Real-time Rule Feedback Service for Resource Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing rule validation systems are inefficient due to lengthy processes, requiring manual submission and evaluation of rulesets, which can take minutes or hours, and lack real-time feedback, hindering rapid optimization and adaptation.
Innovation Solution
A method and system that enable real-time feedback on rule performance, allowing automatic updating and resubmission of rules, utilizing a server and client computer interaction to apply rules to initial data sets, evaluate performance, and generate optimized rules through automatic rule generators and feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ruleset validation is performed using long-running programs on large databases, then measurement precision and reliability are improved, but loss of time increases significantly
Solution Approach 1:
The system pre-processes and stores historical transaction data in optimized formats before validation is needed. Data is pre-aggregated, pre-filtered, and stored in efficient data structures that enable rapid querying during validation, eliminating the need to process raw large databases in real-time
Solution Approach 2:
The validation process is divided into multiple independent stages: data retrieval, rule application, performance calculation, and result generation. Each stage can be processed separately and efficiently, allowing parallel execution and reducing overall validation time while maintaining comprehensive evaluation
2Ease of operation
If manual rule creation and validation is used, then ease of operation is maintained, but productivity decreases due to lengthy validation processes
Solution Approach 1:
The system automatically provides performance feedback on validated rules, including effectiveness metrics and suggestions for improvement. This feedback loop enables rapid iteration where rules can be automatically adjusted and re-validated without manual intervention, dramatically increasing optimization speed while maintaining ease of use
Solution Approach 2:
The system performs automated rule optimization by interpreting validation results and generating improved rule versions. The rule creation process becomes self-service, where the system autonomously refines rules based on performance data, reducing manual effort while maintaining operational simplicity
3Reliability
If comprehensive rule validation is performed on large historical databases, then reliability of rule predictions is improved, but device complexity increases
Solution Approach 1:
Complex data preprocessing and transformation operations are performed in advance, converting raw historical data into optimized validation-ready formats. This preliminary preparation reduces the computational complexity of the actual validation process while maintaining comprehensive evaluation capabilities
Solution Approach 2:
The system introduces intermediate data structures and processing layers that simplify the interaction between complex historical databases and validation algorithms. These intermediaries buffer and organize data in ways that reduce computational complexity while preserving the reliability of predictions
Data Source
AI summary
A method is disclosed. A server computer may receive, from a client computer, a first rule comprising one or more variables, the first rule configured to predict that an event is either a first classification or a second classification. The server computer applies the first rule to an initial data set of events to identify events that are associated with the first classification. The server computer evaluates performance of the first rule. The server computer provides data relating to the performance of the first rule to the client computer. The client computer automatically evaluates the performance of the first rule and generates a second rule in response to the data relating to the performance of the first rule. The server computer may then evaluate the second rule and provide performance data to the client computer. The server computer may use equi-height histograms to evaluate the performance of the rules.