Complex Event Processing Plan Adaptation
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Solution Overview
Problem
Complex event processing systems face challenges in efficiently adapting to changing data characteristics, often leading to missed important changes or substantial computational overhead due to inadequate adaptation mechanisms.
Innovation Solution
A method that continuously updates evaluation plans by identifying and re-verifying invariant conditions in the data stream, ensuring that a better evaluation plan is generated only when a violation of these conditions occurs, thereby avoiding false positives and minimizing unnecessary re-optimizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adaptation mechanisms are employed to modify the evaluation structure in response to data changes, then the system can adapt to changing data characteristics, but it may incur substantial computational overhead by over-adapting or miss important input changes
Solution Approach 1:
The system employs feedback mechanisms by continuously monitoring data characteristics and using this information to dynamically adjust the evaluation structure. The monitoring component tracks statistical properties of incoming data streams, and when significant deviations are detected, the system triggers re-optimization of the evaluation plan. This feedback loop enables the system to adapt to changing data characteristics while avoiding unnecessary adaptations, thus reducing computational overhead.
Solution Approach 2:
The evaluation structure is made dynamic rather than static. The system continuously monitors data characteristics and automatically re-optimizes the evaluation plan when changes are detected. This dynamic approach allows the system to adapt its structure in real-time to match changing data patterns, improving detection efficiency without requiring manual intervention or fixed structures that would be inefficient for varying data types.
2Reliability
If the evaluation structure is modified frequently to respond to data changes, then the system remains up-to-date with current data characteristics, but it may miss important input changes due to over-adaptation
Solution Approach 1:
The system uses feedback from continuous monitoring of data characteristics to determine when re-optimization is truly necessary. By comparing current statistical properties against baseline values and applying threshold-based deviation detection, the system avoids making unnecessary structural changes that would cause it to miss important patterns. Only when significant, meaningful changes are detected does the system modify its evaluation structure.
Solution Approach 2:
The system applies partial adaptation by selectively re-optimizing only specific portions of the evaluation structure when needed, rather than completely rebuilding the entire system. The monitoring component tracks specific statistical properties and triggers targeted re-optimization only for affected areas, maintaining responsiveness to data changes while avoiding the overhead of complete system reconfiguration.
3Device complexity
If a static evaluation structure is used, then computational overhead is minimized, but the system cannot adapt to frequent on-the-fly changes in data characteristics
Solution Approach 1:
The system implements periodic monitoring of data characteristics at predetermined intervals rather than continuous evaluation. The monitoring component checks statistical properties periodically, and only triggers re-optimization when significant deviations are detected during these periodic checks. This approach balances the need for adaptability with computational efficiency by avoiding constant re-evaluation while still capturing important data changes.
Solution Approach 2:
The system performs preliminary analysis of data characteristics during the monitoring phase before committing to full re-optimization. The monitoring component first detects potential changes in statistical properties, and only when predefined thresholds are exceeded does the system proceed with costly re-optimization operations. This preliminary detection mechanism reduces unnecessary computational overhead by filtering out minor fluctuations that don't warrant structural changes.
Data Source
AI summary
A method comprising receiving, as input, a data stream representing events; receiving a complex event pattern (CEP) specification representing an occurrence of a CEP in said data stream based on a set of conditions and set of event attributes; applying an algorithm to generate a current CEP plan configured to determine said occurrence of said CEP, wherein said current CEP plan comprises of a series of execution steps; optimize said current CEP plan by, iteratively: (i) executing said current CEP plan, (ii) identifying, with respect to each of said execution steps, one of said conditions whose verification causes said execution step to be included in said current CEP plan, and (iii) performing a re-verification of all of said identified conditions using said updated current values; and re-applying said algorithm when said re-verification fails with respect to at least one of said identified conditions, to generate an updated CEP plan.