Cached Variables for Context-Aware Event Processing
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Solution Overview
Problem
Conventional event management systems lack efficient methods to inject contextual memory, leading to inefficient processing, repetitive alarms, and suboptimal decision-making due to the absence of historical data, which is particularly problematic in high-velocity environments with stringent service level agreements.
Innovation Solution
Implementing cached variables that store and reference event-related data over a short period, allowing systems to make informed decisions based on recent history without significantly increasing processing times, thereby enhancing contextual awareness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional event management systems process events without historical data, then processing speed is maintained, but contextual awareness is lost leading to repetitive alarms and suboptimal decision-making
Solution Approach 1:
The system pre-processes and stores essential event characteristics in cached variables before full event processing occurs. This preliminary action captures contextual information (event type, source, timestamp) in a compact format that can be quickly retrieved, eliminating the need to re-analyze historical events during subsequent processing and reducing repetitive alarm generation
Solution Approach 2:
The invention extracts only the most relevant contextual features from historical events and stores them in cached variables, rather than retaining complete event histories. This extraction approach maintains contextual awareness for decision-making while minimizing storage requirements and retrieval overhead, thus preserving processing efficiency
2Loss of information
If cached variables store time-series data for all events, then complete contextual history is available, but memory consumption and processing overhead increase significantly
Solution Approach 1:
The system applies different data retention strategies to different event types and contexts. Cached variables store detailed time-series data only for events that require contextual analysis (such as repetitive alarms), while other events use simplified tracking. This local differentiation optimizes memory usage by storing comprehensive history only where necessary for decision-making
Solution Approach 2:
The invention uses lightweight cached variable structures with automatic expiration policies, where historical data is retained only for the minimum necessary duration to prevent repetitive alarms. Once the caching window expires, the compact data structures are automatically discarded, maintaining low memory consumption while preserving contextual information during the active monitoring period
3Reliability
If the system implements comprehensive event tracking with contextual memory, then decision-making quality improves, but processing time increases violating service level agreements
Solution Approach 1:
The event processing system is segmented into multiple stages: initial event intake, cached variable lookup, contextual analysis, and full processing. The cached variable evaluation occurs in a dedicated intermediate stage that can be quickly bypassed when no matching patterns exist, enabling comprehensive contextual checking without blocking the main event processing flow and thus meeting service level agreements
Solution Approach 2:
Cached variables serve as an intermediary layer between raw event streams and the full event processing pipeline. This intermediary provides pre-computed contextual information that accelerates decision-making by filtering and summarizing historical data before it reaches the main processing logic, improving reliability without adding significant processing time
Data Source
AI summary
A definition of a cached variable is associated with an event management orchestration. The definition identifies the cached variable as a counter of events that meet predefined criteria. A time-series data structure is associated with the cached variable. Timestamp data are stored in the time-series data structure based on received events that meet the predefined criteria. The stored time-series data are used to obtain a count value of the cached variable. The count value is used by the event management orchestration to determine a processing or a subsequent event to the received events.