Contextually Blind Data Conversion via Indexed String Matching
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Solution Overview
Problem
Existing machine-based tools face challenges in converting contextually dependent data, especially when the context is indeterminate or unclear, as they struggle to accurately convert data without prior knowledge of the context in which it is used, particularly in unsupervised machine learning scenarios where contextually indeterminate data lacks clear indicators.
Innovation Solution
The use of an indexed string matching process that identifies similar external information based on features rather than context, allowing for contextually dependent conversions without determining the specific context, by representing strings as features and calculating a similarity metric to match data with relevant schemas, even in the absence of clear context information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional context-based conversion methods are used, then conversion accuracy for contextually dependent data is improved, but the system fails when context information is indeterminate or unavailable
Solution Approach 1:
The patent introduces an indexed string matching process as an intermediary mechanism between the data conversion system and contextually indeterminate data. This intermediary enables the system to match data strings with appropriate conversion schemas through feature-based comparison and similarity metrics, bypassing the need for explicit context information while maintaining conversion accuracy
2Manufacturing precision
If context determination is performed before conversion, then conversion quality is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing conversion schemas with their associated features in an indexed database before actual data conversion occurs. This allows the system to quickly retrieve and apply appropriate schemas during conversion without performing complex context analysis in real-time, thereby maintaining high conversion quality while reducing computational resource consumption during the actual conversion process
Data Source
AI summary
Computer-based tools and methods for conversion of data from a first form to a second form without reference to the context of data to be converted. The conversion may be facilitated by matching source data with external information (e.g., public and/or private schema) that contain rules (e.g., context specific rules) for conversion of the data. The matching may be performed based on an optimized index string matching technique that may be operable to match source data to external information that is context dependent without specific identification of the context of either the source data or the external information identified. Accordingly, the conversion of data may be performed in an unsupervised machine learning environment.