Rule Input Attribute Visualization via Disparate Distribution Analysis
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Solution Overview
Problem
Current Business Rules Management Systems (BRMS) lack the ability to effectively visualize the relationship between rule inputs and their context, hiding important contextual attributes when analyzing a subset of rule inputs, which limits user understanding and decision-making.
Innovation Solution
A method and system for visualizing rule input attributes by computing global and specific attribute distributions, flagging attributes with sufficient disparity using a chi-square test, to highlight their correlation with selected rules in the editing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If only a subset of rule inputs is analyzed in the rule editing environment, then the analysis is simplified and more focused, but important contextual attributes are hidden and user understanding is limited
Solution Approach 1:
The patent introduces a new dimension of information presentation by displaying contextual attributes in a visual format (e.g., color-coded indicators, graphical representations) alongside the rule inputs. This allows the system to maintain the simplicity of analyzing a subset of inputs while simultaneously preserving and highlighting contextual information through an additional visual layer that does not clutter the primary analysis view.
2Loss of information
If all rule inputs are displayed in the rule editing environment, then complete contextual information is provided, but the interface becomes cluttered and harder to use
Solution Approach 1:
The patent applies local quality by selectively highlighting only those contextual attributes that are relevant to the specific rule being analyzed. Instead of uniformly displaying all attributes, the system uses visual indicators (such as color coding, icons, or emphasis) to mark attributes with significant correlations, allowing users to quickly identify important context without being overwhelmed by irrelevant information.
Solution Approach 2:
The patent implements partial action by displaying a curated subset of contextual attributes rather than all available attributes. The system calculates correlations and selectively presents only those attributes that have meaningful relationships with the rule inputs, providing sufficient contextual information for effective rule analysis while maintaining interface simplicity and usability.
3Loss of information
If contextual attributes are calculated and displayed for every rule input, then complete contextual understanding is achieved, but the computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the most relevant contextual attributes by calculating correlations between rule inputs and available attributes, then selecting only those with significant relationships for display. This extraction process filters out redundant or weakly related attributes, achieving comprehensive contextual understanding where needed while reducing computational complexity by avoiding unnecessary calculations for all attributes.
Solution Approach 2:
The patent changes the parameter of attribute selection from a static all-or-nothing approach to a dynamic correlation-based approach. By calculating correlation metrics and using these as selection criteria, the system adapts the displayed contextual attributes based on their actual relevance to each specific rule, optimizing both the completeness of contextual understanding and the computational efficiency of the process.
Data Source
AI summary
Embodiments of the present invention provide a method, system and computer program product for visualizing rule input attributes with a rule according to disparate rule attribute distributions. In an embodiment of the invention, a method for visualizing rule input attributes with a rule according to disparate rule attribute distributions has been provided. The method includes identifying in response to a selection of a rule for viewing in a rule viewer an input for the selected rule and determining an attribute for the input. However, attributes present in a guard for the selected rule can be excluded. The method also can include computing a global distribution of the attribute irrespective of the identified input and a specific distribution for the identified input. Thereafter, the attribute can be flagged as being correlated with the selected rule when it is determined that a sufficient disparity exists between the global distribution and the specific distribution.