Visualizing Multi-Dimensional Data Formula Dependencies
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Solution Overview
Problem
In planning applications, users face challenges in tracing dependencies between accounts and Key Performance Indicators (KPIs) due to hidden formulas, making it difficult to understand how proprietary KPIs are calculated, especially when users define them differently.
Innovation Solution
A data visualization framework that displays formula dependencies of multi-dimensional data using visual markers, allowing users to select dimension members and view the formulas used to derive them within the same visual representation, preventing accidental modification of underlying formulas by determining dependencies from metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If formulas are hidden from users in planning applications, then the visual representation remains clean and simple, but users cannot understand how accounts and KPIs are derived
Solution Approach 1:
The patent introduces visual markers as intermediaries that bridge the gap between hidden formulas and user understanding. These markers (arrows, icons, highlights) serve as visual mediators that convey formula dependency relationships without displaying the actual formulas, thus maintaining interface simplicity while providing transparency into how accounts and KPIs are derived.
Solution Approach 2:
The patent replaces the traditional text-based formula display mechanism with a visual marker system. Instead of showing mathematical formulas directly in the spreadsheet, the system uses visual cues like arrows and icons to indicate dependency relationships, substituting a mechanical text-display approach with a visual representation system that achieves the same informational goal more effectively.
2Loss of information
If users can trace formula dependencies, then transparency and understanding improve, but the visual representation becomes more complex
Solution Approach 1:
The patent applies local quality by adding visual markers only at specific locations where formula dependencies exist, rather than uniformly complicating the entire visual representation. Each cell or account that has a formula dependency receives appropriate visual cues (arrows, icons, highlights), while other areas remain unchanged, thus providing necessary transparency without globally increasing complexity.
Solution Approach 2:
The patent segments the formula dependency information into discrete visual markers that can be independently applied to different cells and accounts. This segmentation allows the system to provide transparency information in a modular way, where only the necessary portions of the visual representation are enhanced with dependency indicators, rather than overwhelming the user with a complete system-wide complexity increase.
3Adaptability or versatility
If proprietary KPIs are calculated with personalized formulas, then user customization improves, but other users cannot understand how these KPIs are calculated
Solution Approach 1:
The patent uses visual markers as intermediaries that convey the existence and structure of personalized formulas without revealing the actual custom logic. This allows users to maintain their proprietary KPI calculations with personalized formulas while other users can understand the dependency relationships through visual cues, bridging the gap between customization and comprehensibility.
Data Source
AI summary
A technology for displaying formula dependencies of multi-dimensional data in a visual representation is provided. In accordance with one aspect, a visual representation of a multi-dimensional data is provided based on metadata of a modeled data set. The metadata includes information of a data model comprising dimensions, hierarchies of dimension members, and formulas for deriving the dimension members. The formula dependencies are determined for members of a dimension based on the metadata. A user selection of a dimension member in the visual representation is received and the framework displays a formula dependency of the selected dimension member on the visual representation using one or more visual markers. The visual markers identify one or more corresponding dimension members from which the selected dimension member is derived and represent a formula used for deriving the selected dimension member.