Rule Engine Selection via Abstraction Model
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Solution Overview
Problem
The complexity of composing SQL queries in relational databases makes it difficult for average users, and the creation of rules for analysis routines requires detailed knowledge of the database schema, while rule engines differ in functionality and cost, necessitating a method to select appropriate engines for processing abstract rules based on functionality and cost.
Innovation Solution
A computer-implemented method that receives an abstract rule, determines the required functions, and selects a rule engine from a plurality of engines that include those functions, considering cost and performance metrics, allowing for the translation and processing of the rule in a format suitable for the selected engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users compose SQL queries directly in relational databases, then query functionality is complete, but user complexity and difficulty increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (abstraction model with logical fields and access methods) between the user and the physical database. Users compose queries using simple logical field references instead of complex SQL, and the system automatically translates these abstract queries into appropriate SQL statements executed against the physical database schema.
2Manufacturing precision
If users create rules with detailed database schema knowledge, then rule processing accuracy is high, but user expertise requirements increase
Solution Approach 1:
The abstraction model serves as an intermediary that shields users from physical database schema complexity. Users define rules using logical field names and abstract concepts without needing to understand underlying table structures, column names, or join relationships. The system handles the translation from abstract rule definitions to schema-specific implementations.
3Adaptability or versatility
If multiple rule engines are available with different functionalities, then system versatility is improved, but selection complexity and cost management become difficult
Solution Approach 1:
The system implements self-service automation where the abstraction model and query translation mechanism automatically select and configure the appropriate rule engine based on the query requirements. The system evaluates available engines, matches query characteristics to engine capabilities, and routes queries without requiring manual user intervention or complex selection processes.
4Ease of operation
If abstract rule sets are used to simplify user interaction, then ease of use is improved, but the need for translation and processing layers increases system complexity
Solution Approach 1:
The abstraction model serves multiple functions simultaneously: it provides a simplified user interface for query composition, defines a portable intermediate representation, manages translation to multiple target formats, and handles engine selection. This multi-functionality consolidates what could be separate complex systems into a unified framework.
Data Source
AI summary
Embodiments of the invention provide techniques for selecting rule engines for processing abstract rules based on functionality and cost. In general, an abstract rule is analyzed to determine which functions are required to process the rule. The abstract rule is assigned to a rule engine by evaluating metadata describing the functions and costs of the rule engines. The abstract rule is then translated to the format required by the selected rule engine.