Incremental Data Correlation Pipelines for Real-Time Analytics
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Solution Overview
Problem
Conventional data correlation solutions for real-time or near-real-time data analytics are inefficient, ineffective, and lack operational speed, particularly in critical systems like system monitoring.
Innovation Solution
A data correlation system utilizing an input pipeline, correlation routine, pending pipeline, and completion verification routine to process data source updates incrementally, determining correlations and satisfying completion criteria to store fully-correlated data efficiently and scalably.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data correlation solutions are used for real-time data analytics, then data correlation can be performed, but computational efficiency and operational speed deteriorate
Solution Approach 1:
The patent segments the data correlation process into distinct phases: data ingestion phase, correlation phase, and output phase. It divides data sources into multiple groups and processes correlations within each group independently, then combines results. This segmentation enables parallel processing and reduces the complexity of simultaneous correlations across all data sources, thereby improving computational efficiency and operational speed.
Solution Approach 2:
The patent performs preliminary actions by pre-grouping data sources into multiple groups before receiving data streams. It pre-establishes correlation criteria and prepares data structures for efficient retrieval. When data arrives, the system only needs to process correlations within pre-defined groups rather than performing complex global correlations, significantly reducing processing time and improving operational speed.
2Reliability
If conventional data correlation solutions process all data sources simultaneously, then comprehensive correlation is achieved, but system complexity and resource consumption increase
Solution Approach 1:
The patent divides all data sources into multiple manageable groups, where each group contains data sources that are correlated together. This segmentation reduces the system complexity by limiting the scope of correlation operations to smaller subsets rather than processing all data sources simultaneously. The correlation engine only needs to manage correlations within each group, making the overall system more scalable and less complex.
Solution Approach 2:
The patent implements a dynamic data structure where data sources can be added to or removed from groups as needed. The correlation engine dynamically adjusts which data sources are correlated based on incoming data and changing requirements. This dynamic approach allows the system to maintain comprehensive correlation capability while adapting to varying system conditions, reducing complexity when fewer correlations are needed and ensuring completeness when required.
Data Source
AI summary
Techniques for data correlation across data sources are presented herein. In some examples, a method includes the steps of retrieving a first datagram from a first data structure, wherein the first datagram represents a first update to a first data source; determining, by a first routine, a first correlation between the first datagram and a second datagram that is stored in a second data structure; based on determining the first correlation, updating the second datagram in accordance with first datagram; determining, by a second routine, whether the second datagram satisfies a completion condition; and based on determining that the second datagram satisfies the completion condition, storing the second datagram in the first data structure.