Cognitive Learning System 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 complex data, particularly 'dark data,' which is often neglected or underutilized, making it difficult to derive actionable insights in a timely manner.
Innovation Solution
A method and system utilizing a cognitive learning and inference system that performs ranked insight machine learning operations to generate cognitive profiles and insights from user interactions, incorporating techniques like semantic analysis, goal optimization, collaborative filtering, and natural language processing to prioritize and summarize relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used on big data, then data processing can be performed with simple tools, but the processing time becomes unacceptably long and insights cannot be extracted efficiently
Solution Approach 1:
The patent segments the large-scale data processing task into distributed computing operations across multiple processing nodes. The cognitive learning and inference system divides big data into smaller datasets that can be processed in parallel, enabling efficient extraction of insights from dark data without requiring centralized processing of the entire dataset at once.
Solution Approach 2:
The patent implements dynamic data processing by continuously adapting the processing pipeline based on data characteristics and insights discovered during analysis. The system dynamically adjusts processing parameters, data sampling rates, and analysis depth to optimize the balance between processing speed and insight quality, allowing efficient extraction of actionable insights from dark data.
2Loss of information
If dark data is collected and stored for future analysis, then more potential insights become available, but the data becomes difficult to access and process when needed
Solution Approach 1:
The patent introduces a data lake as an intermediary layer between dark data storage and processing systems. This data lake provides standardized access interfaces and preprocessing capabilities, making previously inaccessible dark data easily queryable and processable without requiring complex direct access to underlying storage systems.
Solution Approach 2:
The patent performs preliminary data preparation and indexing operations on dark data during the data ingestion phase, before analysis is needed. This includes data cleaning, normalization, and creation of searchable indexes, which significantly reduces the complexity of accessing and processing dark data when insights are needed later.
3Measurement precision
If cognitive learning and inference operations are performed on large datasets, then actionable insights can be generated, but the computational resources and processing time required increase significantly
Solution Approach 1:
The patent applies partial action by performing cognitive learning and inference operations on selectively sampled subsets of dark data rather than processing the entire dataset. The system identifies and processes only the most relevant data portions needed to generate actionable insights, reducing computational resource consumption while maintaining insight quality.
4Reliability
If multiple machine learning operations are performed to generate cognitive profiles and insights, then more comprehensive analysis is achieved, but the processing time and system complexity increase
Solution Approach 1:
The patent performs preliminary data preparation, feature extraction, and initial pattern recognition operations before executing the full multi-stage cognitive learning and inference pipeline. This preliminary processing reduces the complexity and time required for subsequent comprehensive analysis operations while maintaining the reliability and comprehensiveness of the final cognitive insights.
Data Source
AI summary
A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a ranked insight machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the ranked insight machine learning operations; and, generating a cognitive insight based upon the cognitive profile generated using the plurality of machine learning operations.