Data Validity Framework for Distributed Processing Stacks
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Solution Overview
Problem
Data corruption during processing in distributed computing networks can lead to incorrect data processing and loss for online entities and users, as data is routed through multiple stacks with different data formats, causing translation errors and inconsistencies.
Innovation Solution
A data validity framework that generates a unique hash value in a base data format using a hashing algorithm like SHA, ensuring data integrity by comparing hash values across processing stacks to detect corruption, and utilizing data translators to convert data into the base format for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is routed through multiple processing stacks with different data formats, then data processing versatility is improved, but data integrity deteriorates due to translation errors and corruption
Solution Approach 1:
The patent introduces a base data format as an intermediary standard that all processing stacks translate to and from. This mediator format enables data to be routed through multiple stacks with different formats while maintaining integrity, as each stack translates to the base format and back, preventing corruption during translation operations.
Solution Approach 2:
The patent establishes a homogeneous base data format that provides consistency across heterogeneous processing stacks. By converting all data to this common format at specific points in the processing pipeline, the system achieves uniformity in data representation, enabling reliable validation and comparison across different stack implementations.
2Adaptability or versatility
If data translation between different formats is performed, then adaptability across processing stacks is improved, but data corruption risk increases
Solution Approach 1:
The patent performs preliminary validation of data before it enters the translation process by checking hash values at the beginning of processing. This preliminary action identifies potentially corrupted data early, preventing further translation operations on already compromised data and minimizing the propagation of errors through the system.
Solution Approach 2:
The patent implements feedback mechanisms through hash value comparison at multiple stages of data processing. By continuously validating data integrity through hash matching after translation operations, the system provides immediate feedback on data state, enabling detection and correction of corruption before it affects downstream processing.
3Reliability
If hash validation is performed at each processing stack, then data integrity is improved, but processing time increases
Solution Approach 1:
The patent applies partial validation by performing hash checks only at critical transition points between processing stacks rather than at every single operation. This selective approach validates data integrity where it matters most (at format translation boundaries) while avoiding redundant validation operations, thus maintaining reliability without excessive time overhead.
Data Source
AI summary
There are provided systems and methods for determining data validity during data processing for multiple processing stacks. During processing requests with a service provider, each request may go through a data flow that invokes multiple processing stacks, where the data is transmitted over a network to different data processing nodes. For example, a distributed computing architecture may invoke multiple disparate nodes to process data, which may become corrupted during data transmission and processing. To ensure data validity, a framework may be provided that provided data translators for each processing stack to covert data handled in a processing format for that stack into a base data format utilized by the framework. The framework may utilize checksums or other hash values of the data in the base data format to determine if the data has been altered at different processing nodes or stacks.