Cognitive Learning Framework for Dark Data Processing
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and analyzing large volumes of complex data, particularly 'dark data,' which is often neglected or underutilized, hindering organizations and individuals from deriving actionable insights.
Innovation Solution
A cognitive information processing system comprising a processor, data bus, and non-transitory computer-readable storage medium with computer program code that performs cognitive learning operations through a cognitive inference and learning system, applying cognitive learning techniques to generate insights and update destinations based on processed data from multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used, then processing time and resource consumption increase, but the system cannot efficiently handle big data volumes
Solution Approach 1:
The patent replaces traditional mechanical data processing systems with a cognitive computing system that uses human-like reasoning, pattern recognition, and inference capabilities. The cognitive system processes data through simulated cognitive functions rather than conventional algorithmic operations, enabling efficient handling of big data volumes while reducing processing time through intelligent pattern matching and insight generation
2Quantity of substance
If dark data is collected and stored, then data volume increases, but the data remains underutilized and inaccessible
Solution Approach 1:
The patent introduces a cognitive intermediary system that acts as a bridge between stored dark data and analytical applications. This cognitive layer processes and interprets underutilized data through human-like reasoning, pattern recognition, and inference, transforming inaccessible dark data into actionable insights that can be utilized by business applications and decision-making processes
3Loss of information
If cognitive learning operations are implemented, then insight generation capability improves, but system complexity increases
Solution Approach 1:
The patent segments the cognitive learning system into distinct functional modules including data ingestion components, cognitive processing units, pattern recognition engines, and insight generation components. Each module performs a specific cognitive function, allowing the complex system to be managed through modular architecture while maintaining high insight extraction capabilities through coordinated operation of specialized sub-systems
Data Source
AI summary
A cognitive information processing system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources to perform a cognitive learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning operation applying the cognitive learning technique to generate a cognitive learning result; and, updating a destination based upon the learning result.