Reasoning Graph Evaluation via Hash Matching
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of data due to computational limitations, particularly when dealing with complex queries and reasoning graphs.
Innovation Solution
The method involves generating hashes from data sets and using these hashes to determine if new data sets match existing ones, thereby allowing for the direct association of outcomes with new data sets without the need for full processing through the reasoning graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full reasoning graph evaluation is performed on all data sets, then processing accuracy is maintained, but computational burden and processing time increase significantly
Solution Approach 1:
The system performs preliminary hashing of data sets before full reasoning graph evaluation. By pre-computing hashes and storing them in a database, the system can quickly compare incoming data sets against the hash database to identify matches, avoiding the need to perform complete reasoning graph evaluation on every data set. This preliminary action significantly reduces processing time while maintaining accuracy for matching data sets.
Solution Approach 2:
The system extracts the essential identifying feature of data sets (their hash values) and separates this from the full data set content. By working with hashes rather than complete data sets during the matching phase, the system reduces computational complexity. Only when a hash match is found does the system retrieve and process the full data set through the reasoning graph, thereby extracting and eliminating unnecessary computational steps.
2Productivity
If hashing and comparison operations are implemented to shortcut processing, then processing speed increases, but system complexity increases
Solution Approach 1:
The system introduces a hash database as an intermediary layer between data set input and reasoning graph evaluation. This hash database stores pre-computed hashes and serves as a quick-reference lookup table. The intermediary hash comparison mechanism simplifies the overall system architecture by providing a clear separation between the fast hashing/comparison operations and the slower but more comprehensive reasoning graph evaluation, making the system's complexity more manageable and structured.
3Use of energy by moving object
If hash matching is used to identify duplicate data sets, then computational resources are saved, but the risk of hash collision increases
Solution Approach 1:
The system implements a feedback mechanism where hash match results trigger verification steps. When a hash match is found, the system retrieves the associated full data set and performs reasoning graph evaluation to confirm the match is valid. This feedback loop allows the system to maintain the efficiency benefits of hash matching while mitigating the risk of false positives from hash collisions through subsequent verification.
Data Source
AI summary
Embodiments disclosed herein relate to methods, systems, and computer programs for automatically determining an outcome associated with a reasoning graph, based on one or more data sets. The methods, systems, and computer programs compare hash values associated with different data sets to determine if they match to assign the outcome associated with a pre-existing hash to the later provided hash and data set associated therewith.