Graph Query Engine for Cognitive Data Analysis
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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 incorporating a graph query engine that receives and processes data from multiple sources, bridging queries into a cognitive graph to facilitate semantic analysis, goal optimization, collaborative filtering, common sense reasoning, natural language processing, and entity resolution, enabling the generation of cognitive 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 can be achieved, but processing efficiency and analysis speed deteriorate due to the large volume and complexity of data
Solution Approach 1:
The patent segments the large-scale data processing task into distributed computing nodes and parallel processing streams. The cognitive information processing system divides incoming data streams into manageable chunks that can be processed simultaneously across multiple computational units, thereby maintaining high processing efficiency while handling large volumes of data.
Solution Approach 2:
The patent introduces a temporal dimension to data processing by implementing real-time streaming analysis alongside batch processing. This multi-dimensional approach allows the system to process data both immediately as it arrives and in aggregated batches, resolving the contradiction between handling large data volumes and maintaining processing speed.
2Loss of information
If comprehensive data collection is performed to capture all potential insights, then data completeness improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data filtering and preprocessing steps that identify and prioritize relevant data elements before full processing occurs. By pre-processing data to extract key features and discard irrelevant information early in the pipeline, the system maintains data completeness for important elements while reducing overall processing time.
Solution Approach 2:
The patent applies partial processing to data streams by focusing computational resources on the most critical and time-sensitive data elements while using less intensive processing for other data. This selective approach ensures that essential insights are extracted within acceptable timeframes without requiring complete processing of all data elements.
3Measurement precision
If complex analytical operations are performed to extract deep insights from data, then insight quality improves, but processing speed and system complexity increase
Solution Approach 1:
The patent segments complex analytical operations into a hierarchy of processing stages, from simple filtering and aggregation to more sophisticated pattern recognition and cognitive analysis. This layered approach allows the system to deliver quick results from simpler operations while gradually applying more complex analysis only where needed, maintaining both speed and insight quality.
Solution Approach 2:
The patent introduces intermediate processing layers that transform raw data into progressively more refined representations before final analysis. These intermediary stages prepare data for complex analytical operations by organizing and structuring information in advance, thereby reducing the computational burden of deep insights while maintaining processing speed.
4Loss of time
If real-time processing is implemented to provide immediate insights, then decision-making speed improves, but processing accuracy and analysis depth may deteriorate
Solution Approach 1:
The patent implements dynamic processing modes that can adjust the depth and type of analysis based on real-time requirements. The system can switch between expedited processing for time-critical decisions and more thorough analysis for less urgent matters, thereby maintaining both rapid response capability and analytical depth as needed.
Solution Approach 2:
The patent implements continuous data processing where real-time analysis operates alongside ongoing batch processing and deeper analytical operations. This continuous action ensures that immediate insights are provided without interrupting more comprehensive analysis, allowing the system to deliver both speed and depth of analysis simultaneously through parallel operational streams.
Data Source
AI summary
An apparatus for use within a cognitive information processing system environment comprising: a graph query engine, the graph query engine coupled to receive data from a plurality of data sources, the graph query engine receiving and processing queries and to bridge the queries into a cognitive graph.