Cognitive Data Architecture for Healthcare Big 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 includes neglected or underutilized information, making it difficult to extract actionable insights in a timely manner.
Innovation Solution
A cognitive information processing system architecture that integrates public and private healthcare data sources with a cognitive data management module, enabling cognitive inference and learning operations through semantic analysis, goal optimization, collaborative filtering, common sense reasoning, natural language processing, and entity resolution to generate actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data processing approaches are used to handle big data, then data storage and management become manageable, but processing efficiency and analysis speed deteriorate significantly
Solution Approach 1:
The patent segments the monolithic data processing system into multiple specialized components including data ingestion services, processing services, storage services, and analysis services. Each component handles specific tasks independently, allowing parallel processing of large data volumes without overwhelming a single processing unit. This segmentation enables the system to manage big data while maintaining processing efficiency through distributed computation.
Solution Approach 2:
The patent introduces cloud-based distributed computing resources as an additional dimension for data processing. By moving from traditional single-server processing to multi-dimensional distributed architecture across multiple servers and data centers, the system can process large data volumes in parallel, resolving the contradiction between data quantity and processing efficiency.
2Loss of information
If comprehensive data collection is performed to capture all potential insights, then data completeness improves, but data management complexity and processing time increase
Solution Approach 1:
The patent applies local quality by implementing data filtering and prioritization at different stages of the data pipeline. Not all data is processed with the same level of detail - critical data receives intensive processing while less important data undergoes lighter processing. This approach maintains data completeness for essential information while reducing management complexity through selective processing strategies.
Solution Approach 2:
The patent implements partial action by collecting and processing only the necessary subset of data required for specific analytical goals rather than uniformly processing all available data. Data collection is tailored to the specific needs of each analysis task, reducing overall management complexity while maintaining completeness for relevant data elements.
3Measurement precision
If advanced cognitive analysis techniques are applied to extract insights, then insight quality improves, but computational resource requirements and processing time increase
Solution Approach 1:
The patent implements preliminary action through data preprocessing and feature extraction stages that prepare data before applying complex cognitive analysis techniques. By pre-processing data to extract relevant features and remove noise beforehand, the system reduces the computational burden of subsequent advanced analysis while maintaining insight quality. This staged approach allows sophisticated analysis to be performed more efficiently.
Solution Approach 2:
The patent employs self-service mechanisms through automated machine learning model selection and hyperparameter optimization. The system automatically selects appropriate analysis techniques and configures them based on the characteristics of the input data, reducing the need for extensive manual computational resources while maintaining high insight quality through adaptive, self-optimizing analysis pipelines.
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, the public data source comprising publicly available healthcare information, the private data source comprising privately managed, company specific healthcare information; 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.