Query Relationship Data Structure for KPI Interdependency Visualization
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Solution Overview
Problem
Conventional systems fail to effectively reveal interdependencies and relationships between key performance indicators (KPIs) and other query results, limiting the optimal use of business data and insights for decision-making.
Innovation Solution
A system that generates query relationship data structures to identify and store relationships between queries, allowing for the creation of query relationship data structures that represent dependencies between query parts, facilitating the management and visualization of query relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple KPIs and query results are collected and displayed in dashboards, then the quantity and value of business insights increase, but the ability to recognize interdependencies and relationships between items deteriorates
Solution Approach 1:
The patent introduces an intermediary layer (relationship data structure and visualization component) that mediates between the raw KPIs/query results and the user. This intermediary automatically detects, stores, and visualizes relationships between queries, making interdependencies visible without requiring users to manually analyze the complex data set. The intermediary transforms implicit relationships into explicit visual connections.
Solution Approach 2:
The system implements feedback by automatically analyzing query relationships and providing visual feedback to users about interdependencies. The relationship detection mechanism continuously monitors query interactions and provides feedback through the user interface, showing how changes in one KPI may affect others, thereby enabling users to understand system behavior without overwhelming complexity.
2Adaptability or versatility
If KPIs are created over long periods by various users, then the versatility and coverage of business metrics improve, but the complexity of managing and understanding query relationships increases
Solution Approach 1:
The system employs self-service automation where the relationship detection and management functions operate autonomously without requiring user intervention. The system automatically detects query relationships, builds the relationship data structure, and maintains the visualization. This self-service approach handles the complexity internally while presenting a simplified interface to users, allowing versatile KPI creation without proportional increase in management complexity.
Solution Approach 2:
The patent segments the complex task of relationship management into distinct automated components: query analysis, relationship detection, data structure construction, and visualization. By segmenting these functions and automating them, the system can handle diverse KPIs from multiple users without proportionally increasing management complexity, as each segment operates independently and systematically.
3Ease of operation
If conventional systems display KPIs without relationship context, then the ease of viewing individual metrics improves, but the optimal use of business data deteriorates
Solution Approach 1:
The patent adds another dimension to KPI visualization by incorporating relationship information alongside the standard metric display. Instead of merely showing KPI values in isolation, the system adds a relational dimension that displays connections and dependencies between queries. This additional dimension enriches the information available to users without compromising the ease of viewing individual metrics, as the relationship context is integrated rather than replacing the original display.
Data Source
AI summary
A query relationship data structure (RELSTRUCT) generator configured to select a plurality of queries, each query structured for application against a database to yield a query result. The RELSTRUCT generator includes a query analyzer configured to identify query parts of individual queries, and determine for each query, a relation, if any, of an included query part to any query part of remaining queries of the plurality of queries. The RELSTRUCT generator also may create, for each query, a query relationship data structure in which the query is related to at least one other query of the plurality of queries, based on the determined relation of a query part of the query and a query part of the at least one other query of the plurality of queries.