Rule Engine Data Discovery via Abstract Syntax Tree
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Solution Overview
Problem
Conventional rule engines face inefficiencies in processing electronic transaction requests due to the need to retrieve large amounts of data, which can lead to delays and increased computational power usage, especially when differentiating between authorized and unauthorized transactions.
Innovation Solution
A mechanism that automatically discovers the types of data required by a computer-based rule engine for evaluating transaction requests, using an abstract syntax tree to determine irrelevant paths and only retrieve necessary data, thereby reducing unnecessary information retrieval and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a rule engine retrieves large amounts of data to evaluate transaction requests, then the accuracy of authorization determination is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the necessary data elements required for rule evaluation by analyzing the abstract syntax tree of the rule engine's evaluation logic. Instead of retrieving all available data, the system identifies and retrieves only those data points that are actually used in the evaluation paths, thereby reducing processing time while maintaining authorization determination accuracy.
Solution Approach 2:
The patent segments the rule engine's evaluation logic into an abstract syntax tree structure, which allows for systematic analysis of data requirements. By breaking down the evaluation process into discrete nodes and paths, the system can identify which specific data elements are needed for each evaluation path, avoiding unnecessary data retrieval.
2Reliability
If a rule engine retrieves large amounts of data to evaluate transaction requests, then the accuracy of authorization determination is improved, but the computational power consumption increases
Solution Approach 1:
The system extracts only the essential data elements required for rule evaluation by analyzing the abstract syntax tree. This extraction process identifies the minimal set of data needed for accurate authorization determination, reducing computational power consumption while maintaining reliability.
Solution Approach 2:
The patent applies partial action by retrieving only the necessary subset of data required for rule evaluation rather than all available data. The abstract syntax tree analysis determines the precise data requirements, allowing the system to perform partial data retrieval that suffices for accurate authorization determination without wasting computational resources.
3Reliability
If a rule engine requires large amounts of data for evaluation, then the comprehensiveness of transaction analysis is improved, but the service performance to end users deteriorates
Solution Approach 1:
The system extracts only the data elements that are actually required for comprehensive transaction analysis by analyzing the rule engine's abstract syntax tree. This targeted extraction maintains analysis comprehensiveness while reducing the overall data volume, thereby improving service performance to end users.
Solution Approach 2:
The patent performs preliminary analysis of the rule engine's evaluation logic by constructing an abstract syntax tree before actual transaction evaluation. This preliminary action identifies the specific data requirements in advance, allowing the system to retrieve only necessary data during transaction processing, thus maintaining comprehensiveness while improving service performance.
Data Source
AI summary
Methods and systems for automatically discovering data types required by a computer-based rule engine for evaluating a transaction request are presented. Multiple potential paths for evaluating the transaction request according to the rule engine are determined. An abstract syntax tree may be generated based on the rule engine to determine the multiple potential paths. Based on an initial set of data extracted from the transaction request, one or more potential paths that are determined to be irrelevant to evaluating the transaction request are identified. Types of data required to evaluate the transaction request according to the remaining potential paths are determined. Only data that corresponds to the determined types of data is retrieved to evaluate the transaction request.