In-Memory Database Framework for Electronic Warfare Signal Processing
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Solution Overview
Problem
Electronic warfare systems face processing speed limitations when handling data streams from signal emitters, as traditional databases and in-memory databases like Redis are insufficient for the high data volumes generated, leading to latency and throughput issues.
Innovation Solution
An in-memory database framework that enables asynchronous signal processing by allowing multiple algorithms to execute in parallel on different threads, with a data storage system that supports individual or batch modifications and notifications, coordinating with an entity component system to manage data access and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional databases are used for signal data processing, then data storage and access are provided, but processing speed and throughput are insufficient for high data volumes
Solution Approach 1:
The patent replaces traditional disk-based database systems with an in-memory database system that stores all data in RAM. This substitution of the storage medium from mechanical disk to memory enables significantly faster data access and processing speeds while maintaining the ability to handle large data volumes from signal emitters.
Solution Approach 2:
The patent changes the storage parameter from disk-based to memory-based, fundamentally altering the performance characteristics of the system. By moving data storage from slow mechanical disks to fast memory, the system achieves high throughput and low latency processing of signal data streams.
2Productivity
If in-memory databases like Redis are used, then processing speed improves, but the system cannot handle the high data volumes generated by multiple signal emitters
Solution Approach 1:
The patent segments the data storage structure into multiple tables organized by signal emitter identifiers. This segmentation allows the system to manage large data volumes from multiple emitters efficiently by dividing the data into manageable, identifier-based segments, while maintaining fast in-memory access speeds.
Solution Approach 2:
The in-memory database system is designed to serve multiple functions: storing data from multiple signal emitters, enabling fast retrieval by identifier, supporting batch operations, and providing the foundation for parallel algorithm execution. This multi-functional design allows a single system to handle both high speed requirements and large data volumes.
3Productivity
If serial pipeline processing is used, then system simplicity is maintained, but processing throughput is limited by sequential execution
Solution Approach 1:
The patent introduces a dynamic parallel processing architecture where multiple algorithms can execute concurrently on different threads. The system dynamically assigns algorithms to available threads based on data triggers, enabling high throughput while managing complexity through a flexible, event-driven model rather than a rigid pipeline structure.
Solution Approach 2:
The patent transitions from single-threaded sequential processing to multi-threaded parallel processing, adding the dimension of concurrency. By utilizing multiple execution threads that can process data simultaneously, the system achieves higher throughput while the framework manages the added complexity through standardized algorithm interfaces and trigger-based coordination.
4Loss of time
If algorithms execute sequentially in order, then implementation simplicity is maintained, but processing time increases for high data volumes
Solution Approach 1:
The system establishes trigger mechanisms that prepare and signal when data is ready for processing. Algorithms are pre-configured with trigger subscriptions, so when data arrives, the appropriate algorithms are automatically activated without sequential waiting. This preliminary setup enables concurrent algorithm execution and reduces overall processing time for high data volumes.
Solution Approach 2:
The parallel execution framework ensures continuous processing by distributing algorithms across multiple threads that operate simultaneously. Rather than sequential execution with idle time between steps, multiple algorithms process data streams continuously in parallel, maximizing productivity and minimizing total processing time for large datasets.
Data Source
AI summary
An extensible open data framework asynchronously processes augments received in response to alterations in registered triggers. The framework includes an active data storage and an algorithm execution protocol. The active data storage includes short term data signals and long term data augments or emitter tracks. The execution protocol includes an execution thread pool having multiple threads that operate in parallel. The parallelization of the algorithm via the execution thread pool scales through multiple threads and cores to process data.