Time-Based Data Stream Correlation Using Ring Buffer Hashing
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Solution Overview
Problem
Existing data stream correlation techniques are inefficient and computationally expensive, leading to delays and limitations in real-time processing, especially with high data rates, and are not scalable for large-scale implementations.
Innovation Solution
Utilizing a ring buffer structure with associated hash tables to correlate data streams based on timestamps and metadata, enabling efficient real-time correlation with low resource requirements and O(1) efficiency, allowing for horizontal and vertical scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing data stream correlation techniques are used, then data correlation can be performed, but processing efficiency is low and resource consumption is high
Solution Approach 1:
The patent segments the data stream processing into distinct phases: buffering incoming records with timestamps, hashing records to generate lookup keys, and correlating records by comparing hashes within time windows. This segmentation allows each phase to be optimized independently, improving overall processing efficiency while reducing computational overhead through specialized data structures for each stage.
Solution Approach 2:
The patent applies preliminary action by pre-computing hash values for incoming data records and storing them in buffered structures before correlation is needed. This allows the correlation phase to operate efficiently by simply comparing pre-computed hashes rather than performing complex comparisons on raw data, significantly reducing real-time computational requirements.
2Speed
If existing correlation methods are implemented, then data streams can be correlated, but real-time processing capability is insufficient for high data rates
Solution Approach 1:
The patent replaces traditional mechanical comparison-based correlation methods with a hash-based lookup system. By computing hash values and using them as indices into buffered data structures, the system achieves O(1) average-case lookup time instead of O(n) linear scanning, enabling real-time processing of high-rate data streams with minimal delay.
3Adaptability or versatility
If traditional correlation techniques are used, then data can be processed, but scalability to large-scale implementations is limited
Solution Approach 1:
The patent implements a universal correlation engine that handles multiple data stream types and correlation requirements through a single unified architecture. The hash-based buffering and lookup mechanism works consistently across different data formats and correlation scenarios, enabling scalable deployment from small to large-scale implementations without requiring fundamentally different system designs.
Data Source
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AI summary
Techniques for efficient data correlation are provided. A first data partition is received, and a first hash table of a plurality of hash tables is selected based on a timestamp associated with the first data partition. Additionally, a first hash bucket in the first hash table is identified based on the first data partition. It is determined that the first hash bucket includes a second data partition. Upon determining that the first hash bucket satisfies a predefined criterion, the second data partition is removed from the first hash bucket, and the first and second data partitions are associated.