Cognitive Graph for Extracting Insights from Dark Data
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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, making it difficult to extract actionable insights.
Innovation Solution
A cognitive information processing system that incorporates multiple data sources, utilizing cognitive inference and learning techniques to process data, apply cognitive learning methods, and generate insights, which are then used to update a destination system, enabling the discovery and prioritization of relevant information from diverse data streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data processing approaches are used to handle big data, then data storage and basic processing can be achieved, but the ability to efficiently analyze and extract insights from complex and dark data is insufficient
Solution Approach 1:
The patent introduces a cognitive graph as an intermediary structure that mediates between raw data sources and analysis operations. The cognitive graph organizes entities, relationships, and attributes in a structured knowledge representation that enables efficient querying and insight extraction from dark data without requiring exhaustive processing of all raw data
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with cognitive inference and learning operations. Instead of using conventional database queries and data mining algorithms, the system employs cognitive techniques including natural language processing, semantic analysis, and inference engines to extract insights from unstructured and dark data
2Loss of information
If cognitive inference and learning techniques are applied to process data from multiple sources, then actionable insights can be generated, but the complexity of the system increases
Solution Approach 1:
The patent segments the cognitive processing system into distinct functional modules including data ingestion components, cognitive graph construction modules, inference engines, and learning operations. This segmentation allows each component to be optimized independently and facilitates easier maintenance and scaling of the complex system
Solution Approach 2:
The cognitive graph serves as a universal data structure that can represent multiple types of data from diverse sources including structured databases, unstructured text, sensor data, and dark data. This multi-functional representation approach reduces system complexity by providing a single unified framework for handling heterogeneous data types
Data Source
AI summary
A cognitive information processing system environment comprising a plurality of data sources; a cognitive inference and learning system coupled to receive data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to perform a cognitive learning operation, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; and, a destination, the destination being updated based upon the learning result.