Cognitive Session Graphs with Blockchain for Data Enrichment
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and analyzing large volumes of complex data, particularly 'dark data,' which includes neglected or underutilized information, to provide actionable insights.
Innovation Solution
A cognitive session graph is generated by receiving data from various sources, including a blockchain data source, processing it to enrich the data, and associating a cognitive blockchain with the graph to facilitate cognitive inference and learning operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data processing can be performed with simple tools, but the processing efficiency is insufficient and cannot handle large volumes of complex data within tolerable time intervals
Solution Approach 1:
The patent segments the complex data processing task into multiple cognitive operators that work in parallel on different aspects of data analysis. These operators include entity recognition, relationship extraction, pattern detection, and inference generation, each handling specific portions of the cognitive processing pipeline simultaneously to improve overall efficiency.
Solution Approach 2:
The patent combines multiple data sources and processing functions into a unified cognitive graph framework. This framework integrates structured and unstructured data, merges various analysis operations into a single coherent system, and consolidates multiple processing streams into unified cognitive insights, thereby managing complexity while enhancing productivity.
2Loss of information
If dark data is collected and stored for potential insights, then more actionable information becomes available, but the data becomes difficult to access, process, and analyze at the right time and place
Solution Approach 1:
The patent introduces a cognitive graph as an intermediary layer between raw dark data and analysis operations. This cognitive graph structure serves as a mediator that pre-organizes unstructured and semi-structured data into meaningful entities and relationships, making dark data accessible and processable without requiring complex ad-hoc processing for each query.
Solution Approach 2:
The patent performs preliminary processing of dark data by automatically constructing cognitive graphs that pre-extract entities, relationships, and patterns from raw data sources. This preliminary action transforms difficult-to-access dark data into structured cognitive representations that can be quickly queried and analyzed when needed, eliminating the need for complex processing at the time of access.
3Speed
If cognitive operators are executed in parallel to improve processing speed, then analysis efficiency increases, but coordinating and managing multiple parallel operations becomes more complex
Solution Approach 1:
The patent implements feedback mechanisms where cognitive operators continuously exchange intermediate results and coordinate their operations. The system monitors the state of parallel operations, adjusts resource allocation dynamically, and uses feedback from completed operators to guide ongoing parallel processing, thereby managing coordination complexity while maintaining high processing speed.
Data Source
AI summary
A method, system and computer-usable medium for providing cognitive insights comprising receiving data from a plurality of data sources, the plurality of data sources comprising a blockchain data source, the blockchain data source providing blockchain data; processing the data from the plurality of data sources, the processing the data from the plurality of data sources performing data enriching to provide enriched data; generating the cognitive session graph, the cognitive session graph being associated with a session, the cognitive session graph comprising at least some enriched data; and, associating a cognitive blockchain with the cognitive session graph.