Cognitive Data Management Module for Big Data Insight Extraction
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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 in a timely manner.
Innovation Solution
A cognitive information processing system architecture that integrates multiple data sources, including public and private data, with a cognitive data management module to access and provide information to an inference and learning system, utilizing processes like semantic analysis, goal optimization, collaborative filtering, and natural language processing to generate cognitive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data storage and basic retrieval are maintained, but the system becomes difficult to process large volumes of data efficiently and cannot extract actionable insights in a timely manner
Solution Approach 1:
The patent segments the monolithic data processing system into multiple specialized modules including cognitive data management module, inference module, learning module, and knowledge representation module. Each module handles specific aspects of data processing, enabling parallel operation and improving overall processing efficiency while reducing the time required to extract insights from big data
Solution Approach 2:
The patent introduces a cognitive data management module as an intermediary between traditional data storage systems and analysis systems. This module performs semantic analysis, data curation, and intelligent retrieval, acting as a mediator that transforms raw data into structured, meaningful information that can be processed efficiently by downstream systems, thereby improving productivity without sacrificing insight extraction speed
2Loss of information
If multiple data sources including dark data are integrated, then more comprehensive insights are available, but the complexity of data management and processing increases significantly
Solution Approach 1:
The cognitive data management module is designed as a universal platform that can handle multiple types of data sources (structured, unstructured, dark data) through a single interface. It performs multiple functions including semantic analysis, data validation, enrichment, and integration, eliminating the need for separate processing pipelines for different data types and reducing overall system complexity while maintaining comprehensive information coverage
Solution Approach 2:
The patent changes the parameters of data representation by introducing semantic annotations, metadata structures, and standardized formats. This transformation converts heterogeneous data from multiple sources into a unified parameter space that is easier to manage and process, reducing complexity while preserving the completeness of insights from diverse data sources including dark data
3Loss of information
If cognitive processing modules are added to analyze and interpret data, then actionable insights and knowledge creation are enhanced, but the computational resources and processing time requirements increase
Solution Approach 1:
The cognitive data management module performs preliminary actions by pre-processing data with semantic analysis, creating structured representations, and generating metadata before data reaches the inference and learning modules. This preliminary structuring reduces the computational burden on downstream cognitive modules, allowing them to focus on higher-level reasoning while consuming fewer computational resources overall
Solution Approach 2:
The patent applies local quality by performing cognitive processing selectively on relevant data subsets rather than processing all data uniformly. The system identifies and focuses computational resources on data portions that are most relevant to current queries or analysis goals, maintaining high-quality cognitive insights while reducing overall computational resource consumption through targeted processing
Data Source
AI summary
A data architecture for use within a cognitive information processing system environment comprising: a plurality of data sources, the plurality of data sources comprising a public data source and a private data source; and, a cognitive data management module, the cognitive data management module accessing information from the plurality of data sources and providing the information to an inference and learning system.