Cognitive Graph Vectors for Dark Data Insight Extraction
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Solution Overview
Problem
Current big data processing technologies face challenges in efficiently capturing, curating, storing, searching, sharing, and analyzing large quantities of complex data, including 'dark data' that is neglected or underutilized, which can provide valuable insights when combined with readily-available data.
Innovation Solution
A method using travel-related cognitive graph vectors to store and process data from multiple sources, associating it with cognitive graph vectors to refine insights, involving cognitive computing techniques like semantic analysis, collaborative filtering, and natural language processing to generate actionable information.
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 actionable insights from complex data including dark data is insufficient
Solution Approach 1:
The patent transforms data from traditional tabular formats into graph-based representations with multiple dimensions including entities, attributes, and relationships. This dimensional transformation enables the system to capture complex connections and contextual information that traditional processing cannot extract, thereby improving insight extraction while maintaining analysis efficiency
Solution Approach 2:
The system creates a composite data structure combining multiple data types (structured, unstructured, semi-structured) and multiple data sources into a unified cognitive graph. This composite approach integrates dark data with traditional data, allowing the system to extract actionable insights from heterogeneous data while processing them through a single efficient pipeline
2Reliability
If cognitive graph vectors with multiple data sources are integrated to provide comprehensive insights, then the quality and contextuality of information improve, but the complexity of data processing and computation increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modules: data ingestion from multiple sources, graph construction, vector generation, and insight extraction. Each module handles specific aspects of processing, reducing overall system complexity while maintaining comprehensive data integration and high insight quality
Solution Approach 2:
The cognitive graph serves as an intermediary structure between raw multi-source data and final insights. It mediates the complexity by providing a standardized representation layer that simplifies subsequent processing while preserving the rich contextual information from diverse sources, thereby maintaining reliability without proportionally increasing processing complexity
Data Source
AI summary
A method, system and computer-usable medium are disclosed for using travel-related cognitive graph vectors.