Cognitive Graph Querying for Big 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 information processing system that receives data from multiple sources, processes queries, and bridges them into a cognitive graph, utilizing techniques like semantic analysis, goal optimization, collaborative filtering, and natural language processing to generate cognitive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data processing approaches are used to handle big data, then data storage and basic retrieval are feasible, but processing efficiency and analysis speed deteriorate significantly
Solution Approach 1:
The patent segments data into structured and unstructured components, processing each type through specialized pathways. Structured data is handled through traditional database operations while unstructured data undergoes semantic analysis and entity extraction, allowing parallel processing that improves overall efficiency while handling large volumes
Solution Approach 2:
The patent introduces a cognitive processing layer as an intermediary between raw data and analysis results. This layer performs semantic analysis, entity recognition, and relationship extraction, transforming unstructured data into structured knowledge representations that can be efficiently queried and analyzed
2Loss of information
If comprehensive data collection is performed to include dark data, then data completeness and insight potential improve, but data accessibility and processing speed worsen
Solution Approach 1:
The patent performs preliminary semantic analysis and entity extraction on unstructured data during the ingestion phase, creating pre-processed knowledge representations stored in graph database structures. This preliminary processing makes the data immediately accessible for querying without requiring intensive processing at query time
Solution Approach 2:
The patent replaces traditional text search and pattern matching mechanisms with semantic graph representations and cognitive processing. This substitution enables meaningful queries against unstructured data through natural language interfaces, dramatically improving accessibility while maintaining completeness
3Measurement precision
If complex semantic analysis and cognitive processing are applied, then insight quality and decision-making value improve, but system complexity and computational resources worsen
Solution Approach 1:
The patent segments cognitive processing into distinct functional modules: semantic analysis, entity recognition, relationship extraction, and graph construction. Each module handles a specific aspect of processing, making the overall complex system manageable through clear separation of concerns and specialized optimization of each component
Solution Approach 2:
The patent extracts key entities and relationships from unstructured data, separating the essential semantic information from the raw text. This extraction creates a simplified graph representation that retains the critical insights while removing unnecessary computational complexity from subsequent processing operations
Data Source
AI summary
A method for providing cognitive insights via a cognitive information processing system comprising: receiving data from a plurality of data sources; receiving and processing queries; and, bridging the queries into a cognitive graph.