Real-Time Event Ingestion and Segmentation for Capacity Change Detection
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Solution Overview
Problem
Repository organizations face challenges in maintaining sufficient resources to meet their obligations due to delayed reporting of capacity changes, leading to potential insolvency risks from large net negative capacity changes.
Innovation Solution
A system that receives event data in near-real time, sorts and segments it by entity and time, and uses machine learning models to identify and notify significant capacity changes, enabling proactive resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If event data is processed in daily batches, then processing resources can be reduced, but reporting delay increases and real-time capacity change detection is lost
Solution Approach 1:
The patent segments event data processing by organizing events into time-based windows (e.g., 5-minute, 15-minute, 1-hour windows) and processing them in chronological order. This allows the system to balance resource usage with timely detection of capacity changes by processing smaller portions of data at regular intervals rather than accumulating all-day batches.
2Measurement precision
If all event data is retained for analysis, then measurement precision is improved, but storage requirements increase
Solution Approach 1:
The patent extracts and retains only essential event attributes needed for capacity change detection (event type, amount, timestamp, entity identifier) while discarding redundant data. This extraction approach maintains the precision needed to detect significant capacity changes while minimizing storage requirements by keeping only the critical data elements.
Solution Approach 2:
The patent transforms raw event data into aggregated metrics such as net capacity change, event frequency, and threshold exceedance counts. By changing the parameter representation from individual event details to summarized statistical measures, the system achieves accurate capacity change detection with reduced data storage requirements.
3Speed
If real-time event processing is implemented, then capacity change detection speed is improved, but system complexity increases
Solution Approach 1:
The patent implements pre-computed rolling sums and net capacity change metrics that are updated incrementally as events arrive. This preliminary computation approach allows the system to detect capacity changes in real-time without complex batch processing logic, reducing system complexity while maintaining detection speed.
4Reliability
If duplicate event data is stored for redundancy, then reliability is improved, but storage efficiency decreases
Solution Approach 1:
The patent creates simplified copies of event data in aggregated forms (e.g., net capacity change by time window, event counts) rather than duplicating full event records. This copying strategy ensures data availability for reliability while maintaining storage efficiency by storing compact summaries instead of redundant full records.
Data Source
AI summary
In some implementations, the techniques may include receiving event data as a stream of event instances. Each received event instance is associated with an entity and a capacity change. In addition, the techniques may include identifying an event time for each received event instance. The techniques may include sorting the received event instances into sets of instances. Each set of instances can be associated with a respective entity. Moreover, the techniques may include segmenting each set of instances into subsets of instances based on the event time for each event instance of the set of instances. Each of the plurality of subsets of instances can correspond to a time period. Also, the techniques may include storing each segmented set of instances as stored event data. Further, the techniques may include performing one or more operations with respect to the stored event data.