Cognitive Inference System for Dark Data Insight Extraction
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and analyzing large volumes of big data, particularly 'dark data,' which is often neglected or underutilized, making it difficult to extract actionable insights in a timely manner.
Innovation Solution
A cognitive inference and learning system (CILS) that receives data from multiple sources, processes it to provide cognitively processed insights, and iteratively improves these insights over time, using techniques like semantic analysis, goal optimization, collaborative filtering, and natural language processing to generate actionable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used, then processing simplicity is maintained, but processing efficiency and timeliness deteriorate when handling big data
Solution Approach 1:
The system segments the data processing task into multiple independent processing nodes, each handling specific portions of the big data. This distributed segmentation enables parallel processing that improves efficiency while keeping individual node complexity manageable.
Solution Approach 2:
The patent introduces a cognitive processing layer as an intermediary between data collection and decision-making. This intermediary layer processes and contextualizes raw data into actionable insights, improving processing efficiency without requiring complete system redesign.
2Loss of information
If dark data is collected and processed, then insight completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing dark data in advance, organizing it in a way that enables rapid retrieval and processing. This preliminary structuring reduces processing time when insights are needed while maintaining completeness of the dark data utilization.
Solution Approach 2:
The patent implements feedback mechanisms where processing results are fed back to refine future processing operations. This allows the system to optimize its approach to dark data processing over time, reducing processing time while maintaining insight completeness through iterative improvement.
3Measurement precision
If iterative learning operations are performed, then insight accuracy is improved, but processing iterations and time increase
Solution Approach 1:
The system applies partial learning operations that focus computational resources on the most impactful iterations. Rather than exhaustive processing, it performs sufficient learning iterations to achieve acceptable accuracy levels, balancing processing duration with insight accuracy requirements.
Solution Approach 2:
The patent dynamically changes processing parameters based on data characteristics and learning progress. This adaptive parameter adjustment allows the system to optimize the number of iterations needed, reducing processing duration while maintaining insight accuracy by adjusting computational intensity to match actual requirements.
Data Source
AI summary
A cognitive information processing system environment which includes a plurality of data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; and, a destination, the destination receiving the cognitively processed insights.