Contextual Rule Proximity Feedback in Business Rules Management Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Business Rules Management Systems (BRMS) lack effective contextual feedback on rule proximity, making it difficult for users to understand the aggregated effects of rule editing, especially when changes to one rule may impact related rules, and unintentional negations or alterations can occur without clear indication.
Innovation Solution
A method and system that provide contextual feedback based on co-occurrence history by creating a rule correlation table to identify and display rules that have been applied to common inputs with a selected rule, using a rules editor to visualize related rules and their shared inputs, with optional normalization to account for rules that always act on all inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users independently edit business rules in a BRMS, then ease of operation is improved, but reliability deteriorates due to lack of contextual feedback on rule relationships
Solution Approach 1:
The system provides feedback by displaying co-occurrence information showing which rules have been applied together with the selected rule in the past. This feedback mechanism allows users to understand rule relationships and potential aggregation effects without restricting independent editing, thus maintaining ease of operation while improving reliability through awareness of rule interactions.
Solution Approach 2:
The system performs preliminary analysis of rule co-occurrence history before the user completes rule editing. By pre-calculating and displaying rules that have been applied together with the selected rule, the system prepares contextual information in advance, allowing users to make informed decisions about rule modifications and understand potential aggregated effects before finalizing changes.
2Device complexity
If manual identification of related rules is used, then device complexity is reduced, but loss of information increases due to exclusion of rules without common variables
Solution Approach 1:
Instead of analyzing rules only through the dimension of shared variables, the system introduces a new dimension of execution history and co-occurrence patterns. By tracking which rules have been applied together across multiple inputs, the system identifies related rules even when they share no common variables, thus reducing information loss while maintaining relatively simple implementation through historical data analysis.
Data Source
AI summary
Embodiments of the present invention provide a method, system and computer program product for providing contextual feedback of rules proximity based on co-occurrence history in a business rules management system (BRMS). In an embodiment of the invention, a method for providing contextual feedback of rules proximity based on co-occurrence history can include selecting a rule for editing in a rules editor executing in memory of a computer, identifying different rules also having been applied to an input in common with the selected rule and displaying the identified different rules in the rules editor in connection with the selected rule.