Streaming Platform for Real-Time Embedding Vector Indexing
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Solution Overview
Problem
Conventional machine learning systems struggle to react timely to real-time data, such as high-velocity fraud attacks or product recommendations, due to batch-based generation of embedding vectors, which may not be current enough to address rapid changes or short-lived offers.
Innovation Solution
A streaming platform that processes and analyzes real-time data using trained neural network models to generate embedding vectors, allowing for immediate identification of similar events and providing real-time recommendations or fraud detection by storing and indexing these vectors for rapid access and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If batch-based embedding vector generation is used, then system complexity is reduced and ease of operation is improved, but real-time responsiveness deteriorates and loss of time increases
Solution Approach 1:
The patent segments the embedding vector generation process into batch processing (for stable vectors) and streaming processing (for real-time updates). The streaming platform receives continuous data streams and generates embedding vectors in real-time, while batch processing handles bulk updates. This segmentation allows the system to maintain operational simplicity through standardized interfaces while achieving real-time responsiveness for time-critical operations.
Solution Approach 2:
The system dynamically selects between batch and streaming processing modes based on operational requirements. For time-sensitive operations like fraud detection or real-time recommendations, the streaming mode is activated to generate vectors immediately. For non-time-critical bulk operations, batch processing is used. This dynamic adaptation resolves the contradiction by adjusting the processing mode to match the urgency of each operation.
2Productivity
If batch-based embedding vector generation is used, then device complexity is reduced, but productivity and real-time response capability deteriorate
Solution Approach 1:
The patent introduces a streaming platform as an intermediary component between data sources and the machine learning model. This intermediary handles the complex real-time data streaming, vector generation, and indexing operations, while presenting a simplified interface to the rest of the system. The intermediary manages the complexity internally through standardized components (data receivers, vector generators, index builders) while maintaining high productivity through continuous real-time processing.
3Speed
If real-time streaming processing is implemented, then real-time responsiveness is improved, but system complexity and device complexity increase
Solution Approach 1:
The system segments real-time processing into modular components: data receivers that ingest streams, embedding vector generators that convert data to vectors, and index builders that maintain searchable structures. Each component handles a specific task independently, reducing overall system complexity while achieving high-speed real-time processing. The segmentation allows parallel processing and independent optimization of each component.
Solution Approach 2:
The streaming platform is designed as a universal system that handles multiple types of data streams (fraud detection data, product recommendation data, etc.) through the same core components. The embedding vector generator and index builder serve multiple functions across different application scenarios, reducing the need for separate specialized systems and thereby controlling device complexity while maintaining real-time speed across diverse workloads.
4Measurement precision
If embedding vectors are updated frequently in real-time, then measurement precision and detection accuracy are improved, but loss of time for processing increases and productivity decreases
Solution Approach 1:
The system dynamically adjusts the update frequency and processing intensity based on data urgency and operation type. For high-precision requirements like fraud detection, real-time streaming updates are activated with higher processing frequency. For less critical operations, batch processing with lower frequency is used. This dynamic adjustment maintains measurement precision when needed while preserving overall system productivity through optimized resource allocation across different operation types.
Data Source
AI summary
Systems, methods, and computer program for generating and using embedding vectors associated with real-time data are provided. A streaming platform receives streaming data associated with events occurring in a network environment. At least one neural network generates embedding vectors from the streaming data associated with the events. The analytical models analyze the embedding vectors and are updated with the result from the analysis. Embedding vectors are also associated with one or more indexes. The streaming platform may receive a query with an embedding vector associated with data from another event that is occurring in real-time in the network. Based on the embedding vector in the query, the streaming platform may use the one or more indexes to provide, in real-time, similar embedding vectors, which are indicative of similar events in the network environment.