Data Storage Schema for Binary Transmission Efficiency
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Solution Overview
Problem
Current data transmission between servers and clients is CPU-intensive and bandwidth-intensive due to the need for data manipulation from human-readable formats like JSON and XML to machine-readable formats, requiring complex software and resulting in software brittleness and inefficiencies in data storage and processing.
Innovation Solution
The process involves defining data elements by a schema into rows with related columns, distinguishing between fixed and dynamic data elements, and storing them in optimized memory allocations to enable direct access and transmission without conversion, using fixed-length allocations for fixed data and dynamic allocations for dynamic data, with schema identifiers for serialization and deserialization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is transmitted in human-readable formats like JSON and XML, then data can be easily interpreted by humans, but bandwidth consumption increases and CPU-intensive processing is required to convert to machine-readable formats
Solution Approach 1:
The patent segments data into fixed-length records with defined schemas, allowing direct transmission of machine-readable binary formats without requiring conversion from human-readable formats. This segmentation enables efficient parsing and processing while reducing bandwidth consumption.
Solution Approach 2:
The patent replaces the mechanical conversion process between human-readable and machine-readable formats with direct binary data transmission. By using predefined schemas and fixed-length records, the system eliminates the need for CPU-intensive parsing and conversion operations.
2Ease of operation
If data is transmitted in human-readable formats like JSON and XML, then data can be easily interpreted by humans, but CPU utilization increases due to processing overhead
Solution Approach 1:
The patent segments data into fixed-length records with defined schemas, allowing direct transmission of machine-readable binary formats without requiring conversion from human-readable formats. This segmentation enables efficient parsing and processing while reducing bandwidth consumption.
Solution Approach 2:
The patent replaces the mechanical conversion process between human-readable and machine-readable formats with direct binary data transmission. By using predefined schemas and fixed-length records, the system eliminates the need for CPU-intensive parsing and conversion operations.
3Ease of operation
If data is stored fragmented in memory around single rows or elements, then data access is simple, but processing overhead increases and memory efficiency decreases
Solution Approach 1:
The patent merges fragmented data rows into contiguous fixed-length records in memory. By combining related data elements into unified record structures with defined schemas, the system improves memory efficiency and enables more effective data processing operations.
4Reliability
If complex software is used to interpret data, then data can be accurately processed, but software brittleness increases and maintenance becomes difficult
Solution Approach 1:
The patent segments data into fixed-length records with defined schemas, allowing direct transmission of machine-readable binary formats without requiring conversion from human-readable formats. This segmentation enables efficient parsing and processing while reducing bandwidth consumption.
Solution Approach 2:
The patent changes the data format parameters from variable-length human-readable formats to fixed-length binary formats with predefined schemas. This parameter change enables more reliable and efficient data processing while reducing software complexity.
Data Source
AI summary
Systems and processes for efficient accessing, storing and transmitting of fixed data elements and dynamic data elements, each having its own native form. The data elements are organized according to a schema, with (a) all fixed data elements stored in their native forms in a fixed memory allocation, and (b) each dynamic data element stored in memory in its own native form, in its own data allocation. With this memory structure, computational overhead of converting data elements from their native forms to JSON, XML or other markup language is avoided, making accessing data (getting), updating data (setting), converting data to a serial stream for transmission or other manipulation (serializing), deserializing, and other manipulations of the data elements much more CPU efficient and requiring less bandwidth.