Graph-Based Data Correlation Platform for Distributed Systems
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Solution Overview
Problem
Conventional data management and analysis applications are not well-suited for distributed data architectures, leading to difficulties in interoperability and efficient use of large datasets, with manual intervention often required to update and maintain data formats and definitions across disparate data sources.
Innovation Solution
A computing platform that correlates subsets of parallelized data from disparate sources using graph-based data arrangements, employing a collaborative dataset consolidation system to identify entity data by aggregating and clustering graph data portions, facilitating automatic data remediation and deduplication through dataset ingestion controllers and attribute correlators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data management applications are used for distributed data architectures, then data processing can be performed, but interoperability and efficient use of large datasets are difficult to achieve
Solution Approach 1:
The patent introduces a computing platform as an intermediary system that sits between disparate data sources and end users. This platform provides standardized interfaces and automated data operations, mediating the interaction between different data architectures and enabling interoperability without requiring complex point-to-point integrations between each data source.
Solution Approach 2:
The computing platform is designed with multi-functional capabilities to handle diverse data operations including data ingestion, correlation, aggregation, and remediation across different data sources. This universal approach allows a single system to perform multiple functions that would otherwise require separate specialized applications, improving adaptability while managing complexity.
2Reliability
If manual intervention is used to update and maintain data formats and definitions across disparate data sources, then data accuracy can be maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The system implements automated data operations that enable self-service functionality. The computing platform automatically performs data correlation, aggregation, and remediation tasks without requiring manual intervention. The system self-manages data format standardization and definition maintenance across disparate sources, maintaining reliability while dramatically improving productivity.
Solution Approach 2:
The system performs preliminary data correlation and aggregation actions automatically during data ingestion, before the data needs to be used. By pre-processing and standardizing data formats and definitions in advance, the system eliminates the need for subsequent manual updates and maintenance, ensuring data accuracy while enhancing efficiency.
3Productivity
If data from disparate sources is processed without parallelization, then processing simplicity is maintained, but productivity and processing speed deteriorate
Solution Approach 1:
The computing platform segments data processing into parallel operations that can be executed simultaneously across multiple data sources. By dividing the overall data processing task into independent correlation and aggregation operations that can run in parallel, the system achieves high throughput while managing complexity through modular architecture.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to receive or analyze datasets in parallel by implementing, for example, parallel computing processor systems to correlate subsets of parallelized data from disparately-formatted data sources to identify entity data and to aggregate graph data portions. In some examples, a method may include classifying data parallelized data to identify a class of observation data, constructing one or more content graphs in a graph data format, correlating parallelized data to other subsets of parallelized data associated with a class of observation data; and aggregating observation data to represent an individual entity.