Cognitive 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 big data, particularly 'dark data,' which is often neglected or underutilized, making it difficult to extract actionable insights.
Innovation Solution
A cognitive learning method and system that processes data from multiple sources using a cognitive inference and learning system, applying various techniques such as semantic analysis, goal optimization, collaborative filtering, and natural language processing to generate cognitive insights and update destinations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then processing tools and methods are simple and well-established, but the ability to efficiently process large volumes of data within tolerable time intervals deteriorates
Solution Approach 1:
The patent segments the data processing system into multiple specialized components: data collection modules, data storage modules, data processing modules, and data visualization modules. Each component handles specific aspects of big data processing, enabling efficient handling of large volumes of data while maintaining manageable system complexity through modular architecture.
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 platform. The system performs multiple functions including collection, storage, processing, and visualization, improving overall productivity without requiring separate specialized systems for each function.
2Loss of information
If dark data is collected and stored, then data volume increases providing more potential insights, but the difficulty of accessing and utilizing this data at the right time and place increases
Solution Approach 1:
The patent implements preliminary data processing actions including data cleaning, transformation, and indexing during the data collection and storage phases. This preliminary preparation ensures that dark data is pre-processed and organized in a way that enables rapid access and utilization when needed, reducing the time and effort required for later analysis.
Solution Approach 2:
The patent introduces data intermediaries such as metadata layers, data catalogs, and indexing mechanisms that bridge the gap between stored dark data and user queries. These intermediaries enable efficient searching, filtering, and accessing of data without requiring direct manipulation of the raw data, significantly improving access speed and effectiveness.
3Measurement precision
If cognitive learning techniques are applied to process data, then actionable insights are generated from dark data, but the complexity of the processing system increases
Solution Approach 1:
The patent implements nested cognitive processing layers where simple data processing operations are nested within more complex cognitive learning techniques. The system starts with basic data collection and storage, then progressively adds cognitive processing layers for pattern recognition, inference, and insight generation. This nested architecture enables high-accuracy insights while managing complexity by building capabilities incrementally.
Solution Approach 2:
The patent implements self-service cognitive processing where the system automatically learns from data patterns and adjusts its processing techniques without requiring manual reconfiguration. The cognitive learning components automatically optimize their parameters and methodologies based on the data they process, generating accurate insights while reducing the operational complexity of managing the system.
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 learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation comprising a plurality of cognitive learning operation lifecycle phases, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; and, updating a destination based upon the cognitive learning result.