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, hindering organizations and individuals from extracting actionable insights.
Innovation Solution
A cognitive information processing system comprising a processor, data bus, and non-transitory storage medium with computer program code that performs cognitive machine learning operations through a cognitive inference and learning system, applying machine learning algorithms to generate insights and update destinations based on learning results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used, then processing speed is maintained at acceptable levels, but the ability to handle big data and extract actionable insights deteriorates
Solution Approach 1:
The patent segments the data processing system into multiple specialized modules: data collection module, data storage module, data processing module, and data visualization module. Each module handles specific aspects of data processing, enabling the system to manage big data effectively while maintaining organizational structure and processing efficiency.
Solution Approach 2:
The patent implements a universal data processing system that can handle multiple types of data (structured, unstructured, semi-structured) from various sources through a single integrated framework. The system performs multiple functions including collection, storage, processing, and visualization, eliminating the need for separate specialized systems for each data type.
2Loss of information
If dark data is collected and processed, then actionable insights are improved, but the time required for processing and analysis increases
Solution Approach 1:
The patent implements preliminary data processing actions including data cleaning, transformation, and preprocessing before actual analysis. The system pre-organizes dark data into structured formats and prepares it for rapid analysis, reducing the time required during the actual insight extraction phase while maintaining high-quality results.
Solution Approach 2:
The patent establishes continuous data processing pipelines that operate continuously rather than in batches. The system maintains continuous collection, storage, and processing operations, enabling real-time insight extraction from dark data without interrupting workflow or requiring lengthy processing cycles.
3Adaptability or versatility
If machine learning algorithms are applied to cognitive learning operations, then decision-making capabilities are improved, but the computational resources and complexity required increase
Solution Approach 1:
The patent applies machine learning algorithms selectively to specific local problems rather than uniformly across the entire system. The cognitive learning operations are implemented only where needed for decision-making, using appropriate algorithm complexity matched to the specific task requirements, thereby reducing overall computational resource consumption while maintaining enhanced decision-making capabilities.
Data Source
AI summary
A cognitive information processing system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: 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.