Cognitive Machine Learning System for Dark Data Processing
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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 machine learning system processes data from multiple sources using a cognitive inference and learning framework, applying machine learning algorithms to generate insights and update destinations based on learning results, incorporating techniques like semantic analysis, goal optimization, 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 the processing efficiency is insufficient and cannot handle large volumes of complex data within tolerable time intervals
Solution Approach 1:
The patent segments the data processing system into multiple specialized components including cognitive computing systems, machine learning models, and distributed processing nodes. Each component handles specific aspects of data processing independently, enabling parallel processing of large datasets while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent introduces cognitive computing systems and machine learning models as intermediary layers between raw data and traditional processing tools. These intermediaries transform unstructured dark data into structured insights that can be efficiently processed by conventional databases and analytics tools, bridging the gap between simple tools and complex data requirements
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 applies preliminary processing actions to dark data during ingestion, including automated tagging, metadata generation, and preliminary machine learning analysis. This pre-processing structures the data in advance, making it immediately accessible and searchable when needed without requiring complex real-time processing
Solution Approach 2:
The patent replaces manual data access and retrieval mechanisms with automated cognitive computing systems that use natural language processing and semantic search. These systems substitute traditional mechanical database querying with intelligent agents that can interpret queries and retrieve relevant dark data insights automatically based on contextual understanding
Data Source
AI summary
A cognitive learning method comprising receiving data from a plurality of data sources; processing the data from the plurality of data sources to perform a cognitive machine learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive machine learning operation applying the cognitive learning technique via a machine learning algorithm to generate a cognitive learning result; and, updating a destination based upon the learning result.