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 processes streams of data from various sources, performs data enrichment, and generates cognitive graphs to provide insights, utilizing techniques like semantic analysis, goal optimization, collaborative filtering, and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data processing can be performed with simple tools, but processing efficiency is insufficient and cannot handle large volumes of data within tolerable time intervals
Solution Approach 1:
The patent segments the cognitive processing system into distinct functional modules including a cognition engine with multiple specialized processors (semantic analysis processor, goal optimization processor, collaborative filtering processor, natural language processing processor). Each module handles specific aspects of data analysis independently, enabling parallel processing and improving overall efficiency while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces a cognitive graph dimension that transforms traditional flat data structures into multi-dimensional semantic networks. By organizing data into entities, attributes, and relationships within a graph framework, the system enables richer contextual analysis and pattern recognition without proportionally increasing processing complexity.
2Loss of information
If dark data is collected and stored for potential insights, then data availability increases, but data accessibility and utilization remain difficult due to lack of proper processing
Solution Approach 1:
The patent introduces a cognitive graph as an intermediary layer between raw dark data and analytical applications. This graph structure serves as a mediator that pre-processes and organizes unstructured data into meaningful semantic relationships, making dark data accessible without requiring complex processing at the point of use. The cognitive graph acts as a universal interface that simplifies data retrieval and analysis.
3Measurement precision
If comprehensive data analysis is performed to extract actionable insights, then insight quality improves, but processing time increases beyond tolerable intervals
Solution Approach 1:
The patent performs preliminary organization of data into cognitive graphs and pre-computation of semantic relationships before actual analysis is needed. By pre-structuring data into entities, attributes, and relationships in advance, the system eliminates the need for complex real-time processing when insights are required, thereby maintaining high accuracy while reducing processing time to acceptable intervals.
Solution Approach 2:
The patent implements continuous data ingestion and cognitive graph updating mechanisms that maintain data freshness without requiring complete re-processing. The system continuously enriches the cognitive graph with new data while preserving existing semantic relationships, enabling ongoing insight generation without interrupting service or incurring significant processing delays.
Data Source
AI summary
A computer-implementable method for providing cognitive insights comprising: receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching and generating a sub-graph for incorporation into a cognitive graph; and, processing the cognitive graph, the processing the cognitive graph providing cognitive insights.