Contextual API Optimization Using Notation-Based Data Compression
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Solution Overview
Problem
Existing computer applications face performance bottlenecks and network overload due to varying document sizes in API interactions, particularly in multi-cloud and hybrid cloud environments, which are not effectively addressed by current optimization methods.
Innovation Solution
A system utilizing a machine learning engine for contextual analysis of interface data between applications, generating fix packs that apply transformation notations and compression techniques based on contextual understanding, with feedback loops and blockchain ledger storage for traceability and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If document size in API interactions is increased to include more attributes and details, then information completeness is improved, but network bandwidth consumption increases and performance bottlenecks occur
Solution Approach 1:
The patent segments the data exchange by identifying and separating frequently occurring values from the full document attributes. Instead of transmitting complete documents with all attributes for every interaction, the system segments the data into essential identifiers and contextual information, reducing network bandwidth consumption while maintaining information completeness where needed.
Solution Approach 2:
The patent creates simplified copies or representations of the full document data. By using notation schemes that reference frequently occurring values and maintain only essential document attributes in transmissions, the system creates a compressed representation that reduces network traffic while allowing reconstruction of complete information when necessary.
2Loss of information
If document size in API interactions is increased to include more attributes and details, then information completeness is improved, but system performance deteriorates due to processing overhead
Solution Approach 1:
The system segments processing by applying notation schemes that identify frequently occurring values separately from document structure. This segmentation allows the system to process only the essential variable attributes while treating frequently repeating values as predefined notations, significantly reducing processing overhead and improving system performance.
Solution Approach 2:
The patent changes the parameter representation by transforming full document attributes into compact notation schemes. By encoding frequently occurring values as notations and only transmitting essential document attributes, the system reduces the amount of data that needs to be processed, thereby improving productivity while maintaining information completeness.
3Measurement precision
If contextual analysis and machine learning processing are applied to interface data, then API optimization accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing interface data to identify frequently occurring values and establish notation schemes before actual optimization occurs. The machine learning engine analyzes historical interface data in advance to create contextual understanding and predefined notations, which then simplify subsequent optimization processes and reduce real-time computational complexity.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning engine continuously analyzes the results of API optimizations and uses this feedback to refine notation schemes and contextual understanding. This feedback loop improves optimization accuracy over time while the system learns to make more efficient decisions, ultimately reducing computational complexity through improved pattern recognition.
Data Source
AI summary
A computer-implemented process includes the following operations. Interface data for a first computer application having a first interface configured to exchange data with a second computer application is identified. The interface data is aggregated using a machine learning engine, and the machine learning engine performs contextual analysis on the aggregated interface data to identify a context. A fix pack for the first computer application is generated using the context from the contextual analysis. The fix pack is caused to be applied to the first computer application. The fix pack includes an installable for the first application to transform notations used by the second computer application when communication with the first application.