Cognitive Engine Processing Dark Data Streams
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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 system comprising a processor, data bus, and non-transitory computer-readable storage medium with computer program code that receives and processes data streams from various sources, performs data enriching, and generates cognitive graphs to provide cognitive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data processing capability is maintained at current levels, but processing efficiency and speed deteriorate due to the volume and complexity of data
Solution Approach 1:
The patent segments the cognitive processing system into multiple specialized agents including data sourcing agents, enrichment agents, graph query agents, and insight agents. Each agent handles specific tasks in the data processing pipeline, allowing parallel processing and reducing overall processing time for big data analytics
Solution Approach 2:
The patent introduces a cognitive graph as an intermediary data structure that models relationships between entities. This cognitive graph serves as a mediator between raw data and analytical insights, enabling efficient querying and pattern recognition without processing entire datasets, thus improving processing efficiency while reducing time loss
2Loss of information
If data enrichment and cognitive graph generation are performed on large volumes of dark data, then actionable insights are extracted, but system complexity and processing requirements increase
Solution Approach 1:
The patent creates a multi-functional cognitive processing platform that handles diverse data types (structured, unstructured, semi-structured) from multiple sources using the same architectural framework. The system performs multiple functions including data sourcing, enrichment, graph generation, querying, and insight extraction within a unified system, managing complexity through standardized interfaces and processes
Solution Approach 2:
The patent performs preliminary data enrichment and cognitive graph generation in advance before analytical queries are executed. By pre-processing data and building cognitive models beforehand, the system reduces the complexity of real-time processing and enables faster insight extraction when queries are submitted
Data Source
AI summary
A cognitive information processing system which includes a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus. The computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching and generating a sub-graph for incorporation into a cognitive graph; and, processing the cognitive graph, the processing the cognitive graph providing cognitive insights.