Real-Time Event Segmentation for Capacity Change Detection
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Solution Overview
Problem
Repository organizations face challenges in managing resources due to delayed reporting of capacity changes, leading to potential insolvency risks from large outflows, necessitating near-real-time event data processing and analysis to identify destabilizing entity behavior.
Innovation Solution
A system for near-real-time event data processing and analysis that includes sorting, segmenting, and storing event instances by entity and time, using machine learning models to predict capacity changes, and generating graphical user interfaces for efficient resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If event data is processed in daily batches, then system complexity is reduced and storage requirements are manageable, but reporting delay increases and real-time response capability deteriorates
Solution Approach 1:
The patent segments event data into multiple categories (e.g., by entity type, event type, time window) and processes them in parallel using separate processing pipelines. This allows the system to handle large volumes of events in near-real-time while maintaining manageable complexity through modular architecture. Different data segments can be processed at different speeds and stored in appropriate formats based on their characteristics.
Solution Approach 2:
The system dynamically adjusts processing parameters based on event volume and complexity. Processing frequency, batch sizes, and retention policies are adjusted in real-time to balance between processing speed and system resource consumption. This enables near-real-time processing during low-complexity periods while maintaining stability during high-volume periods.
2Measurement precision
If all event data is stored for analysis, then measurement precision and analytical capability are improved, but storage requirements and data management complexity increase
Solution Approach 1:
The patent extracts and stores only the essential features and aggregate statistics from event data rather than raw data. Key metrics such as net capacity change, event frequency, and anomaly indicators are extracted and stored in optimized formats. This reduces storage requirements while preserving the information needed for accurate capacity change detection and analysis.
Solution Approach 2:
Different data retention and storage strategies are applied to different data segments based on their importance and characteristics. High-priority data (e.g., events triggering anomalies or capacity thresholds) are retained longer and stored with higher precision, while less critical data is aggregated or discarded after a certain period. This selective approach maintains measurement precision for critical analyses while reducing overall storage requirements.
3Reliability
If machine learning models are used to predict capacity changes, then detection accuracy and risk identification are improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary processing and feature extraction before feeding data to machine learning models. Pre-computed features, aggregated statistics, and simplified event representations are prepared in advance, reducing the computational burden during model inference. This allows accurate predictions while minimizing real-time computational resource consumption.
Solution Approach 2:
The patent dynamically adjusts model complexity and processing parameters based on risk level and computational resources. Simple rules-based filtering is applied to low-risk events, while complex machine learning models are activated only for high-risk or uncertain cases. This adaptive approach maintains high detection accuracy for critical risks while significantly reducing overall computational resource consumption.
Data Source
AI summary
In some implementations, the techniques may include receiving event data. In addition, the techniques may include generating segmented sets of instances by: identifying an event time for each received event instance; sorting the received event instances into a plurality of sets of instances; and segmenting each set of instances of the plurality of sets of instances into a plurality of subsets of instances based on the event time for each event instance of the set of instances. The techniques may include identifying a net capacity change for a subset of instances, the net capacity change being based at least in part on the capacity change of each event instance in the subset of instances. Moreover, the techniques may include comparing the net capacity change to a capacity threshold. Also, the techniques may include providing a notification in response to the respective net capacity change exceeding the capacity threshold.