Probability Landscape Alignment Between DLT and Semantic Databases
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Solution Overview
Problem
The integration of distributed ledger technology (DLT) and semantic databases, such as knowledge graph databases (KG), is hindered by their distinct data formats and data content, limiting interoperability and computational consistency, which restricts cross-platform recursive operations.
Innovation Solution
A method is introduced to enhance computational consistency by generating probability landscapes for both systems, determining differences, and iteratively aligning state transitions until thresholds are met, allowing for the convergence of outcomes to update the semantic database with DLT data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distinct data formats are used in DLT and semantic databases, then each system can maintain its own data structure and processing capabilities, but interoperability and computational consistency between systems deteriorates
Solution Approach 1:
The patent introduces a probability landscape as an intermediary representation that mediates between DLT and semantic database data formats. This probability landscape serves as a common ground that can be generated from both systems, enabling comparison and alignment without requiring direct compatibility between their native data formats. The intermediary allows each system to maintain its structural characteristics while achieving interoperability through the shared probability space.
Solution Approach 2:
The patent transforms data from both DLT and semantic databases into a unified probability parameter space. By changing the representation parameters to probability distributions over state changes, the system enables comparison and alignment of disparate data formats. This parameter transformation allows the systems to speak a common computational language while preserving the essential characteristics of their original data structures.
2Quantity of substance
If DLT stores data as discrete records without context, then storage efficiency and immutability are improved, but ability to derive insights and provide contextual information deteriorates
Solution Approach 1:
The patent segments the data representation into two complementary parts: the discrete immutable records from DLT and the contextual probability landscape. The discrete records maintain storage efficiency and immutability, while the probability landscape captures contextual information and relationships. This segmentation allows each component to fulfill its specific function without compromising the other.
Solution Approach 2:
The patent adds a probabilistic dimension to the data representation by creating a probability landscape that maps possible execution results and their likelihoods. This dimensional extension allows the system to store both the discrete factual records from DLT and the contextual insights from semantic databases in a unified structure, enabling derivation of insights while maintaining storage efficiency.
3Reliability
If iterative alignment of probability landscapes is performed, then computational consistency between DLT and semantic database is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements a feedback mechanism where the probability landscapes of DLT and semantic database are iteratively compared and adjusted. The differences between landscapes are fed back into the system to generate corrected state transition values, which are then used to update the semantic database. This feedback loop continues until convergence, ensuring computational consistency while providing a systematic approach to achieving alignment.
Solution Approach 2:
The patent replaces direct mechanical data synchronization between DLT and semantic database with a probabilistic alignment mechanism. Instead of forcing direct compatibility, the system uses probability distributions to represent uncertainty and iteratively aligns the landscapes. This substitution allows for more flexible and robust alignment that can handle differences in data representations without requiring rigid synchronization protocols.
Data Source
AI summary
A method and system for enhancing computational consistency between a distributed ledger and a semantic database in which a semantic database probability landscape representing expected state changes associated with the execution of a digital protocol and a distributed ledger and a distributed ledge probability landscape representing actual execution results of the digital protocol on the distributed ledger are iteratively varied to arrive at mutually consistent data and information sets. Weightings are returned to the semantic database and a revised digital protocol is returned to the distributed ledger for execution, both of which are derived from the mutually consistent data and information set. A processor and system arranged to provide communication between the distributed ledger and the semantic database are also disclosed.