Cognitive Development Environment for Custom Extension Creation
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and utilizing large volumes of complex 'big data' and 'dark data' due to difficulties in capture, curation, storage, search, sharing, and visualization, which limits their potential for providing actionable insights.
Innovation Solution
A cognitive inference and learning system that processes data from multiple sources using a cognitive platform with a development environment, enabling custom extensions and iterative learning to provide cognitively processed insights to users through a cognitive application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional data processing approaches are used to handle big data, then data processing can be performed with simple tools, but processing efficiency and ability to extract actionable insights deteriorates due to the complexity and volume of data
Solution Approach 1:
The patent introduces a cognitive information processing system as an intermediary layer between traditional data processing tools and big data. This system includes cognitive computing platforms, machine learning algorithms, and natural language processing capabilities that mediate the processing of complex data, enabling efficient extraction of actionable insights while maintaining ease of use through intuitive interfaces.
Solution Approach 2:
The patent transforms the processing approach by changing parameters from traditional deterministic algorithms to probabilistic machine learning models. This includes transitioning from exact matching to fuzzy logic, from structured data processing to unstructured data analysis, and from batch processing to real-time streaming processing, thereby improving productivity while managing complexity.
2Loss of information
If cognitive inference and learning systems are implemented to process big data and dark data, then actionable insights and intelligent recommendations can be extracted, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the cognitive information processing system into distinct modular components including data ingestion modules, preprocessing modules, machine learning model modules, natural language processing modules, and output generation modules. Each module handles specific tasks independently, reducing overall system complexity while enabling comprehensive processing of big data and dark data to extract actionable insights.
Solution Approach 2:
The patent creates a universal cognitive computing platform that can handle multiple types of data (structured, unstructured, semi-structured) and perform multiple functions (classification, prediction, sentiment analysis, entity recognition) through a single integrated system. This multi-functionality reduces the need for separate specialized systems, managing complexity while maximizing insight extraction capability.
3Loss of information
If data from multiple diverse sources is integrated and processed, then comprehensive insights and better decision-making capabilities are achieved, but data curation, storage, and management challenges increase
Solution Approach 1:
The patent introduces cognitive data curation intermediaries that automatically mediate between diverse data sources and the processing system. These intermediaries include automated data quality assessment tools, schema mapping services, and contextualization engines that handle the complexity of integrating structured, unstructured, and semi-structured data from multiple sources, enabling comprehensive insight extraction while managing data management complexity.
Data Source
AI summary
A method, system and computer readable storage medium for cognitive information processing. The cognitive information processing includes receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via a cognitive inference and learning system, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a development environment, the development environment being implemented to a create custom extension to the cognitive inference and learning system, the custom extension being created via a cognitive design user interface; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.