proportional to the actual data needs.
3Adaptability or versatility
If hierarchical data structures are used, then data can be organized flexibly, but processing time increases due to the need to parse and transform entire structures
Solution Approach 1:
The patent applies preliminary action by establishing fixed binary structures and record layouts before data processing. Data formats are predefined with explicit field positions, lengths, and types, eliminating the need for runtime parsing of hierarchical structures. This pre-structured approach enables direct binary reading and transformation without the overhead of interpreting nested hierarchical elements, significantly reducing processing time while maintaining data organization through structured record formats.
4Reliability
If data is stored in formats requiring full file reading, then complete data transformation is achieved, but processor overhead increases
Solution Approach 1:
The patent segments data into fixed-size records with predetermined structures, allowing the processor to handle only the necessary record portions rather than entire hierarchical files. Each record contains self-contained data elements at known offsets, enabling efficient processing that maintains transformation completeness while reducing computational overhead through targeted record-by-record processing.
Data Source
AI summary
A data formatting service may be configured to format files or documents for data transformation for utilization of the data by other services. The data formatting service may be configured to perform a method including receiving a dataset including a first file that includes a first set of elements formatted according to a first hierarchical structure, parsing the first file according to the first hierarchical structure to identify the first set of elements, modifying, based at least in part on the first set of elements, the first file to include metadata that describes a first element of the first set of elements, and storing the modified file in a binary format for access to the first element based at least in part on the metadata.