Dynamic Data Load Process for Decision Service Rule Engine
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Solution Overview
Problem
Existing decision services face challenges in efficiently loading data for rule engines due to complex rule sets and flow paths, leading to high processing intensity and memory usage, especially when rapid execution is required, necessitating an improved data loading approach to reduce execution times and improve system operation.
Innovation Solution
A dynamic data load process determination method that assesses properties of rules and flow paths to dynamically choose between full and selective data load processes, generating a decision blueprint that optimally loads only necessary data based on thresholds and system resources, thereby reducing time, processing power, and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full data load process is used to load all rule data, then data completeness is ensured, but processing time and memory usage increase significantly
Solution Approach 1:
The patent applies partial action by loading only the necessary subset of rule data required for a specific decision request rather than loading all available rule data. The system determines which rules are relevant based on the request characteristics and loads only those, reducing data load time and memory usage while maintaining data completeness for the specific decision task at hand.
Solution Approach 2:
The patent implements dynamic data loading by adjusting the data load process based on real-time request characteristics. The system dynamically determines the scope of data to load based on factors such as request type, user profile, and decision context, transitioning from static full-load approaches to adaptive partial-load strategies that optimize performance.
2Reliability
If all rule data is loaded into the rule engine, then rule evaluation completeness is ensured, but system memory consumption increases
Solution Approach 1:
The system loads only the necessary subset of rule data required for a specific decision request rather than loading all available rule data. The determination of which rules to load is based on analyzing request characteristics and matching them against available rules, thereby reducing memory consumption while ensuring complete rule evaluation for the specific task.
Solution Approach 2:
The patent segments the rule data into manageable portions based on relevance to the current request. Instead of loading the entire rule set into memory, the system divides and loads only the pertinent rules that match the request criteria, reducing memory footprint while maintaining evaluation completeness for the segmented subset.
3Measurement precision
If complex rule sets with multiple flow paths are processed, then decision accuracy is improved, but processing intensity and execution time increase
Solution Approach 1:
The patent applies partial action by processing only the necessary subset of rules and flow paths relevant to the specific decision request. The system analyzes request characteristics to determine which rules need to be evaluated, avoiding unnecessary processing of irrelevant rules and thereby reducing execution time while maintaining decision accuracy for the specific task.
Solution Approach 2:
The system extracts and processes only the essential rules and flow paths required for the current decision request, separating them from the larger complex rule set. This extraction approach allows the system to maintain decision accuracy by focusing on relevant rules while reducing processing intensity by excluding irrelevant ones.
Data Source
AI summary
Techniques are disclosed relating to dynamically determining a data load process usable with a decision service for responding to requests to the decision service. The data load process may be selected from either a full data load process that loads data needed for all the rules in the decision service, or a selective data load process that loads data needed for a subset of the rules in the decision service according to embodiments. The decision service may determine the data load process to implement based on an evaluation of a decision blueprint in response to receiving the request, where the decision blueprint includes a set of rules interconnected by flow paths. Evaluation of properties of the rules and flow paths based on input data in the request may be used to determine the data load process to implement. Overall, this results in faster execution times in various circumstances.