Cognitive Graph Engine for Dark Data Insight Extraction
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and extracting insights from large volumes of big data, particularly 'dark data,' which is often neglected or underutilized, making it difficult to provide timely and relevant information for decision-making.
Innovation Solution
A cognitive information processing system that encapsulates operations to generate cognitive insights by applying them to a target cognitive graph, utilizing processes like semantic analysis, goal optimization, collaborative filtering, common sense reasoning, natural language processing, and entity resolution to process and prioritize data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to process big data, then data processing can be performed with simple tools, but the 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 cognitive graph engine for data structuring, multiple specialized analysis engines (semantic analysis, entity resolution, natural language processing, collaborative filtering, goal optimization, common sense reasoning), and an insight generation engine. This modular segmentation enables parallel processing of different data aspects, dramatically improving processing efficiency while maintaining manageable system complexity through clear module boundaries and specialized functions.
2Loss of information
If dark data is collected and stored for potential insights, then more valuable information becomes available, but the data becomes difficult to access and utilize at the right time and place
Solution Approach 1:
The patent implements preliminary action by pre-processing and structuring dark data into a cognitive graph format that captures relationships, semantics, and contextual information before the data is needed for analysis. The system pre-computes relationships between entities, pre-structures unstructured data with metadata and contextual tags, and maintains the cognitive graph in an optimized state for rapid querying. This preliminary structuring transforms inaccessible dark data into readily queryable knowledge structures, enabling fast retrieval and analysis when insights are needed without the time penalty of ad-hoc data processing.
3Loss of information
If comprehensive data analysis is performed to extract insights, then actionable insights can be discovered, but the complexity of analysis operations increases significantly
Solution Approach 1:
The patent implements universality through the cognitive graph data structure that serves multiple analysis functions simultaneously. The same cognitive graph structure supports semantic analysis, entity resolution, natural language processing, collaborative filtering, goal optimization, and common sense reasoning operations. This universal data representation eliminates the need for separate complex analysis systems for each type of insight extraction, reducing overall system complexity while maintaining comprehensive analytical capabilities. The cognitive graph acts as a multi-functional foundation that simplifies the path from raw data to diverse actionable insights.
Data Source
AI summary
A method for providing cognitive insight via a cognitive information processing system comprising: encapsulating an operation for providing a desired cognitive insight; and, applying the operation to a target cognitive graph to generate a cognitive insight.