Extensible Data Structures for Faster Complex Event Processing
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Solution Overview
Problem
Complex event processing (CEP) systems, such as those using the RETE algorithm, face challenges in efficiently evaluating rules due to high computational complexity, especially when dealing with large datasets and multiple concepts per individual, leading to increased processing time and reduced throughput.
Innovation Solution
The implementation of an extensible data structure that converts clinical content into dynamic fields and function calls, allowing for efficient rule evaluation by minimizing the need for multiple concepts and optimizing the retrieval and updating of singleton values, thereby reducing the computational complexity to O(1) for certain operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional RETE algorithm is used for rule evaluation in CEP systems, then rule-based reasoning capability is maintained, but processing time increases and throughput decreases when dealing with large datasets and multiple concepts per individual
Solution Approach 1:
The patent segments the rule evaluation process by introducing a new data structure that separates singleton values (O(1) access) from multiple concepts. This segmentation allows the system to efficiently handle different types of data access patterns, improving overall throughput while maintaining rule evaluation capabilities.
Solution Approach 2:
The patent implements dynamic field names that can be added at runtime without requiring system reconfiguration. This dynamic approach allows the rule-based system to adapt to changing requirements and data structures, improving productivity by eliminating the need for static schema definitions and reducing processing overhead.
2Loss of information
If multiple concepts per individual are supported in the data model, then clinical content representation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts singleton values from the multiple concepts data model and provides dedicated O(1) access methods for them. This extraction reduces computational complexity by separating the access patterns for singleton values versus multiple concepts, allowing efficient clinical content representation without the full overhead of multiple concept access for every operation.
Solution Approach 2:
The patent changes the parameter access complexity from O(n) to O(1) for singleton values by implementing a specialized data structure. This parameter change maintains accurate clinical content representation while reducing computational complexity for common access patterns.
3Stability of the object's composition
If static field names are used in the data structure, then system stability is maintained, but adaptability to new clinical content decreases
Solution Approach 1:
The patent implements dynamic field names that can be added at runtime without requiring system reconfiguration. This dynamic approach maintains system stability through structured data management while improving adaptability to new clinical content requirements, allowing the system to evolve without structural changes.
4Ease of operation
If O(n) complexity operations are used for retrieving and updating values, then data access flexibility is maintained, but processing efficiency decreases
Solution Approach 1:
The patent segments data access operations by providing specialized O(1) access methods for singleton values while maintaining flexible data structure. This segmentation improves processing efficiency for common access patterns while preserving data access flexibility through the underlying structured design.
Solution Approach 2:
The patent changes the time complexity parameter from O(n) to O(1) for singleton value access by implementing a specialized data structure with direct access paths. This parameter change maintains data access flexibility while dramatically improving processing efficiency.
Data Source
AI summary
A computer-implemented system, methods and computer storage media are provided for creating extensible data structures for rule based systems to improve complex event processing. An underlying individual data model is accessed and the content is translated to create singleton values to be utilized in complex event processing to improve the execution fo the complex event processing.