Alpha Memory Gates for Production Rule System Memory Efficiency
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Solution Overview
Problem
Existing production rule systems face inefficiencies in handling unbounded streams of events, particularly in implementing rules with relative positional constraints, leading to excessive memory usage and inefficient pattern matching due to fixed window sizes and explicit testing of positional constraints.
Innovation Solution
The introduction of alpha memory gates within the production rule network, which control the passage of inputs from the alpha network to the beta network based on positional constraints, along with a scheduler for activating and deactivating these gates, and a definition-use style analysis to optimize memory usage and storage of facts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed window size is used for pattern matching in production rule systems, then the system structure is simple, but memory usage increases excessively when handling large event windows
Solution Approach 1:
The patent applies dynamics by making the testing window size variable rather than fixed. Each alpha memory gate maintains a dynamic testing window that automatically adjusts its size based on the specific rule's positional constraints. This allows the system to use small windows for rules with tight constraints and larger windows only when necessary, optimizing memory usage while maintaining the ability to handle diverse pattern matching requirements
Solution Approach 2:
The patent segments the single large testing window into multiple smaller windows, each associated with a specific alpha memory gate and rule. This segmentation allows each gate to maintain only the necessary portion of event history required by its specific rule, rather than all nodes maintaining a complete copy of the entire event window, significantly reducing overall memory consumption
2Measurement precision
If explicit testing of positional constraints is performed for all alpha matches, then measurement precision is improved, but productivity decreases due to inefficient pattern matching
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the testing window size for each alpha memory gate based on its rule's positional constraints during system initialization. This eliminates the need for runtime calculations and explicit testing of positional constraints during pattern matching, as the window boundaries are predetermined. The gate simply tests events within its pre-defined window, significantly improving pattern matching efficiency while maintaining precise positional constraint enforcement
Solution Approach 2:
The patent applies local quality by allowing each alpha memory gate to have its own customized testing window size tailored to its specific rule requirements. Rather than using a uniform testing approach across all gates, each gate operates with locally optimized parameters that match its rule's positional constraints, improving both precision and efficiency for each specific pattern matching task
3Reliability
If all alpha matches are tested against all events in the window, then completeness of pattern detection is improved, but loss of time increases due to unnecessary join tests
Solution Approach 1:
The patent applies partial action by having each alpha memory gate test only a partial subset of events within the complete window, specifically only those events that fall within its dynamically determined testing window. This partial testing approach maintains completeness for each gate's specific rule while avoiding unnecessary tests of events that cannot possibly satisfy the rule's positional constraints, significantly reducing the total number of join tests performed across the system
Data Source
AI summary
A method for improving memory efficiency of production rule systems is described. In one embodiment, the method includes identifying a rule associated with production rule systems, constructing a production rule network based at least in part on the rule, identifying a positional constraint associated with the rule, and implementing an alpha memory gate in the production rule network based at least in part on the positional constraint. In some cases, the alpha memory gate is one of a plurality of nodes of the production rule network.